phd offer biostatistics

Ph.D. Program in Biostatistics

The Biostatistics Doctoral Program offers students a program which emphasizes biostatistical modeling and inference in a wide variety of fields, including bioinformatics, the biological sciences and veterinary medicine, in addition to the more traditional emphasis on applications in medicine, epidemiology and public health. This feature takes advantage of unique UC Davis strengths, including the unparalleled diversity of the UCD campus in the life sciences. Biostatistics group faculty are researchers with widely varying backgrounds, espousing a wide variety of methodological approaches. PhD Program Planner (pdf) Degree requirements , approved by Graduate Council on August 11, 2020 2020-21 Biostatistics Graduate Handbook(pdf) PhD Program Coordinator:  Andi Carr (abcar [email protected] )

Degree Requirements, Ph.D.

The Graduate Program in Biostatistics offers the Ph. D. degree under Plan C. According to this plan, the Dissertation Committee consists of three faculty members who guide the student in his or her research and pass upon the merits of the dissertation. The complete degree requirements are listed below.  

• Undergrad Preparation
• An undergraduate major in mathematics or statistics is typical for Biostatistics graduate students, but is not required. However, because of the mathematical nature of some of the graduate coursework, students should be able to demonstrate good mathematical ability. Students should also demonstrate some exposure to courses in the life sciences (biological, environmental, medical and agricultural sciences). The minimal background for entrance into the Ph.D. program is: a bachelor's degree with a 3.0 overall grade-point average; one year of calculus; a course in linear algebra; facility with a programming language; and upper-division work in mathematics and/or statistics. Applicants without this minimal background will not be considered for admission in the Graduate Group. For more information about the program's application requirements , please see our Admissions Page . Prerequisites In addition, applicants are expected to have the equivalent of the following UC Davis courses: MAT 25 (Advanced Calculus) and MAT 125A (Real Analysis) and MAT 167 (Applied Linear Algebra). Deficiencies Deficiencies should be made up by the end of the first academic year following initial enrollment by earning a letter grade of “B” or better.
• Program of Study

The program of study will be adjusted to individual needs by the Biostatistics Graduate Adviser. A minimum of 58 units is required. Every full time graduate student must register for a minimum of 12 units per quarter. These 12 units can be made up of a combination of required coursework as described below, additional elective coursework if any, and 299’s. The Course Requirements (58 units) for the Ph.D. degree are as follows: Required Statistics Courses (39 units) STA 231 A, B, C (Mathematical Statistics I-III, 4 units each) STA 232 A, B, C (Applied Statistics, 4 units each) STA 243 (Computational Statistics, 4 units) BST 290 (Biostatistics Seminar, for six quarters, 1 unit) STA 390 (Methods of Teaching Statistics, 2 units) STA 260 (Statistical Practice and Data Analysis, 3 units) The following course may be used to substitute the STA 243 course requirement: STA 141A. If STA 141A is substituted in this way, the substituting course cannot be used to simultaneously satisfy any other requirement. Biostatistics Core Courses (12 units) We note that all of these courses carry a data analysis component and include a computing laboratory. Students will be exposed to projects involving advanced data analyses to address complex life sciences problems. BST 222: Survival Analysis (4 units) BST 223: Generalized Linear Models (4 units) BST 224: Analysis of Longitudinal Data (4 units) Electives (7 units) Biostatistical & Methodological Electives (4 units) : One course chosen from: BST 225 (Clinical Trials, 4 units) BST 226 (Statistical Methods for Bioinformatics, 4 units) BST 227 (Machine Learning in Computational Biology and Genomics) BST 252 (Advanced Topics in Biostatistics, 4 units) STA 250 (Advanced Data Analysis, 4 units) STA 251 (Advanced Statistical Theory, 4 units) STA 237 A, B (Time Series Analysis, 4 units per course) STA 235 A, B (Probability Theory, 4 units per course) Life Sciences Courses (3 units): One course at the upper division or the graduate level in Biology or Life sciences. This course should be approved by the graduate advisor. The intention is to provide a base of knowledge in molecular, cellular, organismal, and population biology, epidemiology or environmental sciences. The students are strongly encouraged to take more courses in Biology, Life Sciences or Environmental Sciences that are relevant to their research. Selection of such courses should be made in consultation with the thesis adviser. For a full list of Life Sciences courses, please consult the Biostatistics handbook. Summary A minimum of 58 units is required; 51 units of core and 7 of elective coursework. All students are expected to enroll in a minimum of 12 units per academic quarter, which may include a combination of required courses, electives, and research units ( BST 299).

Biostatistical Practicum

Students will complete a practicum in the form of an interdisciplinary applied data analysis project. They will work in collaboration with any UC Davis faculty researcher (not required to be a member of the Graduate Group) who conducts studies or experiments which generate data in the medical, biological, veterinary medical, epidemiological, agricultural or environmental sciences, and who will serve as a mentor. The practicum will last a minimum of six weeks sometime before completion of the dissertation and will involve the analysis of original data. The student will prepare or substantially contribute to a project report. The practicum may be conducted as part of employment as a Graduate Student Researcher or as part of the dissertation research.

A report based on an internship of a duration of at least six weeks at a facility, government health office, institute or company outside of UC Davis focusing on biological or medical research can also be used to satisfy this requirement. In this case the mentor will reside at the institution where the internship is carried out.

Qualifying Examinations and Dissertation Requirements  

Preliminary Written Examination

The Ph.D. Preliminary Written Examination will be given at fixed times, typically at the beginning of each Spring Quarter, with 2 months notification in advance before the written examination will be offered. The exam has two parts: a theory component based on STA 231A and STA 231B and a biostatistics component based on BST 222 and BST 223. The exam components may be taken at separate times. The duration of each part is about 3-4 hours. Students in the Ph.D. program must take the theory component in the Spring Quarter immediately after they complete the STA 231A and STA 231B course series and the biostatistics component after they complete BST 222 and BST 223 core course series. A well-prepared student will take this exam during the first year of the program. Otherwise, they are expected to take the exam during the second year of the program in the Spring Quarter. If a student does not attempt the examination at the first time they are eligible to take the exam, it will be recorded as a failure. Every Ph.D. student needs to pass the examination in a maximum of two attempts. In case of failure at the first attempt, the second attempt must take place at the next time the examination is offered (usually the retake is given in the Fall quarter of the third year), and if a student does not attempt the exam at that time, it will be counted as a second failure. Two failures to pass the examination will result in a recommendation to the Dean of Graduate Studies for disqualification of the student in the Ph.D. program.

The Ph.D. Preliminary Written Examination committees in charge may be different for each part of the exam. Pass or fail is determined separately by the exam committees for the statistical theory part and the biostatistics part of the exam. The chair of the GGB (Graduate Group in Biostatistics) will appoint an exam committee for two year terms that will be responsible for preparing, administering and grading the examination for the Biostatistics part of the exam. This committee will make the final decision on each student and forward its recommendation to the chair of the GGB.

Qualifying Exam

The Ph.D. Qualifying Examination is an oral exam. The exam will be attempted as soon as the Ph.D. Preliminary Written Examination has been passed and all required coursework for the Ph.D. degree in Biostatistics has been completed. In accordance with university rules, students are requested to take their qualifying examination, within two quarters of passing the Ph.D. Preliminary Written Examination, but no later than the end of the third year (9th quarter) to remain eligible for academic appointments such as Graduate Student Researcher (GSR) or Teaching Assistant (TA). The Master Graduate Adviser must submit the Application for the Qualifying Exam four weeks to Graduate Studies prior to the exam date; exams taken before receiving Office of Graduate Studies approval, may be deemed null and void. Students must be registered during the quarters in which they take any portion of their Qualifying Examination. To be eligible for the Qualifying Examination, the student must have:

A “B” average in all work done in graduate standing;

Satisfied all departmental or group requirements; and

Removed all academic deficiencies

The preparation for the exam will be done by working closely with the major professor through BST 299 (independent study) who is a regular member of the GGB. The exam committee consists of five faculty members, at least three but no more than four of which are members of the GGB. The Major Professor is not eligible to serve on the Ph.D. Qualifying Examination committee . The Ph.D. Qualifying Examination examines a student on the breadth and depth of knowledge expected from the coursework taken, and a special research topic in Biostatistics specified by the major professor in consultation with the exam committee. The primary purpose of the QE is to validate that the student is academically qualified to conceptualize a research topic, undertake scholarly research and successfully produce the dissertation required for a doctoral degree. A forty-five minute presentation on the specified research topic for the dissertation given by the student is followed by the qualifying examination session of 2-3 hours long, which covers questions on the special research topic as well as coursework in general. The examining committee will be appointed by Graduate Council at the recommendation of the Master Graduate Adviser who consults with the student prior to making the recommendation.

Graduate Studies guidelines for Ph.D. Qualifying Examinations apply. A student who passes the Ph.D. Qualifying Examination is eligible for Advancement to Candidacy for the Ph.D. degree. Title and abstract of the Ph.D. Qualifying Examination presentation will be distributed to all faculty and students of the Graduate Group in Biostatistics, who are invited to attend the 45-minute presentation portion prior to the qualifying examination session. The subsequent qualifying examination portion is a closed session between the student and the committee only. The student must file the appropriate paperwork with the Office of Graduate Studies and pay the candidacy fee to be promoted to Candidacy for the Ph.D. degree.

Qualifying Exam: Outcomes

• A committee, having reached a unanimous decision, shall inform the student of its decision as “Pass” (no conditions may be appended too this decision), “Not Pass” (the Chair’s report should specify whether the student is required to retake all or part of the exam, list any additional requirements, and state the exact timeline for completion of requirements to achieve a “Pass”). If a unanimous decision takes the form of “Not Pass” or “Fail”, the Chair of the QE committee must include in its report a specific statement, agreed to by all members of the committee, explaining its decision and must inform the student of its decision. Having received a “Not Pass” or “Fail”, the student may attempt the QE one additional time or fulfill the committee's requirements for "Pass." After a second exam, a vote of “Not Pass” is unacceptable; only “Pass” or “Fail” is recognized. Only one retake of the QE is allowed. A student who fails the QE on the second attempt will be recommended to the Dean of Graduate Studies for disqualification from the program.
• Dissertation

The doctoral dissertation is an essential part of the Ph.D. program. A topic will be selected by the student, under the advice and guidance of a Major Professor (thesis adviser) and a Dissertation Committee chaired by the Major Professor. Students are encouraged to begin some research activity as early as possible during the second year of their graduate studies. The dissertation must contain an original contribution of publishable quality to the knowledge of Biostatistics that may expand the theory or methodology of Biostatistics, or expand or modify Biostatistical methods to solve a critical problem in applied disciplines.

Acceptance of the dissertation by three designated members of the dissertation committee follows Graduate Studies guidelines (Plan C). The dissertation must be completed and submitted to the dissertation committee prior to taking the final examination described below.

Final Examination

• The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Biostatistics. The final examination will be conducted in two parts. The first part consists of a one hour presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members. Title and abstract of the oral presentation will be distributed to all faculty and students of GGB, who are invited to attend the presentation portion of the examination.
• Ph.D. in Biostatistics Sample Study Plans
• Normative Time to Degree
• The normative time to degree is five to six years.
• PELP, In Absentia & Filing Fee Status
• Information about PELP (Planned Educational Leave, In Absentia (reduced fees when researching out of state), and Filing Fee status can be found in the Graduate Student Guide: http://www.gradstudies.ucdavis.edu/publications
• Leaving the Program Prior to Completion of the Ph.D. Requirements
• Should a student leave the program prior to completing the requirements for the Ph.D., they may still be eligible to receive the Master’s if they have fulfilled all the requirements (see Master’s section). Students can use the Change of Degree Objective form available from the Registrar’s Office: http://registrar.ucdavis.edu/local_resources/forms/D065-graduate-major-degree-change.pdf

**For students that began the program prior and passed the Qualifying Exam prior to Fall 2018, please see the old degree requirements here **

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School of Public Health

A nationally top-ranked institution that offers a superior array of advanced degrees and is home to world-renowned research and training centers.

• Degrees & Programs
• Doctor of Philosophy (PhD)

Biostatistics PhD

• Major Coordinator Sally Olander 612-625-9185 [email protected]
• Director of Graduate Studies [email protected]

Extract and communicate meaning from complex biomedical data.

A biostatistician is an important part of many research teams. Working in close partnership with researchers across a wide array of scientific disciplines, a biostatistician designs studies and develops statistical tools to extract meaning from complex data.

With a biostatistics PhD, you’ll conduct original research, collaborate and consult with biomedical researchers, implement and disseminate results of this research, and teach and mentor others in this field.

• Program Brochure

Biostatistics PhD Profiles

It’s been a unique experience being able to apply my data science knowledge [at Netflix].

John Ssenkusu

Biostatistics, PhD '18, is passionate about the potential to harness data to improve health.

Carlos Serrano

Originally I was planning to go to medical school. In fact, I applied before coming here.

Advantages of the Program

• Personal Attention. The PhD student-to-faculty ratio is approximately 1.5:1, one of the lowest of any biostatistics program in the nation.
• Impact. The Division of Biostatistics & Health Data Science (BHDS) plays a leadership role in many national and international clinical trials, including the first vaccine trial for Ebola and the largest HIV/AIDS treatment trial in history .
• Breadth. Interdisciplinary research includes collaborations across the University of Minnesota with the Medical School, College of Veterinary Medicine, the Carlson School of Management, the Humphrey Institute for Public Affairs, the Supercomputing Institute, and Minnesota Population Center.
• Productivity. PhD students graduate with at least one peer-reviewed publication before graduation; many have three or more.
• Placement. Graduating students have gone on to faculty and postdoctoral positions at top research universities, as well as research leadership positions at government agencies and in private industry.

Connect with Us

• Request More Information
• Prospective Students
• Current Students

Contact 420 Delaware St. S.E. Minneapolis, MN 55455

612-626-3500 [email protected]

• Degrees Offered

PhD in Biostatistics

Description.

The doctoral program in Biostatistics trains future leaders, highly qualified as independent investigators and teachers, and who are well-trained practitioners of biostatistics. The program includes coursework in biostatistics, statistics, and one or more public health or biomedical fields. In addition, successful candidates are required to pass PhD applied and theory exams and write a dissertation that reports the results of new biostatistical research undertaken by the candidate.

Likely Careers

Clinical medicine, epidemiologic studies, biological laboratory and field research, genetics, environmental health, health services, ecology, fisheries and wildlife biology, agriculture, and forestry.

Applicants usually have a degree in mathematics, statistics, or a biological field. All applicants should have the equivalent of 30 or more quarter credits in mathematics and statistics, including linear algebra, probability theory, and approximately 2 years of calculus.

Concurrent Option:    PhD/MD

Application Deadline:   Dec 1 - Autumn Quarter Entry

Competencies

Upon satisfactory completion of the PhD in Biostatistics, graduates will be able to:

• Meet the  learning objectives of the MS program in Biostatistics ;
• Recommend and defend appropriate choices of methods to analyze independent outcome data; 
• Implement non-standard statistical methods accurately and efficiently; 
• Provide rigorous proofs characterizing the properties of standard statistical methods;
• Consult effectively with other scientists, addressing statistical issues in the design and analysis of public health or biomedical studies; and
• Design and carry out biostatistical research that will propose a new statistical method or will provide new information about the properties of existing methods.

Learning objectives for the PhD program in Biostatistics in the Generic Pathway:  Upon satisfactory completion of the PhD program in Biostatistics in the Generic Pathway, graduates will be able to:

• Recommend and defend appropriate choices of methods to analyze longitudinal, clustered and other non-independent outcome data; 
• Develop expertise in an area of biostatistical methodology; explain the strengths and weakness of different statistical methods in that area; and
• Explain both orally and in writing how advanced statistical methods work, assessing their strengths and limitations, and the place of particular methods in the larger statistical literature.

Biostatistics PhD

Many issues in the health, medical and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is a fundamental concern of our program.

This program offers training in the theory of statistics and biostatistics, computer implementation of analytic methods and opportunities to use this knowledge in areas of biological/medical research. The resources of Berkeley Public Health and the UC Berkeley Department of Statistics, together with those of other university departments, offer a broad set of opportunities to satisfy the needs of individual students. Furthermore, the involvement of UCSF faculty from the Department of Biostatistics and Epidemiology also enriches instructional and research activities.

A PhD degree in Biostatistics requires a program of courses selected from biostatistics, statistics, and at least one other subject area (such as environmental health, epidemiology, or genomics), an oral qualifying examination, and a dissertation. Courses cover traditional topics as well as recent advances in biostatistics and statistics. Those completing the PhD will have acquired a deep knowledge and understanding of the MA subject areas. Since graduates with doctorates often assume academic research and teaching careers, a high degree of mastery in research design, theory, methodology, and execution is expected, as well as the ability to communicate and present concepts in a clear, understandable manner.

The PhD degree program requires 4–6 semesters of coursework, the completion of the qualifying examination and dissertation (in total, a minimum of four semesters of registration is required). Since there are no formal course requirements for the PhD, a program of courses appropriate to a student’s background and interests may be developed with a graduate adviser.

Qualifications

A Master’s degree in Biostatistics or a related field is recommended but not required for admission to the PhD program. Strongly recommended prerequisite courses are calculus, linear algebra, and statistics. Applicants admitted without a Master’s degree may be required to go through the Biostatistics MA curriculum; students can concurrently earn that degree with no additional cost or time to degree. Normative time to degree is 5 years.

Students entering with a relevant master’s degree in biostatistics or statistics must have a faculty advisor who is a member of the Biostatistics Graduate Group committing funding and mentorship support.

GRE Exemption Criteria

GRE General Test scores are required for admission to the Biostatistics PhD program however applicants are exempted from the requirement if they meet all of the following criteria:

• Completed two semesters of calculus for a letter grade and earned a grade of “B” or higher.
• Completed one semester of linear algebra for a letter grade and earned a grade of “B” or higher.
• Completed one semester of statistics for a letter grade and earned a grade of “B” or higher.
• Cumulative undergraduate GPA of 3.0 or higher.
• Overall quantitative/math GPA of 3.0 or higher.
• For students with a Master’s in Biostatistics or a related field, graduate GPA of 3.0 or higher.
• For international students: TOEFL score of 100 or higher OR IELTS score of 7.0 or higher.

Berkeley Public Health also exempts applicants who already hold a doctoral level degree from the GRE requirement.You can find more information on the application instructions page . There is a program page in the Berkeley Graduate Application where you can indicate you meet the criteria for GRE exemption. Applicants who are exempted from the GRE are not at a disadvantage in the application review process.

Many doctoral graduates accept faculty positions in schools of public health, medicine, and statistics and/or math departments at colleges and universities, both in the United States and abroad. Some graduates take research positions, including with pharmaceutical companies, hospital research units, non-profits, and within the tech sector.

Funding and Fee Remission

Prospective students who are US citizens or permanent residents can find more information about applying for an application fee waiver for the Berkeley Graduate Application. Fees will be waived based on financial need or participation in selected programs described on the linked website. International applicants (non-US citizens or Permanent Residents) are not eligible for application fee waivers.

All PhD students are fully funded (including tuition and fees and a stipend or salary) with the exception of Non-Resident Supplemental Tuition (NRST) for the second year, if applicable. NRST is typically waived after the first year of study for PhD students when they advance to candidacy. Information on applying to GSI positions for biostatistics students can be found in the Biostatistics Division student handbook .

Tuition and fees change each academic year. To view the current tuition and fees, see the fee schedule on the Office of the Registrar website (in the Graduate: Academic section).

Please contact [email protected] if you have any questions about funding opportunities for the biostatistics programs.

Diversity, Equity and Inclusion

The Division of Biostatistics is committed to challenging systemic inequities in the areas of health, medical, and biological sciences, and to advancing the goals of diversity, equity, and inclusivity in Biostatistics and related fields.

Diversity, Equity and Inclusion in Biostatistics

Admissions Statistics

Emeritus faculty, faculty associated in biostatistics graduate group.

• Peter Bickel PhD Statistics
• David R. Brillinger PhD Statistics
• Perry de Valpine PhD Environmental Science, Policy, and Management
• Haiyan Huang PhD Statistics
• Michael J. Klass PhD Statistics
• Priya Moorjani PhD Molecular & Cell Biology
• Rasmus Nielsen PhD Integrative Biology and Statistics
• Elizabeth Purdom PhD Statistics
• Sophia Rabe-Hesketh PhD Education
• John Rice PhD Statistics
• Yun S. Song PhD Statistics; Electrical Engineering and Computer Sciences
• Bin Yu PhD Statistics

Biostatistics PhD

Many issues in the health, medical, and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is the fundamental concern of the Group in Biostatistics. The program offers training in theory of statistics and biostatistics, the computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological/medical research. The curriculum is taught principally by members of the Division of Biostatistics (School of Public Health) and the Department of Statistics (College of Letters & Science) and provides a wide range of ideas and approaches to the analysis of data.

Established in 1955, the Graduate Group in Biostatistics curriculum offers instruction in statistical theory and computing, as well as opportunities to rigorously apply this knowledge in biological and medical research. The degree programs offered (listed below) are appropriate for students who have either a strong mathematical and statistical background with a focus in the biomedical sciences, or degrees in the biological sciences with a focus in mathematics and statistics. (The MA degree can be obtained under Plan I or Plan II. The PhD dissertation is administered according to Plan B.)

Master of Arts (MA)

The Masters of Arts Degree in Biostatistics is completed in 4 semesters. Candidates for this degree are expected to earn 48 units with courses in biostatistics, statistics, public health, and biology. Students pursuing the MA degree in Biostatistics will be expected, upon completion of the program, to be well-versed in the following areas:

Understand the foundations of statistical inference, e.g., maximum likelihood estimation, regression.

Have grounding in theoretical framework and ability to apply existing estimators in following categories:

Computational statistics

Multivariate analysis

Categorical data analysis

Survival analysis

Longitudinal data analysis

Causal inference

Clinical trials

Statistical genomics

Statistical computing

Have fluency in statistical programming languages for both analysis using classic methods and implementation of novel methods.

Identify and apply sound and pertinent methods to address statistical inference questions in biological, public health, and medical research.

Effectively communicate research findings, orally and in writing.

Doctor of Philosophy (PhD)

All Biostatistics PhD students are required to hold a master's degree in Biostatistics or a related field. The PhD degree requires 4-6 semesters of course work in biostatistics, statistics, and at least one other subject area (e.g., biology, environmental health, epidemiology). There are no unit or course requirements for the PhD, so a program of courses appropriate to a student's background and interests may be developed. Courses cover traditional topics as well as recent advances in biostatistics and in statistics. Those completing the PhD will have acquired a deep knowledge and understanding of these subject areas. Since graduates with doctorates often assume academic careers in research and teaching, a high degree of mastery in research design, theory, methodology, and execution is expected as well as the ability to communicate and present research findings and area of expertise in a clear, understandable manner.

Many doctoral graduates accept faculty positions in schools of public health, medicine, and statistics and/or math departments at colleges and universities, both in the United States and abroad. Some graduates take research positions, including with pharmaceutical companies, hospital research units, non-profits, and within the tech sector.

Biostatistics Doctor of Philosophy (PhD) with Designated Emphasis (DE)

Students enrolled in the UC Berkeley Biostatistics doctoral (PhD) program are eligible to apply for interdisciplinary study in a Designated Emphasis (DE), which we refer to as the Associated Programs. At UC Berkeley, acquiring a DE is like earning a "minor" with a PhD degree. Applications for Designated Emphasis are reviewed on a rolling basis throughout the year. However, students must apply prior to taking the qualifying exam and are strongly encouraged to begin the application process early in the third semester of graduate study. To be accepted to a Designated Emphasis, you must be a PhD candidate in one of the Associated Programs (e.g., Biostatistics). The two DE programs offered in biostatistics are:

• Designated Emphasis in Computational and Genomic Biology (DE-CGB)
• Designated Emphasis in Computational Science and Data Science and Engineering (DE-CSDE)

The goal of the DE-CGB program is to train a new generation of computational biology researchers by enhancing and facilitating interactions between faculty, postdoctoral scholars and students in the Associated Programs through a flexible and integrated research and teaching environment which transcends traditional departmental boundaries. Upon successful completion of all requirements and dissertation, your transcript and diploma will read, "PhD in Biostatistics with a Designated Emphasis in Computational & Genomic Biology."

The DE in Computational Science and Engineering (CSE) promises to bring a new paradigm to interdisciplinary research and education. The team-oriented approach provides our students with a solid foundation in the different facets of genomic research and ensuing competitive edge for the most desirable jobs in academia and industry, which increasingly require interdisciplinary training by combining high-performance computing, mathematical modeling, scientific and engineering theory, and analysis of large scale databases of observations. Upon successful completion of all requirements and dissertation, your transcript and diploma will read, "PhD in Biostatistics with a Designated Emphasis in Computational Science and Engineering."

Contact Info

[email protected]

2121 Berkeley Way West, 5th floor

Berkeley, CA 94720

At a Glance

Department(s)

Biophysics Graduate Group

Admit Term(s)

Application Deadline

December 4, 2023

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

Biostatistics, PhD

Bloomberg school of public health, program overview.

The PhD program of the Johns Hopkins Department of Biostatistics provides training in biostatistical methodology and practice, grounded both in the theory of probability and statistics and in advanced data science. The program is unique in its broad emphasis spanning the foundations of statistical reasoning through data science and in providing rigorous training in both real analysis-based probability and statistics, equivalent to what is provided in most departments of mathematical statistics and in data science principles and practice.

The Department of Biostatistics PhD program prepares persons who have demonstrated excellence in mathematics, engineering, and the natural or social sciences to become research biostatisticians in academia, industry, or government. PhD graduates:

• Conduct and publish original research on the theory, methodology and practice of biostatistics and data science;
• Translate methodological advances into software and other tools by which to disseminate these into practice;
• Apply innovative theory and methods to the solution of public health problems;
• Serve as expert biostatisticians and data scientists on collaborative teams of investigators addressing key public health questions;
• Teach biostatistics and data science effectively to health professionals and scientists as well as to graduate students in biostatistics.

Program Requirements

Course location and modality is found on the BSPH website .

The core curriculum consists of the following components:

All PhD students are required to:

• Take at least 14 courses from the core course list above. The 14 courses should contain at least a one-year sequence of Methods (140.751-754), a one-year sequence of Theory (140.646-649 or 140.721-724 or 140.731-734), and a one-year sequence of Data Science (140.776/777/628/629 or 140.644/777/778/779). The 14 courses should contain no more than 4 introductory-level courses (140.646-649, 140.776/628/629).
• In addition to the 14 core courses, take 16 credits of advanced PhD elective courses.
• Take at least 18 credit units of formal coursework in courses outside the Department of Biostatistics. At least nine of these credits must be taken in the School of Public Health.
• Take Current Topics in Biostatistics Research (140.860), Academic & Research Ethics at BSPH (550.860), Responsible Conduct of Research (550.600), and Epidemiologic Inference in Public Health I (340.721).

Students who use the one-year sequence of introductory-level probability and statistical theory (140.646-649) to fulfill requirement (1) must also take the half-year sequence of advanced data science (140.711-712).

Students who use the one-year sequence of introductory-level data science (140.776/777/628/629) courses to fulfill requirement (1) must take at least two advanced-level probability (140.721-724) and two advanced-level statistical theory (140.731-734) courses.

Student Evaluations

The Department is committed to providing every opportunity for its students to successfully complete the academic program of their choice. To support students in progressing toward the degree and to further their educational experience, the Department offers a comprehensive written examination at the end of the first year and a practice oral exam, usually taken no later than six months after the end of the fourth term of the second year. See the Department of Biostatistics Student Handbook for more details about the first-year PhD comprehensive exam.

The main purpose of the practice examination is to evaluate students' ability to communicate statistical ideas and concepts. Students should prepare a paper/proposal related to their potential thesis topic. In addition, the University requires students to successfully complete a preliminary oral examination, typically taken at the beginning of the third year where a thesis proposal is presented and discussed, and an oral thesis defense, where the completed thesis is defended in a public forum.

Research and Teaching Assistantships

The Department of Biostatistics offers teaching and research assistantships to its PhD students on a competitive basis. All PhD applicants (US and international) are ranked based on their merits; top candidates are offered admission. All students who matriculate are provided with assistantships that last five years and include full tuition, health insurance, and a living stipend. Students in their second year and following are required to apprentice with faculty as research assistants for up to 19 hours per week and as teaching assistants for 5 hours per week during academic terms. Students find the teaching and research assistantships to be the most valuable part of their PhD experience. At AY22-23 rates, students who apprentice as described above and engage in 40 hour per week research assistantships during non-academic periods, allowing for one month of vacation, earn a stipend of $37,500 to $42,000, graduated by seniority, per year.  

The Department offers a weekly seminar program featuring recent work by outstanding statistical scientists from around the world. Attendance is required for all graduate students. One seminar per month may be designated to be part of the Biostatistics "Grand Rounds" series, which features statistical analyses addressing important public health questions.

In addition, first-year graduate students are required to complete the Current Topics in Biostatistics Research course ( 140.860 ), where faculty, postdoctoral fellows, and senior students from the Department present their research, with a focus on the public health and scientific questions driving the work, why the research makes a difference for the subject area and how to translate the research into practice. 

Recommended Curriculum

First-year phd students.

In addition to the core courses described in the program requirements, the following curriculum is recommended:

PH.260.600 Introduction to the Biomedical Sciences

"Cells to Society" modules, if applicable*

PH.550.860 Academic & Research Ethics at BSPH

PH.140.840 Special Studies and Research Biostatistics (credits as needed in order to get to at least 16 credits total)

PH.140.860 Current Topics in Biostatistics Research

Second Term

Fourth term, second-year phd students.

PH.340.721 Epidemiologic Inference in Public Health I (if not taken the first year)

PH.550.600 Living Science Ethics - Responsible Conduct of Research

PH.550.860 Academic & Research Ethics at BSPH (if not taken in a previous term)

PH.140.840 Special Studies and Research Biostatistics (Credits as needed in order to get to at least 16 credits total)

"Cell to Society" modules, if applicable*

Select one or both of the following:

PH.140.820 Thesis Research Biostatistics (credits as needed in order to get to at least 16 credits total)

Third-Year PhD Students

Coursework in scientific/statistical electives/special studies for a minimum of 16 credits per term

Fourth-Year PhD Students

* Students are required to take eight 552.xxx “Cells to Society” courses (552.601.81, 552.603.81, 552.607.81, 552.608.81, 552.609.81, 552.610.81, 552.611.81, 552.612.81) by the end of Year Two.

Doctoral Student Policies

Below covers policies regarding academic performance of doctoral students that are specific to the Department of Biostatistics. Students also must satisfy the academic standing requirements of the Johns Hopkins University and Bloomberg School of Public Health. 

For a full list of program policies, please visit the PhD in Biostatistics page where students can find a link to our PhD handbook.

Academic Standing and Exams

Doctoral students are expected to stay in good academic standing throughout their PhD studies.

Students are expected to maintain grades of B or higher in core classes. Any core class with a grade lower than a B will need to be retaken.

First year students maintaining B grades in core classes can sit for the first year Departmental exam. Students with any grades lower than a B in core classes must receive approval from the graduate committee to sit for the exams. In the event that students are denied, possible resolutions include postponing the exam for one year, switching to a master’s program or being required to leave the doctoral program based on a consideration by the faculty of overall academic performance.

In the event of a failure of the exam, students are allowed one retake. Student retakes typically occur in the following year, with exceptions occurring when mitigating circumstances are present, such as a leave of absence. In the event of a failure in the retake, the student will be asked to leave the doctoral program, typically with the option to join the master’s program. The exams, either a first take or retake, are only given once per year at the start of the summer break (typically in early June).

The grading of the Departmental exam is as follows. Passing scores are determined by exam writers after grading with examiners blinded from student names. Students who pass all sections of the exam pass the exam. Students failing one or more sections will be discussed by the faculty as a whole. This discussion will include exam and course performance. Possible resolutions include: declaring the student as passing the exam, declaring the student as having failed the exam, take-home remediation of sections of the exam or a full retake (only available if it is the student’s first attempt at the exam).

To maintain good academic standing, students must complete their school-wide preliminary oral exam by the end of their third academic year before the start of the first term of their fourth year (typically late August or early September depending on that year’s academic calendar).

Full funding for tuition and stipend is provided to PhD students for five years. Students are expected to finish their doctoral programs within this time. Students who do not finish within five years may continue in the program for up to two additional years. In these cases, a 75% departmental tuition scholarship is provided, but students are responsible for the remaining tuition, their own health insurance, and living expenses. Personal office space cannot be guaranteed for students beyond the five-year point. Common areas (Biostatistics Library, Genome Cafe) remain available for use by these students.

Program Changes from PhD to ScM or MHS

In the event that a doctoral student switches to the ScM or MHS programs, the following should be noted.

Funded doctoral students forfeit their funding in the event of a program switch.

Students have the option of switching to part-time status after switching programs. However, visa residency requirements for maintaining full-time student status typically prevent foreign students from being able to switch to part-time.

MHS and ScM students receive a 75% tuition reduction in their second year provided that they have taken 12 credits of courses outside of the Department (of which at least 6 credit hours must come from the School of Public Health courses) and have passed their Departmental exams. Doctoral students considering a program switch should appropriately plan their first year coursework to ensure eligibility for the tuition reduction in their second year. The 75% tuition remission is contingent on passing the first year exams at the master’s level. PhD students who take the doctoral exam and then elect or are asked to switch to a master’s program will be informed whether their performance on the doctoral exam constitutes a pass at the master’s level. Students who are deemed to have not passed at the master’s level will be asked to take the master’s Departmental exam in the subsequent year to fulfill the requirements of the master’s program and will not be eligible for the tuition reduction until the exam requirements have been met. Students will be allowed this one administration of the master’s exam in these circumstances.

Upon successful completion of the Doctor of Philosophy in Biostatistics, students will have mastered the following competencies:

• Apply quantitative methods to public health and scientific problems;
• Examine and apply foundational concepts of probability theory and statistical inference;
• Construct, fit and interpret different types of linear model (LM), generalized linear model (GLM), linear mixed model (LMM) and generalized linear mixed model (GLMM) in the context of scientific and public health applications, and conduct statistical inference in these models;
• Develop foundational insights for applying biostatistical theory and methodology to solve public health and scientific problems;
• Produce a complete data analysis to answer a targeted scientific or public health question.

According to the requirements of the Council on Education for Public Health (CEPH), all BSPH degree students must be grounded in foundational public health knowledge. Please view the list of specific CEPH requirements by degree type .

We're still accepting applications for fall 2024!

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Biostatistics Ph.D. Program

Requirements of the ph.d. program.

The Ph.D. degree requires successful completion of:

• Core courses
• Electives in Biostatistics and Statistics
• Epidemiology requirement
• Public health requirement (Foundations of Public Health Practice, online non-credit course). Students with an MPH from a CEPH-accredited institution are exempt.
• Electives in a cognate area
• Approaches to the Responsible Practice of Biostatistics (BIOS 810)
• Qualifying Examinations in Theory and Applications

Dissertation

• Presentation of proposal for research including an extensive literature review
• Writing of the dissertation
• Oral defense of the dissertation
• Mandatory Form for Scheduling a Proposal or Defense

After successful completion of coursework and the Qualifying Examinations, the student is advanced to candidacy and begins work on his/her dissertation.

Financial Support

All students admitted to one of our residential programs are considered for financial support. There are four types of financial support that we offer our students: Graduate Student Instructor (GSI), Graduate Student Research Assistant (GSRA), Training Grants and Fellowships.

Graduate Student Instructor (GSI)

Graduate Student Instructors are appointed at 50% effort, which involves working approximately twenty hours per week. This appointment includes full payment of tuition, health insurance coverage, registration fees and a monthly stipend. GSI’s are appointed to help with the instruction of Biostatistics courses offered to students from other Public Health Departments. The duties of a GSI can include preparing materials for and teaching labs, holding office hours, grading homework and exams, and tutoring.

Graduate Student Research Assistant (GSRA)

GSRA’s are appointed at 50% effort, which involves working approximately twenty hours per week on a research project. This appointment includes full payment of tuition, health insurance coverage, registration fees and a monthly stipend. GSRA’s generally work closely with a faculty member who is a principal or co-investigator on the research project. The duties of the GSRA can involve analysis of biomedical research data or statistical research. Currently, GSRAs are working on projects involving statistical methods development and application to bioinformatics, cancer, clinical trials, dentistry, diabetes, environmental health, epidemiology, genetics, health education, kidney disease, and survival analysis.

Training Grants

Students are also supported through involvement in training grants which provide support similar to the GSRA or GSI appointments. Currently, the Department has three training grants. Information can be obtained by clicking on the links below.

• Genomic Science Training Program
• Training Program in Cancer Research (PDF)

Scholarships, Fellowships and Awards

Awards for tuition assistance are available and are granted without a work obligation. These awards are generally made on the basis of academic merit, expected contribution to the field, and underrepresented backgrounds. Some examples of awards our students have received include:

• the Shapiro Award
• the Rackham Merit Fellowship for Historically Underrepresented Groups
• the Rackham Non-Traditional Fellowship
• the Rackham Regents Fellowship
• the School of Public Health Tuition Assistance Award.

Other award/scholarship opportunities exist for continuing students. They include:

• the Rackham One-Term Dissertation Grant
• the Barbour Scholarship
• the Pre-doctoral Fellowship
• the Susan Lipshutz Award
• the Rackham Travel Award .

The Rackham Sources of Aid page lists various sources of information on financial assistance available to students on campus.

Prospective students interested in applying for specific awards should contact Student Services at 734-615-9817 or [email protected].

Financial Aid

Many of our students are offered funding as GSIs, GSRAs or fellows. If a student does not receive such an offer, he or she may apply for financial aid through the University of Michigan’s Office of Financial Aid . This office requires applicants for any and all types of financial aid to complete the Free Application for Federal Student AID (FAFSA) provided by the American College Testing Center (ACTC) . A FAFSA will be sent to you directly if you indicate your interest in financial assistance on the admission application form. FAFSAs are available from most high school or college libraries and financial aid officers, as well as from the School of Public Health Office for Student Engagement and Practice. FAFSA is also available at www.fafsa.ed.gov .

Prerequisites

Minimum requirements.

• three semesters of calculus
• a course in matrix or linear algebra
• an introductory course in statistics or biostatistics

Students entering with a relevant master's degree in biostatistics or statistics are likely to have completed several of the courses required for the Ph.D. program. For this reason, we outline two programs of study: one for students with a relevant master's degree and one for students without a relevant master's degree. Each student should determine the details of the program of study after consultation with his/her faculty advisor.

Courses for a PhD Student with a Relevant Master's Degree

Typically, a student entering with a relevant master's degree will have had the following courses or their equivalents:

• BIOS 601 Probability and Distribution Theory
• BIOS 602 Biostatistical Inference
• BIOS 650 Applied Statistics I: Linear Regression
• BIOS 810 Approaches to the Responsible Practice of Biostatistics
• MATH 451 Advanced Calculus I
• Foundations of Public Health TBA, starting Fall 2018
• One or two electives in Biostatistics or Statistics

This accelerated program is not possible unless the student has already completed the first three courses listed above. MATH 451 can be taken in the first term of year 1, if necessary.

* Or another advanced probability course.

It is assumed that students entering with a relevant Master's degree will have taken equivalent courses that will enable them to be exempted from 3-6 hours of this requirement.

B. Electives (15 credit hours)

Electives may be selected from Biostatistics at the 600/800 level, from Statistics at the 500/600 level, or with approval of the Candidacy Committee, from courses taught in other departments. At least 12 of these hours should be in formal courses and 9 of the 12 hours should be at the 800 level in Biostatistics or 600 level in Statistics. A formal course is defined to be a graded course that is taught in a lecture format.

C. Epidemiology Requirement

All students in the School of Public Health are required to demonstrate competency in biostatistics and epidemiology. The epidemiology requirement may be satisfied in any one of the following ways:

• Completing Epidemiology 601 (Fall) or PH 512 (Fall & Winter).
• Taking and passing the Epidemiology exemption examination.
• Completing Epidemiology 516 and any necessary prerequisites to that course. (Option available to MS student but not to MPH students)
• Epidemiology 621 as an option for students funded by the Training Program in Cancer Research.

D1. Open Elective Requirement

Depending on the number of credit hours used to complete their epidemiology cognate, Ph.D. students will take 7-10 credit hours of "open elective" courses to be selected from an approved list. If the Epidemiology course taken is 515/516, the student needs 7 credits. If the Epidemiology course is 601, then 9 credits. If the Epidemiology course is 503, then 10 credits. All current Biostatistics students are granted access to the approved list, which is maintained on a Google Drive document. The approved list includes additional electives in biostatistics (600+ level) and statistics (500+ level), applied courses in public health or related topics, and courses in computational methods. Students who want to take University of Michigan courses that are not currently on the approved list are encouraged to request approval from the Curriculum Committee; please contact Nicole Fenech ([email protected]) and provide the name, number of the course and description.  (Courses far afield from biostatistics and public health will not be approved).

In Table 3, we present a possible sequence of courses and examinations for a student entering with a relevant Master's degree. Prior to registering for this sequence, the student should confirm with his/her faculty advisor that he/she has adequate prior course work. Also, the student should discuss with his/her faculty advisor the possibility of receiving exemptions from the Core courses listed above. BIOS 820 or 990 taken in the last term are individually-tailored reading courses in the area of biostatistics in which the student would like to do his or her literature review.

May Year 2:Qualifying Examinations

*Students who have taken BIOS 651 or equivalent prior to entry in the PhD program could finish Core courses (BIOS 801, 802, 653, 699) the first year and write the Qualifying Examinations May Year 1.

*This represents a minimal program of study for the PhD degree. The timing of electives and of cognates/open electives may be freely interchanged.

D2. Public Health Requirement 

All MS and Ph.D students (who do not have MPH degree) are required to take a 1-credit course on "Introduction to Public Health" (PUBHLTH 610) during the first semester of the program. This course is offered in the Fall of every year.

Courses for a PhD Student without a Relevant Master's Degree

This program does not assume any relevant course work for a student entering the PhD program. In practice, students with a relevant Master's are likely to have had some courses that are equivalent to requirements in this program. Therefore, prior to registering for courses the student should discuss with his/her advisor the specific courses that should be taken.

At least 15 credit hours of electives are required. They may be selected from Biostatistics at the 600/800 level, from Statistics at the 500/600 level, or with approval of the Candidacy Committee, from courses taught in other Departments. At least 12 of these hours should be in formal courses, 9 of the 12 hours should be at the 800 level in Biostatistics or 600 level in Statistics. A formal course is defined to be a graded course that is taught in a lecture format.

• Completing Epidemiology 601 (Fall) or PH 512  (Fall & Winter).
• Completing Epidemiology 516 and any necessary prerequisites to that course. (Option available to MS students, but not to MPH students)
• Epidemiology 621 as an option for students funded by the Training Program in Cancer Research 

D1a. Cognate Requirement (for entering class prior to Fall 2015)

PhD students must complete at least 9 hours of course work in a cognate area. This should consist of a coherent set of courses in an area (or in related areas) of application of biostatistics; the courses should be approved for graduate credit and may be from more than one department. Cognate courses should be primarily applied as opposed to mathematics/statistical in nature. For example, courses in areas such as mathematics, statistics, operational research, computer science, econometrics and psychometrics would most likely not qualify as cognate courses. Courses from other departments in Public Health or in areas such as genetics, biology, psychology, economics and many other similar areas will likely qualify as cognate courses. Courses in Bioinformatics that are biological or experimental in nature would typically count toward the cognate, whereas those that are more quantitative or technical would not. Courses taken to satisfy the epidemiology requirement count toward the cognate requirement. Faculty advisors can provide guidance and recommend approval of cognate courses. If questions arise on review by Student Services, the Curriculum Committee will make the final decision.

Waivers of cognate requirements .   It is possible to have cognate courses taken in a graduate program elsewhere recognized and to receive a partial or complete waiver. It should be noted, however, that if the previously taken courses were applied toward a degree, the required credit hours for the UM degree will not be reduced. A waiver of cognate requirements should be discussed with your advisor and must be approved by the Curriculum Committee and all requests must go through the Department's Student Services office.

D1b. Open Elective Requirement (for entering class Fall 2015 or later)

Depending on the number of credit hours used to complete their epidemiology cognate, Ph.D. students will take 7-10 credit hours of "open elective" courses to be selected from an approved list. If the Epidemiology course taken is 515/516, the student needs 7 credits. If the Epidemiology course is 601, then 9 credits. If the Epidemiology course is 503, then 10 credits. All current Biostatistics students are granted access to the approved list, which is maintained on a Google Drive document. The approved list includes additional electives in biostatistics (600+ level) and statistics (500+ level), Math 451, applied courses in public health or related topics, and courses in computational methods. Students who want to take University of Michigan courses that are not currently on the approved list are encouraged to request approval from the Curriculum Committee; please contact Nicole Fenech ([email protected]) and provide the name, number of the course and description.  (Courses far afield from biostatistics and public health will not be approved).

D2. Public Health Requirement (new requirement for MS and Ph.D students beginning Fall 2013 cohort)

In Table 4 we present a possible sequence of courses and examinations for students entering without a relevant Master's degree.

Spring/Summer Year 2: Qualifying Examinations

* This represents a minimal program of study for the PhD degree. The timing of electives and of cognates may be freely interchanged. These courses also allow a student to receive a Master's degree at the end of the second year. Three additional electives would be taken in year 3 for a total of 15 credit hours of electives. BIOS 820 or 990, which are individually-tailored reading courses in the area of biostatistics in which the student would like to do his or her literature review, are particularly recommended.

Qualifying Examinations and Advancement to Candidacy

Qualifying examinations.

As a rule, students must be admitted to the Biostatistics Ph.D. program before taking the Qualifying Examination. This rule may be waived in exceptional circumstances, subject to written consent of the Admission and Candidacy Committees. The Qualifying Examination is not individualized to the student. They are prepared and graded by the members of the Candidacy Committee.

The Qualifying Examination is offered once each year, in late May. Questions will be at the level of the final exams used in our required Biostatistics core courses (601, 602, 650, and 651). In addition, students are required to take and pass 699 before taking the Qualifying Examination.

Full-time Ph.D. students entering without a relevant master's degree are expected to take the Qualifying Exam within two years of entering the program, while students entering with a relevant master's degree are expected to take it within one year of entering the program. 

The requirements for part-time students are prorated, so that, for example, a half-time student entering with a relevant master's degree will be required to take the Qualifying Examinations within two years. A student retaking a Qualifying Examination must retake it the next time it is offered. If a student wishes to delay the examinations, he/she must submit a written request to the Candidacy Committee, justifying the delay.

A student who has failed the Qualifying Examination and wishes to continue in the Ph.D. program, may retake the examination. If a student fails the examination twice, then he/she will not be allowed to continue in the program.

Advancement to Candidacy

Advancing to candidacy requires passing the Qualifying Examinations and completing the required coursework. Once these requirements are met, the student should apply for candidacy by submitting the Candidacy Requirements form to the chair of the Candidacy Committee. The Candidacy Committee then makes the final decision regarding advancement.

Departmental Guidelines for Ph.D. Dissertation

Dissertation committee.

In accordance with Rackham Graduate School regulations, the dissertation committee must have at least four members, with at least two from within and at least one from outside the Department of Biostatistics. A member whose research interests are closely aligned with those of the student is the committee chair, unless this member is from outside the Department, in which case this member and a member from within the department are designated as co-chairs. The dissertation committee is selected by mutual agreement between the student and committee members and is nominated to the dean of the Graduate School by the chair of the department. The committee directs and reviews the student's doctoral research, conducts the oral defense of the dissertation, and decides whether or not the dissertation is approved.

• Ph.D. candidates should form their dissertation committee within 12 months of reaching candidacy; it is recommended that meetings with the committee members take place every six to 12 months.
• Candidates are expected to present their thesis proposal within 24 months of achieving candidacy. The proposal presents an opportunity to practice writing skills for the thesis and to present the materials to the members of the dissertation committee. The dissertation proposal does not require a complete outline of the dissertation or the very near completion of the work. Rather, the proposal should be presented along the lines of an NIH grant proposal and generally address questions of overall aims, carry out a comprehensive literature review in the research area, present a section on preliminary results, and provide a detailed plan for additional research. Presentation of the proposal offers a very useful milestone for the student to give a more formal summary of work and to get feedback and comments from the dissertation committee. The additional purpose of the thesis proposal is for the whole committee to review and approve the proposed direction and content of the proposed research.

Dissertation Content

The dissertation research must be a creative and significant original contribution to the field of biostatistics, involving the development and evaluation of biostatistical methodology that has application to important biomedical problems. The development of software and computational techniques for novel statistical methods is an important aspect of scholarly work. Various models for the structure of a dissertation have been used and are acceptable. In some cases, the thesis consists of three separate, often fairly loosely related, papers that are judged to be of publishable quality. A more traditional form of thesis would be one that provides an in-depth treatise on a topic, that may look at various facets of a problem and may not easily subdivide into a set number of separate publishable papers. For guidance, students may wish to review the collection of Ph.D. dissertations that have been written in the department and that are on display in the departmental library.

Dissertation Submission

• It is the responsibility of the student to see that the dissertation defense is advertised within the department at least three weeks in advance of the scheduled defense time. In addition, the student is responsible for providing a copy of the submitted dissertation to each member of the dissertation committee at least two weeks in advance of the date of the defense.
• The dissertation should be submitted by the student to the graduate office in the department at least two weeks prior to the defense. The thesis would then be available for review to any faculty member or student in the department prior to the defense. The Front Office will send a note to all faculty and students regarding the availability of the thesis and lend it out to anyone interested.

Conduct of Defense (Examination)

• The chair will call on the candidate for presentation of the dissertation, typically for a 50-minute presentation and will then call on committee members for questions. It is typical to call on the external member(s) of the committee first and then on others on the committee. Once the committee has completed a first round of questions, the chair will solicit any questions from the audience. Further questions from the committee will also be invited.
• The defense is to be public; therefore, examination of the candidate by committee members and others should take place with all who are interested present. There would still be time for an ‘in camera’ deliberation of the dissertation committee, and in exceptional circumstances where more information is needed, the committee may decide to meet again with the candidate after the public meeting is complete.

Rackham Procedures

Please make sure to read important Rackham guidelines and procedures .

Ph.D. Student Directory

Frequently asked questions

For more information about the admissions process, contact the Graduate Program Coordinator, Nicole Fenech.

E-mail : [email protected] Telephone : 734-615-9817

Mail Department of Biostatistics School of Public Health University of Michigan 1415 Washington Heights Ann Arbor, MI 48109-2029 Fax : 734-763-2215

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Doctor of Philosophy in Biostatistics .

The PhD in Biostatistics is jointly administered by the Department of Biostatistics at the School of Public Health and the Department of Mathematics & Statistics at the Graduate School of Arts & Sciences. The program is geared toward the graduate student who seeks a career as a professional, academic, or industrial biostatistician in biomedical or epidemiologic sciences. The program meets the needs of the health professional who wishes to continue with public health training and achieve a higher and more specialized degree and the statistician who wishes to specialize in statistical methods for biomedical or epidemiologic applications.

Students who complete the PhD program will gain knowledge in probability, statistical inference and hypothesis testing, the design and conduct of experimental and epidemiological studies, statistical computation, and data analysis. Research interests of the faculty include multivariate analysis, survival analysis, medical statistics, clinical trials methodology, statistical genetics, surveillance, robust statistics, longitudinal data analysis, time series, regression, estimation theory, and the design of experiments.

Program Directors:

• Kathryn Lunetta
• Laura White

Program of Study

Students entering with a bachelor's degree must complete a total of 64 credits. Students entering with a master's degree must complete at least 32 credits.

It is expected that students take courses from both the Biostatistics Department in the School of Public Health and the Mathematics & Statistics Department in the Graduate School of Arts & Sciences. Upon completion of coursework, each student must pass Qualifying Examinations .

The dissertation work must address a relevant question in statistical methodology and pose a new approach, extend an existing approach, or provide novel application of an existing method. When the dissertation is completed, the candidate defends his or her work before the dissertation committee. More information about the dissertation requirements is available in the Graduate School of Arts & Sciences Bulletin .

Program Requirements

Required Core Courses:

• CAS MA 575 Linear Models
• CAS/MET MA 581 Probability
• CAS/MET MA 582 Mathematical Statistics
• GRS MA 781 Estimation Theory
• GRS MA 782 Hypothesis Testing
• SPH BS 805 Intermediate Statistical Computing and Applied Regression Analysis or SPH BS 806 Multivariate Analysis for Biostatisticians
• SPH BS 857 Analysis of Correlated Data
• SPH BS 853 Generalized Linear Models with Applications
• SPH EP 770 Concepts and Methods in Epidemiology

For complete information on the PhD in Biostatistics program of study, please review the PhD Handbook 2023-2024 and the Graduate School of Arts & Sciences Bulletin .

Students admitted to the PhD program (post-master or post-bachelor) in Biostatistics at Boston University as a full-time student will receive  five years of financial-aid in the form of fellowship or assistantship support. The financial-aid package will consist of a stipend as well as a scholarship to cover tuition, mandatory fees, and individual basic health insurance. A student will typically be appointed as a Doctoral Research Assistant (RA).

Doctoral Research Assistantship

Eligibility (RA): All eligibility requirements for admission must be met. All qualified PhD applicants (international as well as domestic students) who intend to pursue their education as a full-time student are eligible to receive financial aid through Doctoral Research Assistantship .

How to apply (RA): Eligible individuals who are interested in being considered for Doctoral Research Assistantship must complete the standard online application for the PhD program in Biostatistics by December 1. No separate application for financial aid is required.

Click here to view the Research Assistantship Handbook.

Interdisciplinary Training Program for Biostatisticians

For more information on eligibility and requirement for the Training Grant program, click here .

Financial Aid Opportunities

For more information, please visit GRS’s financial aid page for fellowships and scholarships and BUSPH’s financing your education page for additional internal and external funding opportunities.

All requests or questions related to admissions and financial aid should be sent to [email protected] . Applicants should not directly contact faculty members regarding admissions or funding opportunities .

Alumni Careers

Our graduates are highly sought after and work in a wide variety of careers upon graduating. Below is information on our graduates' first jobs after graduation.

PhD in Biostatistics Admissions Requirements

Applications for the PhD in Biostatistics program must be completed on the Graduate School of Arts & Sciences website. The deadline for fall admission is December 1. We do not have spring admission.

Requirements for Admissions

• At least the equivalent of bachelor of arts degree; no specific undergraduate major is required
• One year of calculus, including multivariate calculus*
• One full semester of linear algebra*
• Unofficial transcripts from all colleges and universities attended
• 1-2 pages personal statement
• Three letters of recommendation
• Official report of TOEFL scores for applicants whose native language is not English

Note: GRE is not required for admission.

*Online courses can be used to fulfill prerequisite courses if they: 1) are taken from an accredited university or college, 2) are letter-graded, and 3) carry the same credits as in-person courses at the institution.

Graduate programs are jointly administered by the SPH Department of Biostatistics and the Department of Mathematics & Statistics at the Graduate School of Arts & Sciences. Program degrees are the Graduate School of Arts & Sciences.

Students can visit the fee waiver application to find out more information about their eligibility.

Attend a Webinar: Sept 14, Oct 18, Nov 8, Jan 17, & Feb 15

Webinar Information

Attend a live webinar via Zoom to speak with the Program Directors and learn more about the program. Fill out the  Biostatistics Virtual Session Form to RSVP. The 2023 - 2024 webinar dates are as follows:

• Thursday, September 14, 2023 @ 9am EDT
• Thursday, October 18, 2023 @ 12pm EDT
• Thursday, November 8, 2023 @ 9am EST
• Thursday, January 17, 2024 @ 12pm EST
• Wednesday, February 15, 2024 @ 9am EST

For more information about the program, please email [email protected] .

The Department of Biostatistics and Bioinformatics offers a Ph.D. degree in Biostatistics through the Duke University Graduate School. A distinguishing feature of the program is its integration within the world-class biomedical research enterprise at Duke University and the Duke School of Medicine. The goal of the program is to train students to become independent researchers who will advance the field of biostatistics, including statistical genetics and genomics. New methodological development with application in health-related areas is critical to this goal.  The program emphasizes development of methodology that incorporates the features of health-related problems, effective collaboration and communication with scientists, and ability to teach biostatistics. 

The Department currently has over sixty (60) faculty members who are individually affiliated with various research groups, centers and institutes across the School of Medicine, including the Duke Clinical Research Institute, the Duke Cancer Institute, the Durham VA Medical Center, the Center for HIV/Aids Vaccine Immunology, and the Duke Center for Aging. These affiliations provide a wide range of experiences and opportunities for graduate study.

Program Aims

• To train students to develop new statistical methodology, so they can develop into independent researchers and thought leaders in the field of biostatistics.  
• To train students in the practice of applying their statistical knowledge and skills across a broad spectrum of applications, so they can contribute to collaborative scientific endeavors in biomedical research. 

Class of 2022 Ph.D. in Biostatistics

Upcoming Dissertation Defenses

PhD in Biostatistics

Lead the future of biological data analysis with our doctorate in biostatistics..

Bayesian methods. Biomarker discovery. Machine learning. Experiment with these and other areas of research to examine causes and effects of disease as you harness your quantitative aptitude and sense of curiosity to pursue UMass Amherst’s PhD in biostatistics.

Designed to prepare you for research and teaching positions in academic institutions as well as leadership roles in health-related agencies, our program offers both major and minor areas of concentration. Core courses include graduate work in generalized linear models, Bayesian inference, survival analysis, as well as statistical theory. Every student is encouraged to select, in consultation with their academic advisor, the elective courses to take, based on their training, background, and interests.

Completion of a doctorate in biostatistics requires self-direction, independence, perseverance, and, naturally, quantitative aptitude. Evidence of these qualities plays an important role in our admissions decisions.

Related offerings

Students interested in our PhD in Biostatistics may also be interested in these other offerings.

• 4+1 Accelerated MS in Biostatistics
• MS in Biostatistics

Benefits list

Funding Your Education

Many PhD students are supported with research assistant (RA) positions funded by faculty grants. Doctoral students are also provided financial support via teaching assistant (TA) positions. All full-time PhD students are currently fully funded.

Partner With Research Centers and Institutes

You’ll find a wealth of opportunities in our campus research centers, including state-of-the-art research hubs like the  Institute for Applied Life Sciences , and international collaborations with researchers in the  Institute for Global Health , to support a wider breadth of research inquiries.

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Every program in the School of Public Health and Health Sciences is fully accredited by the  Council of Education for Public Health , a key benefit of attending UMass.

Featured class

The goal of this course is to introduce statistical modeling approaches that are widely used in medical and public health research, including novel approaches developed in the past few years.

Featured faculty

Nicholas reich.

Focus on forecasting, machine learning, time series, infectious disease modeling, cluster-randomized trials.

Leontine Alkema

Focus on Bayesian inference; statistical demography; global child, maternal, and reproductive health.

Raji Balasubramanian

Metabolomic studies, pediatric HIV studies, measurement error in self-reported outcomes, study design, high-dimensional data and analysis of biological networks

Ken Kleinman

Focus on cluster-randomized trials, missing data, statistical software, electronic medical records

In the spotlight

"Biostatistics as a field has helped me find that niche of working collaboratively with other people who are trying to make the world a better place, and you're putting your math and data science skills to work in a way that feels important."

Application information & deadlines

Prospective students must apply first through the SOPHAS site and then the UMass Graduate School Supplemental Application, which will be emailed to you upon completion of the SOPHAS application.

Priority Deadline

December 1, 2023.

Priority consideration will be given to applicants who submit the application by Dec. 1.

Application Deadline

February 1, 2024.

The application deadline is Feb. 1.

Biostatistics

Advancing public health, medicine, and biology through statistics

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Best Biostatistics Programs

Ranked in 2024, part of Best Health Schools

A biostatistics program in public health trains students

A biostatistics program in public health trains students to apply statistical principles and methods to problems in health sciences, medicine and biology. Public health biostatisticians may use these methods to explain or predict health outcomes in a pandemic, for example. The historical and scientific data that biostatistics provides can help steer decisions of public health officials. These are the best public health schools for biostatistics. Read the methodology »

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Doctor of Philosophy

The Doctor of Philosophy is an advanced degree, preparing you for careers such as independent investigators, collaborative biostatisticians, and educators.  A PhD in Biostatistics opens many opportunities for work in academia, government, and private industry.

Learn statistical theory, skills and techniques, and develop theory and applications of biostatistics. You will learn from internationally recognized faculty in UW’s Department of Biostatistics, and complete course work in biostatistics, statistics, and one or more public health or biomedical fields. As a PhD student, you will undertake research that advances the field of biostatistics and write a dissertation presenting your work. Earning a PhD in Biostatistics opens many opportunities for careers in academia, government, non-profit organizations, and private industry.

Course Pathway Options

The PhD program offers two pathways and both are typically completed in 4 to 5 years.

• Standard Pathway - The PhD in Biostatistics Standard Pathway includes coursework in biostatistics, statistics, and one or more public health or biomedical fields.
• Statistical Genetics Pathway -The PhD in Biostatistics Statistical Genetics Pathway provides training in the areas of statistical genetics, population genetics, and computational molecular biology.

Earn your degree from the top-ranked public biostatistics program

At UW, you will enter a strong community of cohorts. Capstone students move through classes together which builds friendships and provides a dynamic, interactive learning environment. Plus capstone students interact with MS Thesis and PhD students in both formal and informal settings.

“Biostatistics students may get involved in a breadth of research areas. Our faculty collaborators which include those at UW, as well as Fred Hutch, Seattle Children's, and other organizations, are working on amazing projects and are willing to involve us in their areas of expertise, so I feel like I have opportunities to work on practically any topic.” "

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Learn what it's like to be a student in the program.

Graduate Programs in Biostatistics

PhD in Biostatistics

The Biostatistics PhD program emphasizes both didactic and experiential learning. Program years 1 and 2 will include theoretical and applied classroom work in the core mathematical statistics and biostatistics courses, with additional electives in mathematics and/or computer science, and in the life sciences. The core courses incorporate classroom projects in theory and data analysis, and introduce literate programming and reproducible research practices. Year 2 requires a set of Biostatistics Rotations under the tutorship of a faculty mentor, using data drawn from collaborative projects in biomedical or public health sciences, with required oral and written presentations. The student will select, by the end of year 2, a primary advisor from among participating program faculty. Additional training in the biomedical area of application will occur in years 3 and 4. Throughout, the student will participate in presentations and discussions in a seminar series and journal club. The PhD thesis, completed in years 3-4 and potentially 5, will contain an original contribution of quality that would be acceptable for publication in the biostatistics literature, which extends the theory or methodology of biostatistics, or extends biostatistical methods to solve a critical problem in applied disciplines.

Degree requirements include 64 units of coursework from Mathematical Statistics (24 units), Biostatistics (29 units), Life Sciences (8 units) and Electives (3 units), and training in Human Subjects Research and Ethics. 

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Epidemiology and Biostatistics: Biostatistics, PhD

Related programs.

• Epidemiology and Biostatistics: Biostatistics, MS
• Epidemiology and Biostatistics: Epidemiology, PhD

The PhD program in biostatistics is designed to prepare students to be independent researchers in the development of statistical methodologies and in the appropriate and innovative application of these methodologies to biomedical research problems.  In the first five semesters of the program, students complete a series of courses in both theory and applied methodology, engage in individually mentored research experiences, explore statistical collaboration, and complete the qualifications examination. Within this period, students also identify a dissertation research problem and an advisor and present a research proposal as part of the candidacy examination.  Students typically defend their dissertations and graduate within five years of matriculation.

For more information: https://www.med.upenn.edu/ggeb/ggeb-courses.html

View the University’s Academic Rules for PhD Programs .

Required Courses 

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2023 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

Sample Plan of Study

Students should take BSTA 6990 Lab Rotation if mentor is not yet selected or BSTA 8990 Pre-Dissertation Research if mentor has been selected.

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• Biostatistics & Epidemiology
• PhD in Epidemiology and Biostatistics

PhD in Epidemiology & Biostatistics

The educational mission of the Epidemiology & Biostatistics PhD Program is to train students using an integrated approach that draws from the  population and quantitative health sciences. These include global, population, public, and community health, biostatistics, epidemiology, genomic epidemiology, and computational biology.  This training provides the foundation for trainees to play integral roles in successfully solving our most pressing health problems. 

To develop into the research leaders expected of our graduates, each student will take a common set of first year core courses that provides extensive exposure to each of the areas noted above. By the end of their first year students will choose a mentor and lab (wet or dry) in which to do their dissertation work. Research areas span all of the above and often combine these approaches with the expectation that cross-disciplinary studies will develop broader and more complete solutions to complex public health problems.

As part of their training all students will develop both a broad set of skills and competencies needed to assume positions of leadership in cutting edge research as well as in-depth expertise in specific areas of particular relevance to their research projects. These will be developed through a combination of demanding didactic coursework as well as individualized training within each research group led by their mentor.

A key component of our training program is to engage students in as many collaborative roles as practical. This will serve the important goal of giving each student a means to understand how to study the multiple determinants of health risk and outcomes, including biological and non-biological (environmental) influences, and the variation and disparity in outcomes.

Students will master the rigorous scientific and analytic methods necessary to be at the forefront of efforts to not only describe, but also effectively evaluate and improve health. Exposure to cutting edge research will be facilitated by our  department-wide seminar  that includes talks by world-leading experts both from off- and on-campus. As part of their training all students will participate in these seminars, including as speakers. This will help develop the necessary communication skills that is expected of successful researchers.

Graduates from accredited universities and colleges will be considered for admission to the department. All applicants must satisfy both CWRU and department requirements for graduate admission.  All incoming PhD students take a required common core curriculum , followed by additional coursework as determined by their mentoring or dissertation committees.

Learn more about PhD admission:

• Admission to the PhD in Epidemiology & Biostatistics PhD Program

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The Bioinformatics PhD Program is well established, with a long history of successful graduates in both academia and industry.  

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To apply for the Bioinformatics PhD Program, you must submit complete applications by December 1 for admission the following Fall term. Early applications are not allowed and will not be considered. 

Please visit the Rackham Graduate School web pages for additional information on applying. There you will also find information on how to respond to an offer of admission, plus tips and materials required for international applicants and incoming students.

If you are certain about pursuing a Bioinformatics PhD, then applications should be submitted directly to the Bioinformatics PhD Program ; there are more than 100 diverse affiliated faculty to choose from.

Applicants should be U.S. citizens or permanent residents. In addition, applicants with a background in quantitative sciences should consider applying directly. Separately, if you are transferring from another University of Michigan Program or have obtained an established University of Michigan mentor affiliated with the program, a direct application is most appropriate.

PIBS is an umbrella program that offers first-year PhD students flexibility in exploring opportunities in bioinformatics and thirteen other graduate programs. Through PIBS, students have the opportunity to rotate in, and potentially join the lab of a faculty mentor in another program; there are more than 500 diverse faculty to select from. PIBS students who list Bioinformatics as their primary choice must complete at least one rotation with a Bioinformatics-affiliated faculty member. After 10 months in PIBS, students officially join Bioinformatics (or one of the other programs). You can visit the PIBS website for more information.

Please note that reviewing admissions faculty for both PIBS and direct applications are the same. In addition, admitted applicants take the same Bioinformatics-specific courses and activities. See below for details on program diversity outreach, application materials, and funding.

Students who will have an MS in a relevant field (e.g. computer science, statistics, biostatistics, biology) from another university may request to have up to 6 credit-hours (two classes) waived. These classes may be used to help fulfill the core PhD requirements for biology (1 course), statistics (2 courses), and/or computing (1 course). To obtain approval, students need to send a detailed syllabus of the class(es) they took to the PhD directors along with their grade(s), which must be a B or better. The other PhD course requirements, including BIOINF-529 and two advanced bioinformatics courses, cannot be waived.

Most international Bioinformatics PhD applicants should apply through PIBS. However, some who are already embedded in a University of Michigan mentor lab affiliated with the program may be an appropriate fit for the direct Bioinformatics PhD program.

The TOEFL or IELTS exam is required unless Rackham Graduate School waiver requirements have been met. Criteria for English proficiency exemption can be found on the Rackham website . In addition, a list of required credentials from non-U.S. institutions for an application can be found here.

The Bioinformatics Graduate Program encourages applications from traditionally underrepresented minorities, students with disabilities, and those from disadvantaged backgrounds. There are numerous funding opportunities and resources on campus to contribute to students overall well-being while pursuing studies. Several resources available to students can be found on the Rackham Graduate School Diversity, Equity, and Inclusion website .

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All application materials should be submitted electronically when possible. Applicants must meet  Rackham's Minimum Requirements for Admission . The  online application form  can be found on the Rackham Admissions webpages. The application is available in early September through the deadline. 

• GPA, minimum 3.2/4.0 (exceptions may be made if deemed appropriate)
• Letters of recommendation (3 required): Please be aware that submitting only the Rackham Recommendation for Admission Form is insufficient; forms must be accompanied by a letter from the recommender. All letters are due by the application deadline. Without them, applications will not be considered complete or reviewed by the Program Admissions Committee.
• Statement of Purpose: The Statement of Purpose should be a concise, well-written statement about your academic and research background, your career goals, and how Michigan's graduate program will help you meet your career and educational objectives.
• Personal Statement: The Personal Statement should be a concise, well-written statement about how your personal background and life experiences, including social, cultural, familial, educational, or other opportunities or challenges, motivated your decision to pursue a graduate degree at the University of Michigan. This is not an Academic Statement of Purpose, but a discussion of the personal journey that has led to your decision to seek a graduate degree.
• Transcripts: Please submit unofficial transcripts electronically with your online application
• GRE scores are no longer included as part of admission
• Applicants whose native language is not English must demonstrate English proficiency via either the TOEFL or IELTS exam. The institution code is 1839. Other exams may not be substituted. Rackham Graduate School offers a full explanation of this requirement , including exemption criteria. Please contact Rackham directly ( [email protected] ) with questions.

Diversity is a key component of excellence, especially for solving the complex biomedical challenges that our field of computational medicine and bioinformatics faces. We believe that all people—regardless of background, race, religion, sexual/gender orientation, age or disability—deserve an equitable opportunity to pursue the education and career of their choice.

The Bioinformatics Graduate Program will provide tuition, healthcare coverage, and a stipend on a 12-month basis. This level of support will be maintained throughout a student's tenure in the Program, provided s/he remains in good academic standing and makes reasonable progress towards the degree as determined by the Graduate Directors, with faculty input. It is expected that the student will be supported directly by the mentor's laboratory, beginning in the second year. The expected time to degree is typically 5-6 years.

The U-M MS program is a terminal degree program. If you are interested in the Bioinformatics PhD Program, you must submit a new application. If you are a Bioinformatics MS student who is in good academic standing and has identified a Bioinformatics affiliated faculty mentor, you may apply for admission directly to the PhD Bioinformatics Program for the Winter term. Reviewing faculty take all application components into account and mentors are prepared to take both academic and financial responsibility for their trainees.

Eligibility: Only current or recently graduated University of Michigan Master’s students are eligible. Before applying, students must have completed more than half of all required courses, with at least six credits from the Bioinformatics Program.

Application deadline: October 1

The online application form can be found on the Rackham Admissions webpages. The application is available in early September through the deadline.

• Letters of recommendation: Please be aware that submitting only the Rackham Recommendation for Admission Form is insufficient; forms must be accompanied by a letter from the recommender. If you wish to include three letters from your original application, only one additional letter is needed. It must be from the DCMB faculty member who will serve as your primary mentor. The letter should state clearly that the mentor takes responsibility for your funding upon admission. Alternatively, you may wish to obtain three new letters of recommendation. The Admissions Committee strongly encourages you to include letters from those familiar with your research and coursework obtained while pursuing your Master’s degree. Of these, one must be from the faculty member who will serve as your primary mentor. The letter should state clearly that the mentor takes responsibility for your funding upon admission.
• Statement of Purpose: The Statement of Purpose should be a concise, well-written statement about your academic and research background, your career goals, and how the PhD Program will help you meet your career and educational objectives.
• Transcripts: Only a current, unofficial U-M transcript is necessary. You do not need to re-submit materials included with your Master’s application.
• TOEFL: If you submitted TOEFL scores when applying to the Master’s Program, additional test scores are not needed.

Bioinformatics consists of a mathematical and/or statistical analysis of a biomedical problem using computation. We define bioinformatics widely and include traditional bioinformatics areas such as for examples, systems biology, genomics, proteomics, plus statistical and evolutionary genetics, clinical informatics, and protein modeling.

As an interdisciplinary field, Bioinformatics attracts graduate students from mathematics, statistics, physics, computer science, biomedical engineering, chemistry, biochemistry and biology. Most incoming students have both a major in one and a minor in another discipline. In recent years students have entered with undergraduate training in bioinformatics or computational biology.

Each student obtains individual counseling by one of the two graduate program directors upon arrival and throughout their academic career. As Bioinformatics is still developing, new courses are added all the time. Current students are encouraged to contact the Program Directors about courses that may be relevant to their studies and are not listed on the website (esp. if they are new or infrequently offered).

In most cases, we recommend you apply to the PIBS program, as it provides flexibility in classes, funding, and a central admission for many biomedical programs. If you have no or very little biology background, please contact our Student Services Representative as to whether a direct application would be better. Current student who are considering transferring areas of study should also contact the Bioinformatics Graduate Office.

There is no need to apply both direct and through PIBS, as the same committee sees your applications.

For most students, thesis work includes computing, reading, and writing. A small group also participates in wet laboratory work. Please check both the research areas and student webpages for an overview of the varied subjects addressed in research and student theses.

Many of our graduate students obtain academic postdoctoral fellowships and go on to faculty positions. Quite a significant number of graduates go into non-academic professions such as small or large biotech companies. Some have founded their own business, and others apply their analytical skills in companies unrelated to bioinformatics. For a current list of graduate placement, please visit the alumni pages.

No. If you want to get a PhD, directly apply to the PhD Program.

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Driving Innovations in Biostatistics with Denise Scholtens, PhD

“I'm continually surprised by new data types. I think that we will see the emergence of a whole new kind of technology that we probably can't even envision five years from now…When I think about where the field has come over the past 20 years, it's just phenomenal.”  —  Denise Scholtens, PhD  

• Director, Northwestern University Data Analysis and Coordinating Center (NUDACC)  
• Chief of Biostatistics in the Department of Preventive Medicine  
• Professor of Preventive Medicine in the Division of Biostatistics and of Neurological Surgery  
• Member of Northwestern University Clinical and Translational Sciences Institute (NUCATS)  
• Member of the Robert H. Lurie Comprehensive Cancer Center  

Episode Notes 

Since arriving at Feinberg in 2004, Scholtens has played a central role in the dramatic expansion of biostatistics at the medical school. Now the Director of NUDACC, Scholtens brings her expertise and leadership to large-scale, multicenter studies that can lead to clinical and public health practice decision-making.    

• After discovering her love of statistics as a high school math teacher, Scholtens studied bioinformatics in a PhD program before arriving at Feinberg in 2004.  
• Feinberg’s commitment to biostatistics has grown substantially in recent decades. Scholtens was only one of five biostatisticians when she arrived. Now she is part of a division with almost 50 people.  
• She says being a good biostatistician requires curiosity about other people’s work, knowing what questions to ask and tenacity to understand subtitles of so much data.   
• At NUDACC, Scholtens and her colleagues specialize in large-scale, multicenter prospective studies and clinical trials that lead to clinical or public health practice decision-making. They operate at the executive level and oversee all aspects of the study design.  
• Currently, Scholtens is involved with the launch of a large study, along with The Ohio State University, that received a $14 million grant to look at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy.  
• Scholtens first started her work in data coordinating through the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study, which looked at 25,000 pregnant individuals. This led to a continued interest in fetal and maternal health.   
• When it comes to supportive working environments, Scholtens celebrates the culture at Feinberg, and especially her division in biostatistics, for being collaborative as well as genuinely supportive of each other’s projects. She attributes this to strong leadership which established a culture with these guiding principles.   

Additional Reading  

• Read more about the ASPIRIN trial and other projects taking place at NUDACC   
• Discover a study linking mothers’ obesity-related genes to babies’ birth weight, which Scholtens worked in through the HAPO study   
• Browse all of Scholtens recent publications 

Recorded on February 21, 2024.

Continuing Medical Education Credit

Physicians who listen to this podcast may claim continuing medical education credit after listening to an episode of this program..

Target Audience

Academic/Research, Multiple specialties

Learning Objectives

At the conclusion of this activity, participants will be able to:

• Identify the research interests and initiatives of Feinberg faculty.
• Discuss new updates in clinical and translational research.

Accreditation Statement

The Northwestern University Feinberg School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Credit Designation Statement

The Northwestern University Feinberg School of Medicine designates this Enduring Material for a maximum of 0.50  AMA PRA Category 1 Credit(s)™.  Physicians should claim only the credit commensurate with the extent of their participation in the activity.

American Board of Surgery Continuous Certification Program

Successful completion of this CME activity enables the learner to earn credit toward the CME requirement(s) of the American Board of Surgery’s Continuous Certification program. It is the CME activity provider's responsibility to submit learner completion information to ACCME for the purpose of granting ABS credit.

All the relevant financial relationships for these individuals have been mitigated.

Disclosure Statement

Denise Scholtens, PhD, has nothing to disclose.  Course director, Robert Rosa, MD, has nothing to disclose. Planning committee member, Erin Spain, has nothing to disclose.  FSM’s CME Leadership, Review Committee, and Staff have no relevant financial relationships with ineligible companies to disclose.

Read the Full Transcript

[00:00:00] Erin Spain, MS: This is Breakthroughs, a podcast from Northwestern University Feinberg School of Medicine. I'm Erin Spain, host of the show. Northwestern University Feinberg School of Medicine is home to a team of premier faculty and staff biostatisticians, who are the driving force of data analytic innovation and excellence here. Today, we are talking with Dr. Denise Scholtens, a leader in biostatistics at Northwestern, about the growing importance of the field, and how she leverages her skills to collaborate on several projects in Maternal and Fetal Health. She is the Director of the Northwestern University Data Analysis and Coordinating Center, NUDACC, and Chief of Biostatistics in the Department of Preventive Medicine, as well as Professor of Preventive Medicine and Neurological Surgery. Welcome to the show.  

[00:01:02] Denise Scholtens, PhD: Thank you so much.  

[00:01:02] Erin Spain, MS: So you have said in the past that you were drawn to this field of biostatistics because you're interested in both math and medicine, but not interested in becoming a clinician. Tell me about your path into the field and to Northwestern.  

[00:01:17] Denise Scholtens, PhD: You're right. I have always been interested in both math and medicine. I knew I did not want to be involved in clinical care. Originally, fresh out of college, I was a math major and I taught high school math for a couple of years. I really enjoyed that, loved the kids, loved the teaching parts of things. Interestingly enough, my department chair at the time assigned me to teach probability and statistics to high school seniors. I had never taken a statistics course before, so I was about a week ahead of them in our classes and found that I just really enjoyed the discipline. So as much as I loved teaching, I did decide to go ahead and invest in this particular new area that I had found and I really enjoyed. So I wanted to figure out how I could engage in the field of statistics. Decided to see, you know, exactly how studying statistics could be applied to medicine. At the time, Google was brand new. So I literally typed in the two words math and medicine to see what would come up. And the discipline of biostatistics is what Google generated. And so here I am, I applied to grad school and it's been a great fit for me.  

[00:02:23] Erin Spain, MS: Oh, that's fantastic. So you went on to get a PhD, and then you came to Northwestern in 2004. And so tell me a little bit about the field then and how it's changed so dramatically since.  

[00:02:36] Denise Scholtens, PhD: So yes, I started here at Northwestern in 2004, just a few months after I had defended my thesis. At the time there was really an emerging field of study called bioinformatics. So I wrote my thesis in the space of genomics data analysis with what at the time was a brand new technology, microarrays. This was the first way we could measure gene transcription at a high throughput level. So I did my thesis work in that space. I studied at an institution with a lot of strengths and very classical statistics. So things that we think of in biostatistics like clinical trial design, observational study analysis, things like that. So I had really classic biostatistics training and then complimented that with sort of these emerging methods with these high dimensional data types. So I came to Northwestern here and I sort of felt like I lived in two worlds. I had sort of classic biostat clinical trials, which were certainly, you know, happening here. And, that work was thriving here at Northwestern, but I had this kind of new skillset, and I just didn't quite know how to bring the two together. That was obviously a long time ago, 20 years ago. Now we think of personalized medicine and genomic indicators for treatment and, you know, there's a whole variety of omics data variations on the theme that are closely integrated with clinical and population level health research. So there's no longer any confusion for me about how those two things come together. You know, they're two disciplines that very nicely complement each other. But yeah, I think that does speak to how the field has changed, you know, these sort of classic biostatistics methods are really nicely blended with a lot of high dimensional data types. And it's been fun to be a part of that.  

[00:04:17] Erin Spain, MS: There were only a handful of folks like you at Northwestern at the time. Tell me about now and the demand for folks with your skill set.  

[00:04:26] Denise Scholtens, PhD: When I came to Northwestern, I was one of a very small handful of biostatistics faculty. There were five of us. We were not even called a division of biostatistics. We were just here as the Department of Preventive Medicine. And a lot of the work we did was really very tightly integrated with the epidemiologists here in our department and we still do a lot of that for sure. There was also some work going on with the Cancer Center here at Northwestern. But yeah, a pretty small group of us, who has sort of a selected set of collaborations. You know, I contrast that now to our current division of biostatistics where we are over 20s, pushing 25, depending on exactly how you want to count. Hoping to bring a couple of new faculty on board this calendar year. We have a staff of about 25 statistical analysts. And database managers and programmers. So you know, when I came there were five faculty members and I think two master's level staff. We are now pushing, you know, pushing 50 people in our division here so it's a really thriving group.  

[00:05:26] Erin Spain, MS: in your opinion, what makes a good biostatistician? Do you have to have a little bit of a tough skin to be in this field?  

Denise Scholtens, PhD: I do think it's a unique person who wants to be a biostatistician. There are a variety of traits that can lead to success in this space. First of all, I think it's helpful to be wildly curious about somebody else's work. To be an excellent collaborative biostatistician, you have to be able to learn the language of another discipline. So some other clinical specialty or public health application. Another trait that makes a biostatistician successful is to be able to ask the right questions about data that will be collected or already have been collected. So understanding the subtleties there, the study design components that lead to why we have the data that we have. You know, a lot of our data, you could think of it in a simple flat file, right? Like a Microsoft Excel file with rows and columns. That certainly happens a lot, but there are a lot of incredibly innovative data types out there: wearables technology, imaging data, all kinds of high dimensional data. So I think a tenacity to understand all of the subtleties of those data and to be able to ask the right questions. And then I think for a biostatistician at a medical school like ours, being able to blend those two things, so understanding what the data are and what you have to work with and what you're heading toward, but then also facilitating the translation of those analytic findings for the audience that really wants to understand them. So for the clinicians, for the patients, for participants and the population that the findings would apply to.   

Erin Spain, MS: It must feel good, though, in those situations where you are able to help uncover something to improve a study or a trial.  

[00:07:07] Denise Scholtens, PhD: It really does. This is a job that's easy to get out of bed for in the morning. There's a lot of really good things that happen here. It's exciting to know that the work we do could impact clinical practice, could impact public health practice. I think in any job, you know, you can sometimes get bogged down by the amount of work or the difficulty of the work or the back and forth with team members. There's just sort of all of the day to day grind, but to be able to take a step back and remember the actual people who are affected by our own little niche in this world. It's an incredibly helpful and motivating practice that I often keep to remember exactly why I'm doing what I'm doing and who I'm doing it for.  

[00:07:50] Erin Spain, MS: Well, and another important part of your work is that you are a leader. You are leading the center, NUDACC, that you mentioned, Northwestern University Data Analysis and Coordinating Center. Now, this has been open for about five years. Tell me about the center and why it's so crucial to the future of the field.  

[00:08:08] Denise Scholtens, PhD: We specialize at NUDACC in large scale, multicenter prospective studies. So these are the clinical trials or the observational studies that often, most conclusively, lead to clinical or public health practice decision making. We focus specifically on multicenter work. Because it requires a lot of central coordination and we've specifically built up our NUDACC capacity to handle these multi center investigations where we have a centralized database, we have centralized and streamlined data quality assurance pipelines. We can help with central team leadership and organization for large scale networks. So we have specifically focused on those areas. There's a whole lot of project management and regulatory expertise that we have to complement our data analytics strengths as well. I think my favorite part of participating in these studies is we get involved at the very beginning. We are involved in executive level planning of these studies. We oversee all components of study design. We are intimately involved in the development of the data capture systems. And in the QA of it. We do all of this work on the front end so that we get all of the fun at the end with the statistics and can analyze data that we know are scientifically sound, are well collected, and can lead to, you know, really helpful scientific conclusions.  

[00:09:33] Erin Spain, MS: Tell me about that synergy between the clinicians and the other investigators that you're working with on these projects.  

[00:09:41] Denise Scholtens, PhD: It is always exciting, often entertaining. Huge range of scientific opinion and expertise and points of view, all of which are very valid and very well informed. All of the discussion that could go into designing and launching a study, it's just phenomenally interesting and trying to navigate all of that and help bring teams to consensus in terms of what is scientifically most relevant, what's going to be most impactful, what is possible given the logistical strengths. Taking all of these well informed, valid, scientific points of view and being a part of the team that helps integrate them all toward a cohesive study design and a well executed study. That's a unique part of the challenge that we face here at NUDACC, but an incredibly rewarding one. It's also such an honor and a gift to be able to work with such a uniformly gifted set of individuals. Just the clinical researchers who devote themselves to these kinds of studies are incredibly generous, incredibly thoughtful and have such care for their patients and the individuals that they serve, that to be able to sit with them and think about the next steps for a great study is a really unique privilege.  

[00:10:51] Erin Spain, MS: How unique is a center like this at a medical school?  

[00:10:55] Denise Scholtens, PhD: It's fairly unique to have a center like this at a medical school. Most of the premier medical research institutions do have some level of data coordinating center capacity. We're certainly working toward trying to be one of the nation's best, absolutely, and build up our capacity for doing so. I'm actually currently a part of a group of data coordinating centers where it's sort of a grassroots effort right now to organize ourselves and come up with, you know, some unified statements around the gaps that we see in our work, the challenges that we face strategizing together to improve our own work and to potentially contribute to each other's work. I think maybe the early beginnings of a new professional organization for data coordinating centers. We have a meeting coming up of about, I think it's 12 to 15 different institutions, academic research institutions, specifically medical schools that have centers like ours to try to talk through our common pain points and also celebrate our common victories.  

[00:11:51] Erin Spain, MS: I want to shift gears a little bit to talk about some of your research collaborations, many of which focus on maternal and fetal health and pregnancy. You're now involved with a study with folks at the Ohio State University that received a 14 million grant looking at the effectiveness of aspirin in the prevention of hypertensive disorders in pregnancy. Tell me about this work.  

[00:12:14] Denise Scholtens, PhD: Yes, this is called the aspirin study. I suppose not a very creative name, but a very appropriate one. What we'll be doing in this study is looking at two different doses of aspirin for trying to prevent maternal hypertensive disorders of pregnancy in women who are considered at high risk for these disorders. This is a huge study. Our goal is to enroll 10,742 participants. This will take place at 11 different centers across the nation. And yes, we at NUDACC will serve as the data coordinating center here, and we are partnering with the Ohio State University who will house the clinical coordinating center. So this study is designed to look at two different doses to see which is more effective at preventing hypertensive disorders of pregnancy. So that would include gestational hypertension and preeclampsia. What's really unique about this study and the reason that it is so large is that it is specifically funded to look at what's called a heterogeneity of treatment effect. What that is is a difference in the effectiveness of aspirin in preventing maternal hypertensive disorders, according to different subgroups of women. We'll specifically have sufficient statistical power to test for differences in treatment effectiveness. And we have some high priority subgroups that we'll be looking at. One is a self-identified race. There's been a noted disparity in maternal hypertensive disorders, for individuals who self identify according to different races. And so we will be powered to see if aspirin has comparable effectiveness and hopefully even better effectiveness for the groups who really need it, to bring those rates closer to equity which is, you know, certainly something we would very strongly desire to see. We'll also be able to look at subgroups of women according to obesity, according to maternal age at pregnancy, according to the start time of aspirin when aspirin use is initiated during pregnancy. So that's why the trial is so huge. For a statistician, the statisticians out there who might be listening, this is powered on a statistical interaction term, which doesn't happen very often. So it's exciting that the trial is funded in that way.  

[00:14:27] Erin Spain, MS: Tell me a little bit more about this and how your specific skills are going to be utilized in this study.  

[00:14:32] Denise Scholtens, PhD: Well, there are three biostatistics faculty here at Northwestern involved in this. So we're definitely dividing and conquering. Right now, we're planning this study and starting to stand it up. So we're developing our statistical analysis plans. We're developing the database. We are developing our randomization modules. So this is the piece of the study where participants are randomized to which dose of aspirin they're going to receive. Because of all of the subgroups that we're planning to study, we need to make especially sure that the assignments of which dose of aspirin are balanced within and across all of those subgroups. So we're going to be using some adaptive randomization techniques to ensure that that balance is there. So there's some fun statistical and computer programming innovation that will be applied to accomplish those things. So right now, there are usually two phases of a study that are really busy for us. That's starting to study up and that's where we are. And so yes, it is very busy for us right now. And then at the end, you know, in five years or so, once recruitment is over, then we analyze all the data,  

[00:15:36] Erin Spain, MS: Are there any guidelines out there right now about the use of aspirin in pregnancy. What do you hope that this could accomplish?  

 Prescribing aspirin use for the prevention of hypertension during pregnancy is not uncommon at all. That is actually fairly routinely done, but that it's not outcomes based in terms of which dosage is most effective. So 81 milligrams versus 162 milligrams. That's what we will be evaluating. And my understanding is that clinicians prescribe whatever they think is better, and I'm sure those opinions are very well informed but there is very little outcome based evidence for this in this particular population that we'll be studying. So that would be the goal here, would be to hopefully very conclusively say, depending on the rates of the hypertensive disorders that we see in our study, which of the two doses of aspirin is more effective. Importantly, we will also be tracking any side effects of taking aspirin. And so that's also very much often a part of the evaluation of You know, taking a, taking a drug, right, is how safe is it? So we'll be tracking that very closely as well. Another unique part of this study is that we will be looking at factors that help explain aspirin adherence. So we are going to recommend that participants take their dose of aspirin daily. We don't necessarily expect that's always going to happen, so we are going to measure how much of their prescribed dose they are actually taking and then look at, you know, factors that contribute to that. So be they, you know, social determinants of health or a variety of other things that we'll investigate to try to understand aspirin adherence, and then also model the way in which that adherence could have affected outcomes.  

Erin Spain, MS: This is not the first study that you've worked on involving maternal and fetal health. Tell me about your interest in this particular area, this particular field, and some of the other work that you've done.  

[00:17:31] Denise Scholtens, PhD: So I actually first got my start in data coordinating work through the HAPO study. HAPO stands for Hyperglycemia Adverse Pregnancy Outcome. That study was started here at Northwestern before I arrived. Actually recruitment to the study occurred between 2000 and 2006. Northwestern served as the central coordinating center for that study. It was an international study of 25,000 pregnant individuals who were recruited and then outcomes were evaluated both in moms and newborns. When I was about mid career here, all the babies that were born as a part of HAPO were early teenagers. And so we conducted a follow up study on the HAPO cohort. So that's really when I got involved. It was my first introduction to being a part of a coordinating center. As I got into it, though, I saw the beauty of digging into all of these details for a huge study like this and then saw these incredible resources that were accumulated through the conduct of such a large study. So the data from the study itself is, was of course, a huge resource. But then also we have all of these different samples that sit in a biorepository, right? So like usually blood sample collection is a big part of a study like this. So all these really fun ancillary studies could spin off of the HAPO study. So we did some genomics work. We did some metabolomics work. We've integrated the two and what's called integrated omics. So, you know, my work in this space really started in the HAPO study. And I have tremendously enjoyed integrating these high dimensional data types that have come from these really rich data resources that have all, you know, resulted because of this huge multicenter longitudinal study. So I kind of accidentally fell into the space of maternal and fetal health, to be honest. But I just became phenomenally interested in it and it's been a great place.  

[00:19:24] Erin Spain, MS: Would you say that this is also a population that hasn't always been studied very much in biomedical science?  

[00:19:32] Denise Scholtens, PhD: I think that that is true, for sure. There are some unique vulnerabilities, right, for a pregnant individual and for the fetus, right, and in that situation. You know, the vast majority of what we do is really only pertaining to the pregnant participant but, you know, there are certainly fetal outcomes, newborn outcomes. And so, I think conducting research in this particular population is a unique opportunity and there are components of it that need to be treated with special care given sort of this unique phase of human development and this unique phase of life.  

[00:20:03] Erin Spain, MS: So, as data generation just really continues to explode, and technology is advancing so fast, faster than ever, where do you see this field evolving, the field of biostatistics, where do you see it going in the next five to ten years?  

[00:20:19] Denise Scholtens, PhD: That's a great question. I think all I can really tell you is that I'm continually surprised by new data types. I think that we will see an emergence of a whole new kind of technology that we probably can't even envision five years from now. And I think that the fun part about being a biostatistician is seeing what's happening and then trying to wrap your mind around the possibilities and the actual nature of the data that are collected. You know, I think back to 2004 and this whole high throughput space just felt so big. You know, we could look at gene transcription across the genome using one technology. And we could only look at one dimension of it. Right now it just seems so basic. When I think about where the field has come over the past 20 years, it's just phenomenal. I think we're seeing a similar emergence of the scale and the type of data in the imaging space and in the wearable space, with EHR data, just. You know, all these different technologies for capturing, capturing things that we just never even conceived of before. I do hope that we continue to emphasize making meaningful and translatable conclusions from these data. So actionable conclusions that can impact the way that we care for others around us. I do hope that remains a guiding principle in all that we do.  

[00:21:39] Erin Spain, MS: Why is Northwestern Medicine and Northwestern Feinberg School of Medicine such a supportive environment to pursue this type of work?  

[00:21:47] Denise Scholtens, PhD: That's a wonderful question and one, honestly, that faculty candidates often ask me. When we bring faculty candidates in to visit here at Northwestern, they immediately pick up on the fact that we are a collaborative group of individuals who are for each other. Who want to see each other succeed, who are happy to share the things that we know and support each other's work, and support each other's research, and help strategize around the things that we want to accomplish. There is a strong culture here, at least in my department and in my division that I've really loved that continues to persist around really genuinely collaborating and genuinely sharing lessons learned and genuinely supporting each other as we move toward common goals. We've had some really strong, generous leadership who has helped us to get there and has helped create a culture where those are the guiding principles. In my leadership role is certainly something that I strive to maintain. Really hope that's true. I'm sure I don't do it perfectly but that's absolutely something I want to see accomplished here in the division and in NUDACC for sure.  

[00:22:50] Erin Spain, MS: Well, thank you so much for coming on the show and telling us about your path here to Northwestern and all of the exciting work that we can look forward to in the coming years.  

[00:22:59] Denise Scholtens, PhD: Thank you so much for having me. I've really enjoyed this.  

[00:23:01] Erin Spain, MS: You can listen to shows from the Northwestern Medicine Podcast Network to hear more about the latest developments in medical research, health care, and medical education. Leaders from across specialties speak to topics ranging from basic science to global health to simulation education. Learn more at feinberg. northwestern.edu/podcasts.  

Applying to Biostatistics Ph.D. Programs

April 15, 2024

2024   ·   biostatistics   admission   phd  

My application cycle for Ph.D. programs in Biostatistics is finished and I am thrilled to join Brown’s Biostatistics department in the fall!

When I was preparing my applications, I profited from other folks sharing their experiences, especially Kat Hoffman and Simon Couch . Being a first-generation college student, a community college grad, and an international student– I understand how valuable this advice and I want to pay it forward to the next generation of aspiring Biostatisticians.

If you are getting ready to apply, I hope my experiences can help you out. Please don’t hesitate to reach out to me at [email protected] . If you are a non-traditional and/or underrepresented applicant, I would be happy to glance over your work (availability permitting). In your email, briefly describe why you are seeking what specific advice.

A Preliminary Disclaimer:

Please note that this post represents my opinions alone. My situation and circumstances may be quite different from you. In that sense, please take all opinions with a grain of salt. When stating/ recalling facts, I will do so to the best of my availability. Please reach out to me if you find any factual inaccuracies.

My Background

I am originally from Munich, Germany and I graduated from a large public state school with degrees in Economics, Business Analytics, and a minor in Mathematics. Before that, I completed an Associate’s degree in Business Administration at a community college.

After graduation, I briefly worked at an economic research consultancy focusing on energy economics. Coming out of undergrad, I was dead set on pursuing a Ph.D. in Economics, so when I was offered a pre-doc position at the Energy & Environment Lab at the University of Chicago , I jumped on it. I worked at the intersection of causal inference and machine learning there, although my work was not very technical (i.e. I was applying existing methods to new data sets rather than developing new statistical methods). Throughout this time, I became more and more fascinated with Statistics.

After 8 months, I decided to leave the E&E Lab and start my studies as a non-degree-seeking graduate student at the University of Chicago. The rationale was that I needed some more rigorous coursework (e.g., analysis) under my belt to be a competitive candidate for Ph.D. programs in (Bio-)Statistics.

If you want a more comprehensive overview, check out my CV here (Note that this is my current CV, not the one I submitted. Keep scrolling if you are interested in the CV I submitted).

My Interests and Goals

I am interested at the intersection of causal inference and statistical network analysis. In particular, I care about causal inference in high-dimensional networks, non-parametric and assumption-lean methodology, and dynamic treatment regimes. Ultimately, I would like to apply my work at the interface of public health and climate, helping policymakers make more informed decisions in response to environmental disasters and global warming.

With respect to my post-Ph.D. goals, I oscillate around the following numbers:

• A career in academia: 55%
• A research career in a public/ think tank role: 43%
• A career in private industry: 2%

When it came down to sending out my applications, I ended up applying exclusively to Biostatistics Ph.D. programs (with the exception of one program). Here is why:

• Biostatistics allows me to be a statistician while maintaining my applied interest in public health and environment
• Biostatistics programs tend to be much more causal inference focused than traditional Statistics Ph.D.’s
• Biostatistics programs are highly interdisciplinary, something I really value

My Weaknesses

• Undergraduate background : I majored in neither Statistics nor Math in undergrad. Although my coursework was certainly not completely unrelated to (Bio-)Statistics, I know that competing with math and stat majors from Ivy+ universities was going to be challenging. On that note, my undergraduate institution is not particularly renowned in Statistics.
• Recommendations : This is a mixed bag but I actually didn’t end up with a single UChicago recommender. Only one of my recommenders (who was my professor for my graduate ML course senior year) was a Statistician (+1 Economist, +1 Operations Researcher). (Not so) fun fact: I was going to get a recommendation from a UChicago professor but I did so poorly on the midterm that we decided a letter from him wouldn’t be wise lol (So keep your head up; you’re allowed to have accidents). Another thing that was less than ideal for me is that I did not get a letter from the UChicago lab I worked at. Lab policy dictates that you have to stay a certain amount of time to be eligible for a letter and I did not do that–that probably wasn’t great.
• Graduate coursework : I was enrolled in a full-time non-degree-seeking program taking graduate coursework, but that is not the same as an MS in Statistics.

My Strengths

• My Publication : I have a single-author peer-reviewed publication from undergrad. Though the paper doesn’t propose any new statistical methodology, it does focus on the application of some interesting methods in novel ways and applies them to data. I think that paper really helped my case. Moreover, the professor who supervised this thesis (an economist) wrote me a kick-ass letter of recommendation.
• Research Experience : Ironically, my application profile was the inverse of most other (“traditional”) applicants. Usually, applicants to (Bio-)Statistics Ph.D.’s are math/stat undergrad majors from very respectable institutions with little to no research experience. My profile was the opposite of that. I had almost two years worth of full-time research experience but a lack of formal preparation. I think my research background in causal inference and ML (in addition to my publication) were my strongest assets.
• Grades : I had excellent grades throughout my undergrad. During my first quarter at UChicago, I got two “A-“ and one “Pass” (Injury related). The latter is respectable though certainly not outstanding.
• Leadership Experience : I do think that my leadership roles–e.g., founding a successful data science / social justice org in undergrad–was a big bonus on my application.

The Application Process

I went into this application cycle with the mentality of “giving it a shot”. I was fully prepared to get rejected by all programs because I thought my strengths did not quite outweigh my weaknesses. With that in mind, I applied to both masters degrees (Only in Canada for funding and personal reasons) and my top Ph.D. programs. Here is the list of the places I applied to:

I applied to the UChicago Data Science Ph.D. mostly because I was currently working at the UChicago Data Science Institute and I knew a good amount of the faculty. I applied to Masters only in Canada because (1) I was eligible for German government funding in Canada but not the U.S., (2) For personal reasons, and (3) Overall cost and quality of life. Additionally, I was a finalist for a full-ride leadership-based scholarship at McGill.

You may also notice that I didn’t apply to many other top-ranked departments. This is because I either found little research fit or (this was mostly the case) I didn’t want to live wherever the school was located. I encourage you to not sweep this factor under the rug.

I also had an internal ranking. The following factors were most important to me:

• Substantial research in causal inference and networks: #1 JHU, #2 Brown, #3 UW, #4 Yale
• Location and access to nature: #1 UW, #2 Berkeley
• Research fit with individual faculty: #1 JHU, Berkeley, Brown, Yale, #2 Harvard

One more thing: Since I was ready to get rejected from all programs, I was working towards being a more competitive applicant during the next cycle while I was applying. In that sense, I had started a graduate research assistantship at UChicago in causal inference methodology and started TA’ing. This forward-looking approach really helped me mentally since I kept reassuring myself that I can just try again next year.

Preparation

First things first, I decided to take the GRE. I performed slighly above average, but nothing outstanding. Overall, I found the GRE to be a colossal waste of time, money, and energy. If I could redo my application cycle, I would have opted to not take it and scratch the programs that require it (Only two of them) off my list. After all, with all the evidence that is out there showing how the GRE puts marginalized students at a disadvantage, merely requiring the GRE is a huge red flag for me.

I started preparing my application materials very early, around June, because I knew I had a ton of time over the summer (as opposed to little to no time in the fall). One thing I did that made life significantly easier for myself and my recommenders is to start a GitHub repository called grad-apps that contained all my application materials. At the time I was writing my applications, this repo was public, so all my recommenderes and mentors could have easy access to it. It is now private (and will remain private) for privacy reasons.

That being said, I am happy to share how I set it up. Here is content of the README.md file that sketches out the basic setup:

Hi! If you are reading this, I want to thank you for helping me in my graduate school application process. Thank you so much for your support.

• You can navigate this repository via the branches
• Please access my most up-to-date CV and this README via this main branch
• Each program I am applying to has a corresponding branch with the following format: [institution]-[type]-[program], e.g., “uw-phd-biostatistics” for the University of Washington’s Biostatistics Ph.D.
• Since this is a public repo, please note that you do not have editing access. If you have comments/ suggestions, please do not directly edit these materials. Instead, please use the comment function and/or let me know separately.

Please note that I update this repo every time I make local changes. If you have any questions at all, please reach out to me via email or via cell.

[Followed by a table of programs with every deadline]

My Application Materials

I have decided to post my application materials for Brown’s Biostatistics Ph.D., the program I will be attending in the fall. It should go without saying that you should under no circumstances copy and paste from my materials. That being said, I know that sometimes it is difficult to find good examples of SOP’s, personal statements, and CV’s. If there are any additional programs that you would like to see my application materials for, please reach out to me via email .

Now, I will provide some commentary and context on these application materials. I was extremely lucky to have a handful of faculty and UChicago’s GRAD advising staff give me feedback. With that being said, please don’t treat these materials as the gold standard. They are by no means perfect.

• Education, Coursework, Awards : I initially had grades on there as well (Nothing worse than an A-) but a professor recommended against that because the “A-“ could catch somebody’s eye early. I know how nitpicky this sounds but I am just echo’ing what he said here.
• Research Experience and Community Service : Some folks said that my CV boasts a lot of details but that was on purpose. My research experience was my strongest asset and I wanted to highlight everything I did.
• Presentations, Professional Service, Skills : Having sections for this is not necessary for Ph.D. applicants because you may not have gathered any substantial experiences yet. I did have some space to spare though, so I decided to include some of the presentations and talks I had given. One more thing: Don’t forget to include your programming skills! I know some programs, e.g., UW Biostatistics, explicitly require that on your CV.

SOP and Personal Statement

• General : My SOP’s were identical across all programs I applied to with the exception of two paragraphs (Of course I adjusted the name of school in the other paragraphs lol). Generally though, I wanted to lead with an eye-catcher (the bolded sentence in the first paragraph). Then, I wanted to get addressing my main weakness (the lack of rigorous coursework) out of the way (that’s paragraph #2). Up to paragraph #6, I describe my research experience and how it led to my current interest.
• Paragraphs #7 and #8 : I use paragraph #7 to talk about how my interests tie into the program/department/faculty members. A lot of times, people will put this paragraph as the second paragraph which may work very well with your application. The last paragraph is similar to seventh paragraph but focuses more on research community, centers, and personal fit.
• Personal Statement : This was identical across all schools I applied to.

Overall, I think that being very neat and submitting “pretty” application materials was a tiny Brownie point for me. I would recommend submitting neat materials to anyone and if you can use something like LaTeX you may make an extra good impression.

After submitting all my applications and trying to financially recover, the waiting game began. Pretty soon after winter break was over, I heard back from Brown. They wanted to have my autumn quarter transcript. About two days after I sent that over, I was officially invited to interview day.

The interview day (virtual) was an all-day thing, filled with various info sessions and interviews. I know that about 30 people out of ~380 applicants (which is an insane number) were invited to interview day. Here is a rough outline of my schedule:

• Info session for all the shortlisted candidates across all the Ph.D. programs in the School of Public Health (incl. Epidemioligy, Biostatistics, etc.)
• Departmental info session specific to Biostatistics
• 3-4 interviews with faculty. One interview was with a member of the admissions committee (30min). Additionally, you were allowed to choose up to 3 faculty members for a 15min interview. Since my 30min interview happened to be with a professor I was interested in working with, I ended up choosing only two additional faculty. Thus, I had a total of 3 separate faculty interviews.

I talked to some folks around preparation and decided to go a bit “lighter” than usual. When I am nervous in interviews (which you bet I was), I tend to jump into rabbit holes and try to impress my knowledge. That is generally not a good strategy since you’re being interviewed to assess how you are as a person and the faculty member you’re interviewing with could choose to grillyou on something you said. Keep in mind that you made it to the interview stage because the admissions committee is already impressed with your qualifications on paper . With that in mind, here is how I prepared:

• Researched each interviewer’s active grants (Important in Biostatistics especially) and what the project was about. Wrote out 2-3 questions about that work.
• Researched each interviewer’s fields of interest and recent work and crystallized out one or two overlapping interests. I didn’t fully read through any person’s paper because of the reason I stated above.
• Looked into each interviewer’s story to look for common ground (If there is something striking that could be a phenomenal ice breaker)

After having prepared, here is how I experienced the interview:

• Very relaxed and cordial atmosphere. I was not grilled on any technical questions though I was asked a specific (but very fair) question about my published paper.
• It felt like a true conversation where faculty seemed to be most interested in who I am as a person and less about my qualifications.
• It seemed that my interviewers really appreciated that I knew about their active grants. Of course, they don’t expect you to know everything about it (that’s their job, after all) but just showing that you did your “homework” makes a great impression.

After I got done with the interviews, I had a feeling that I did really well.

Every year I read a lot of grad school applications from accomplished people that don't give me the info I'm looking for. It feels like a major hidden curriculum thing. So here's (my opinion on) how to write a great Statement of Purpose/Research for a PhD program. 🧵 1/ — Roman Feiman (@RomanFeiman) October 27, 2022

The Waiting Game

Ironically, despite an overwhelming feeling that did super well, I did’t hear back for a really long time. In the previous stages, Brown had been very quick, so I expected a decision within 2 weeks of the interview. One month, then two months passed–and nothing. It was really tough on my mental health because I had only heard back negative news from the other programs so far and I was losing hope. Please make sure to take care of yourself while you wait. For me, working out and going to therapy were two great outlets.

Then, around mid March, I got the email and call that I was admitted. I had gotten off the waitlist.

My advice to you while you wait: Please don’t tie your personal value into these applications. I knew coming in that I was an excellent candidate but also how stiff the competition was. Ph.D. admissions truly are a blackbox and as long as you do everything in your power to maximize your chances, you should be very proud of yourself.

Decision Time

My final results.

As of April 15th (Decision day), these are my results from the admissions process:

As you can see, it was a tough cycle. I was very surprised by some rejections and less so by others. One important thing I want to mention: I believe I got rejected from most programs because I was not a “traditional” math/stat undergrad from a great school. There is only so much I can do to remedy that ex-post. I decided to still share this post because I had been told from faculty and mentors that the materials themselves were strong. Remind yourself that it only takes one singular program to admit you.

My Decision

Brown ended up flying me out and organizing a visit day. I felt like it was a particularly strong match because of the cordial and down-to-earth department culture. I was at the time still debating between McGill and Brown–since I could have (with an almost certain guarantee) transferred into the Ph.D. program after Year #1.

My decision ended up coming down to:

• Research fit: I can work at the intersection of causal inference and networks.
• Stipend and benefits: Brown pays one of the highest stipends I have heard of.
• Departmental/ Culture fit: The department seems amazing and my gut is telling me I will be very happy there.

Funding is an important thing to consider, as well. Brown was amongst the top-paying (if not the top-paying) schools out of my list. In my admissions offer, alongside health insurance and full tuition, I am guaranteed a stipend of $49,012 and a one-time first-year supplement stipend of $1,750. When evaluating funding decisions, it does sometimes help looking at whether the school’s graduate students are unionized (Brown’s are).

Final Thoughts

Overall, I am very happy with how things went. I came into this process having two clear favorites–UW and Berkeley Biostatistics–and am coming out very satisfied despite getting rejected by both. This process is very intimidating and I am hoping that my thoughts add a little bit of clarity. If you are a non-traditional applicant, I invite you to reach out to me to have a chat (Caveat: Come fall, I will likely have very limited availability, but I would be happy to chat over the summer). Lastly, please reach out to me if you have any burning questions so I can answer them here.

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JHBC Biostatistics Help: Bloomberg School Graduate Students

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Students can fill out the sign-in form between 9:30 - 11:30 a.m. on Fridays. The biostatistician experts running the clinic on that Friday will send an MS Team note to confirm availability and initiate a MS Teams call.  

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Thesis defense – evelyn jiang.

Thesis Defense: Evelyn Jiang Time: Monday April 29, 2024, at 1:00 PM Eastern Time Location: Kresge 201 Evelyn will present the thesis titled “Predicting gene expression using whole-genome DNA methylation … Continue reading "Thesis Defense – Evelyn Jiang"

Thesis Defense – Pluto Zhang

Thesis Defense: Pluto Zhang Time: Friday May 3, 2024, at 11:00 AM Eastern Time Location: Kresge 201 Pluto will present the thesis titled “Statistical, Machine and Deep Learning Models for … Continue reading "Thesis Defense – Pluto Zhang"

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