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Yale GSAS data about Ph.D. admissions, enrollment, degree completion, and employment, by program.

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Graduate School of Arts and Sciences Programs and Policies 2023–2024

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Statistics and Data Science

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203.432.0666 http://statistics.yale.edu M.A., M.S., Ph.D.

Chair Joseph Chang

Directors of Graduate Studies Andrew Barron (24 Hlh, [email protected] ) John Emerson (24 Hlh, [email protected] )

Professors Donald Andrews ( Economics ), Andrew Barron, Jeffrey Brock ( Mathematics ), Joseph Chang, Katarzyna Chawarska ( Child Study Center ​), Xiaohong Chen ( Economics ), Nicholas Christakis ( Sociology ​), Ronald Coifman ( Mathematics ​), James Duncan ( Radiology and Biomedical Imaging ​), John Emerson ( Adjunct ), Alan Gerber ( Political Science ​), Mark Gerstein ( Molecular Biophysics and Biochemistry ​), Anna Gilbert, John Hartigan ( Emeritus ), Edward Kaplan ( School of Management/Operations Research ​), Harlan Krumholz ( Internal Medicine ​), John Lafferty, Zongming Ma, David Pollard ( Emeritus ), Nils Rudi ( School of Management ), Jasjeet Sekhon, Donna Spiegelman ( Biostatistics ), Daniel Spielman, Hemant Tagare ( Radiology and Biomedical Engineering ​), Van Vu ( Mathematics ), Yihong Wu, Heping Zhang ( Biostatistics ), Hongyu Zhao ( Biostatistics ), Harrison Zhou, Steven Zucker ( Computer Science ​)

Associate Professors P.M. Aronow ( ​Political Science ​), Forrest Crawford ( Biostatistics ), Amin Karbasi ( Electrical Engineering ​), Vahideh Manshadi ( School of Management/Operations ), Ethan Meyers ( Visiting ), Sekhar Tatikonda 

Assistant Professors Elisa Celis, Zhou Fan, Joshua Kalla ( Political Science ), Roy Lederman, Lu Lu, Fredrik Savje ( Political Science ​), Dustin Scheinost ( Radiology and Biomedical Imaging ), Andre Wibisono ( Computer Science ), Zhuoran Yang, Ilker Yildirim ( Psychology ), Ilias Zadik

Fields of Study

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak conver g ence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

Special Requirements for the Ph.D. Degree in Statistics and Data Science

There is no foreign language requirement. Students take at least twelve courses, usually during the first two years. The department strongly recommends that students take S&DS 551 (Stochastic Processes), S&DS 600 (Advanced Probability), S&DS 610 (Statistical Inference), S&DS 612 (Linear Models), S&DS 625 (Statistical Case Studies),  S&DS 631 (Optimization and Computation), S&DS 632 (Advanced Optimization Techniques), and S&DS 661 (Data Analysis), and requires that students take S&DS 626 (Practical Work). Substitutions are possible with the permission of the director of graduate studies (DGS); courses from other complementary departments such as Mathematics and Computer Science are encouraged. With the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science to fulfill these elective requirements.

The qualifying examination consists of three parts: a written report on an analysis of a data set, one or more written examination(s), and an oral examination. The examinations are taken as scheduled by the department. All parts of the qualifying examination must be completed before the beginning of the third year. A prospectus for the dissertation should be submitted no later than the first week of March in the third year. The prospectus must be accepted by the department before the end of the third year if the student is to register for a fourth year. Upon successful completion of the qualifying examination and the prospectus (and meeting of Graduate School requirements), the student is admitted to candidacy. Students are expected to attend weekly departmental seminars.

Students normally serve as teaching fellows for several terms to acquire professional training. All students are required to be teaching fellows for a minimum of two terms, regardless of the nature of their funding. The timing of this teaching is at the discretion of the DGS. 

Combined Ph.D. Program

The Department of Statistics and Data Science also offers, in conjunction with the Department of Political Science, a combined Ph.D. in Statistics and Data Science and Political Science. For further details, see Political Science .

Master’s Degrees

M.A. in Statistics

Three different M.A. in Statistics are offered. All require completion of eight term courses approved by the DGS; of which one must be in probability, one must be in statistical theory, and one must be in data analysis. The remaining five elective courses may include courses from other departments and, with the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science.

M.A. in Statistics (en route to the Ph.D. in Statistics and Data Science) This degree requires an average grade of HP or higher, and two terms of residence.

M.A. in Statistics (en route to the Ph.D. in other areas of study) Pursuit of this degree requires an application process managed by the DGS of Statistics and Data Science followed by approval from the DGSs from both programs and the cognizant Graduate School dean. All eight courses for this degree must earn grades of HP or higher. Most of the courses for the M.A. in Statistics should be in addition to the requirements of the primary Ph.D. program. This degree also has an academic teaching fellow requirement, to be determined by the DGSs from both programs and the cognizant Graduate School dean.

Terminal M.A. in Statistics Students are also admitted directly to a terminal master of arts program in Statistics. Students must earn an average grade of HP or higher and receive at least one grade of Honors. Full-time students must take a minimum of four courses per term. Part-time students are also accepted into the program. All students are expected to complete two terms of full-time tuition and residence, or the equivalent, at Yale. See Degree Requirements: Terminal M.A./M.S. Degrees, under Policies and Regulations.

Terminal M.S. in Statistics and Data Science Students are also admitted directly to a terminal master of science program in Statistics and Data Science. To qualify for the M.S., the student must successfully complete an approved program of twelve term courses with an average grade of HP or higher and receive at least two grades of Honors, chosen in consultation with the DGS. With the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science to fulfill elective requirements. Full-time students must take a minimum of four courses per term. Part-time students are also accepted into the program. All students are expected to complete three terms of full-time tuition and residence, or the equivalent, at Yale. See Degree Requirements: Terminal M.A./M.S. Degrees, under Policies and Regulations.

Program information is available online at http://statistics.yale.edu .

S&DS 501a / E&EB 510a, Introduction to Statistics: Life Sciences   Jonathan Reuning-Scherer

Statistical and probabilistic analysis of biological problems, presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics. TTh 1pm-2:15pm

S&DS 502a, Introduction to Statistics: Political Science   Jonathan Reuning-Scherer

Statistical analysis of politics, elections, and political psychology. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and public policy. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 503a, Introduction to Statistics: Social Sciences   Jonathan Reuning-Scherer

Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 505a, Introduction to Statistics: Medicine   Jay Emerson and Jonathan Reuning-Scherer

Statistical methods relied upon in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 506a, Introduction to Statistics: Data Analysis   Robert Wooster and Jonathan Reuning-Scherer

An introduction to probability and statistics with emphasis on data analysis. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 530a / PLSC 530a, Data Exploration and Analysis   Ethan Meyers

Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and web data sources are used. HTBA

S&DS 538a, Probability and Statistics   Joseph Chang

Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability: conditional probability, random variables, distributions, law of large numbers, central limit theorem, Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used throughout for calculations, simulations, and analysis of data. Prerequisite: after or concurrently with MATH 118 or MATH 120 . TTh 1pm-2:15pm

S&DS 540a, An Introduction to Probability Theory   Robert Wooster

Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling. This course may be appropriate for non-S&DS graduate students. Prerequisite: MATH 115 or equivalent. MW 2:30pm-3:45pm

S&DS 541a, Probability Theory   Yihong Wu

A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, Markov chains, probabilistic modeling. Prerequisite: calculus of functions of several variables. MW 9am-10:15am

S&DS 542a, Theory of Statistics   Andrew Barron

Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Prerequisite: S&DS 541 . HTBA

S&DS 565a, Introductory Machine Learning   John Lafferty

This course covers the key ideas and techniques in machine learning without the use of advanced mathematics. Basic methodology and relevant concepts are presented in lectures, including the intuition behind the methods. Assignments give students hands-on experience with the methods on different types of data. Topics include linear regression and classification, tree-based methods, clustering, topic models, word embeddings, recurrent neural networks, dictionary learning, and deep learning. Examples come from a variety of sources including political speeches, archives of scientific articles, real estate listings, natural images, and others. Programming is central to the course and is based on the Python programming language. TTh 11:35am-12:50pm

S&DS 572a, YData: Data Science for Political Campaigns   Joshua Kalla

Political campaigns have become increasingly data driven. Data science is used to inform where campaigns compete, which messages they use, how they deliver them, and among which voters. In this course, we explore how data science is being used to design winning campaigns. Students gain an understanding of what data is available to campaigns, how campaigns use this data to identify supporters, and the use of experiments in campaigns. The course provides students with an introduction to political campaigns, an introduction to data science tools necessary for studying politics, and opportunities to practice the data science skills presented in S&DS 523 . W 1:30pm-3:20pm

S&DS 580a, Neural Data Analysis   Ethan Meyers

We discuss data analysis methods that are used in the neuroscience community. Methods include classical descriptive and inferential statistics, point process models, mutual information measures, machine learning (neural decoding) analyses, dimensionality reduction methods, and representational similarity analyses. Each week we read a research paper that uses one of these methods, and we replicate these analyses using the R or Python programming language. Emphasis is on analyzing neural spiking data, although we also discuss other imaging modalities such as magneto/electro-encephalography (EEG/MEG), two-photon imaging, and possibility functional magnetic resonance imaging data (fMRI). Data we analyze includes smaller datasets, such as single neuron recordings from songbird vocal motor system, as well as larger data sets, such as the Allen Brain observatory’s simultaneous recordings from the mouse visual system. TTh 2:30pm-3:45pm

S&DS 600a, Advanced Probability   Sekhar Tatikonda

Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed. TTh 2:30pm-3:45pm

S&DS 610a, Statistical Inference   Harrison Zhou

A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Knowledge of probability theory at the level of S&DS 541 is assumed. TTh 11:35am-12:50pm

S&DS 612a, Linear Models   Zongming Ma

The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to the R statistical language); alternatives to least squares. Prerequisites: linear algebra and some acquaintance with statistics. MW 11:35am-12:50pm

S&DS 625a, Statistical Case Studies   Brian Macdonald

Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Enrollment limited; requires permission of the instructor. HTBA

S&DS 627a, Statistical Consulting   Jay Emerson

Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms ( S&DS 627 and 628 ) and receive one credit at the end of the year. Enrollment limited; requires permission of the instructor.   ½ Course cr F 2:30pm-4:30pm

S&DS 631a / AMTH 631a, Optimization and Computation   Zhuoran Yang

An introduction to optimization and computation motivated by the needs of computational statistics, data analysis, and machine learning. This course provides foundations essential for research at the intersections of these areas, including the asymptotic analysis of algorithms, an understanding of condition numbers, conditions for optimality, convex optimization, gradient descent, linear and conic programming, and NP hardness. Model problems come from numerical linear algebra and constrained least squares problems. Other useful topics include data structures used to represent graphs and matrices, hashing, automatic differentiation, and randomized algorithms. Prerequisites: multivariate calculus, linear algebra, probability, and permission of the instructor. Enrollment is limited, with preference given to graduate students in Statistics and Data Science. TTh 1pm-2:15pm

S&DS 645b / CB&B 645b, Statistical Methods in Computational Biology   Hongyu Zhao

Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics. We discuss statistical issues arising in analyzing population genetics data, gene expression microarray data, next-generation sequencing data, microbiome data, and network data. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and methods of classification and clustering; models include hidden Markov models, Bayesian networks, and graphical models. Offered every other year. Prerequisite: S&DS 538 , S&DS 542 , or S&DS 661 . Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed. Th 10am-11:50am

S&DS 665a, Intermediate Machine Learning   John Lafferty

S&DS 365 is a second course in machine learning at the advanced undergraduate or beginning graduate level. The course assumes familiarity with the basic ideas and techniques in machine learning, for example as covered in S&DS 265 . The course treats methods together with mathematical frameworks that provide intuition and justifications for how and when the methods work. Assignments give students hands-on experience with machine learning techniques, to build the skills needed to adapt approaches to new problems. Topics include nonparametric regression and classification, kernel methods, risk bounds, nonparametric Bayesian approaches, graphical models, attention and language models, generative models, sparsity and manifolds, and reinforcement learning. Programming is central to the course, and is based on the Python programming language and Jupyter notebooks. MW 1pm-2:15pm

S&DS 688a, Computational and Statistical Trade-offs in High Dimensional Statistics   Ilias Zadik

Modern statistical tasks require the use of both computationally efficient and statistically accurate methods. But, can we always find a computationally efficient method that achieves the information-theoretic optimal statistical guarantees? If not, is this an artifact of our techniques, or a potentially fundamental source of computational hardness? This course surveys a new and growing research area studying such questions on the intersection of high dimensional statistics and theoretical computer science. We discuss various tools to explain the presence of such “computational-to-statistical gaps” for several high dimensional inference models. These tools include the “low-degree polynomials” method, statistical query lower bounds, and more. We also discuss connections with other fields such as statistical physics and cryptography. Prerequisites: maturity with probability theory (equivalent of 241/541) and linear algebra and a familiarity with basic algorithms and mathematical statistics. T 4pm-5:50pm

S&DS 690a, Independent Study   Jay Emerson

By arrangement with faculty. Approval of DGS required. HTBA

S&DS 700a, Departmental Seminar   Staff

Presentations of recent breakthroughs in statistics and data science.   0 Course cr M 4pm-5:30pm

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Yale Statistics Ph.D. Program

The Department offers a broad training program comprised of the main areas of statisical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, econometrics, classification, statistical computing, and graphical methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. Recent graduates have accepted appointments at the Duke University, University of California at Santa Barbara, The City University of New York, Yale University, Bristol-Meyers Squibb, RAND, Federal Reserve Board, New York University, Trinity University, Iowa State University, Merck, and Tulane University.

All applications for this program should be submitted directly to the Yale Graduate School Office of Admissions through the online application page.

• Application requirements and guidelines
• GRE scores for the General Test and for the Subject Test (usually in Mathematics, sometimes in the area of the undergraduate major) should accompany an application.
• All applicants should have a strong mathematical background, including advanced calculus, linear algebra, elementary probability theory, and at least one course providing an introduction to mathematical statistics. An undergraduate major may be in statistics, mathematics, computer science, or in a subject in which significant statistical problems may arise.
• For those whose native language is not English, the Test of English as a Foreign Language (TOEFL) scores are required.
• The Ph.D. program admits only a small number of new students each year. Only 6 initial offers (for a pool of 73 applications) were made for admission in fall 2010.
• The offer of admission typically includes full tuition and a stipend. Consult the Graduate School's financial assistance page for details.
• Tuition and Living Costs

Course of Study

• Fourteen courses are required before students can be admitted to candidacy after the second year. Usually students take four courses in each semester of the first year and three courses in each semester of the second year.
• Ph.D. students are strongly advised to take the courses highlighted in RED , which are taught every year, even if they involve some review of material taken in undergraduate courses. Substitutions are possible with the permission of the DGS.
• The theory qualifying exam is usually based on a combination of advanced undergraduate material (as covered in Stat 241, 242, and 251/551) and graduate material at the level of Stat 600, 610, and 612.
• For the practical qualifying exam, students are expected to be comfortable with R, and have had experience at working with real data. Most students gain that experience from a combination of Stat 661, 625 and participation in the statistical consulting clinic (Stat 627).

Normally during the first two years, fourteen term courses in this and other departments are taken to prepare students for research and practice of statistics. These include courses devoted to case studies and practical work, for which students prepare a written report and give an oral presentation. Specific course requirements .

There is no foreign language requirement.

The SPEAK test

For further details consult that web site.

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Teaching requirements

Qualifying examinations.

• Practical Exam: a written report on an analysis of a data set. Held during a five day period in December, following the end of classes.
• Theory Exam: a written paper on theoretical statistics. A one-day exam (9:00 am -- 4:00 pm) held in early January.
• Oral Exam: held shortly after completion of the Theory Exam.
• A typical theory exam . [Look at http://www.stat.yale.edu/dept-private/Exams/ for copies of other old exams.Yale login required].
• Well prepared students sometimes take one of the Practical or Theory Exams in their first year. No record is kept of an unsuccesful attempt.
• Students who do not pass the exams during January/December of their second year have the option of a retake at the end of the spring semester.

Prospectus and Dissertation

Dissertation research in collaboration with one member of the faculty is begun during the third year. A prospectus for the dissertation should be submitted no later than the first week of March in the third year. The prospectus must be accepted by the department before the end of the third year.

Upon successful completion of the qualifying examination and the prospectus (as well as meeting the Graduate School Honors requirements), the student is admitted to candidacy. Most students complete the dissertation in the fifth year.

Please see our Alumni page for a sampling of recent Dissertation topics.

Dissertation fellowships

Further information.

Consult the Graduate School's Programs and Policies for general information about Ph.D. study at Yale.

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Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

• Programs of Study
• MA - Master of Arts
• Department of Statistics and Data Science
• Statistics and Data Science

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Director of Graduate Studies

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Karen Kavanaugh

Departmental Registrar

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• 203-436-8105

Admission Requirements

Standardized testing requirements.

GRE is optional; GRE Mathematics Subject Test is optional.

English Language Requirement

TOEFL iBT or IELTS Academic is required of most applicants whose native language is not English.

You may be exempt from this requirement if you have received (or will receive) an undergraduate degree from a college or university where English is the primary language of instruction, and if you have studied in residence at that institution for at least three years.

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The YSPH Biostatistics Program offers me great flexibility and opportunities to explore my research interests and collaborate with interdisciplinary laboratories locally and globally. The collaborative, supportive and diverse environment allows me to learn, grow and develop both professional and personal skills.

Degree Requirements - PhD in Biostatistics Standard Pathway

2023-24 matriculation.

All courses are 1 unit unless otherwise noted.

The Ph.D. degree requires a total of 16 course units. Course substitutions (other than those noted here) must be identified and approved by the student’s advisor and the DGS.

Required Courses (8 course units)

• BIS 525 Seminar in Biostatistics and Journal Club - 0 units
• BIS 526 Seminar in Biostatistics and Journal Club - 0 units
• BIS 610 Applied Area Readings for Qualifying Exams
• BIS 623 Advanced Regression Analysis OR S&DS 612, Linear Models
• BIS 628 Longitudinal and Multilevel Data Analysis
• BIS 643 Theory of Survival Analysis
• BIS 691 Theory of Generalized Linear Models
• BIS 699 Summer Internship in Biostatistical Research - 0 units
• BIS 508 Foundations of Epidemiology and Public Health
• EPH 600 Research Ethics and Responsibilities - 0 units
• EPH 608 Frontiers of Public Health (not offered in 2023-24)*
• S&DS 610 Statistical Inference

PhD Elective Courses (8 course units)

* Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

Course offerings subject to change.

rev. 7.3.2023

Research Experience

In a number of courses, especially the Statistical Consulting (BIS 678) course students gain actual experience with various aspects of research including preparation of a research grant, questionnaire design, preparation of a database for analysis, and analysis and interpretation of real data. In addition, doctoral students can gain research experience by working with faculty members on ongoing research studies prior to initiating dissertation research, which includes but is not limited to BIS 699. During the summer following each year of course work, candidates are required to take a research rotation that is approved by the department and communicated to the DGS.

The Dissertation

The Department strives for doctoral dissertations that have a strong methodological component motivated by an important health question. Hence, the dissertation should include a methodological advance or a substantial modification of an existing method motivated by a set of data collected to address an important health question. The dissertation must also include the application of the proposed methodology to real data. Students that have chosen the Implementation and Prevention Science Methods pathway must complete a dissertation relevant to this topic. A fairly routine application of widely available statistical methodology is not acceptable as a dissertation topic. Candidates are expected not only to show a thorough knowledge of the posed health question, but also to demonstrate quantitative skills necessary for the creation and application of novel statistical tools.

Recent Dissertation Projects

• Causal Inference for Intervention Effects Under Contagion
• Statistical Methods for Identifying Shared Genetic Architecture and Genetic Risk Factors in Lung Diseases
• Single Cell and Multi-Omics Data Integration Computational Methodologies
• Causal Inference for Time-Varying Treatments for Hypertension
• Ancestry-Specific Genetic and Epigenetic Association Studies of Smoking Initiation and Cessation in Admixed Populations
• Novel Methods for Identification and Inference in Public Health
• Genetic Covariance Analysis Reveals Heterogeneous Etiologic Sharing of Complex Traits: From Theory to Applications
• Latent Space Construction for Analyzing Large Genomic Data Sets
• Leveraging Genomics and Immunomics for More Precise Immunotherapy
• Statistical Methods for Identifying Gene Experession and Epigenetic Signatures in Post-Traumatic Stress Disorder (PTSD)
• Functional Connectivity to Link Genes to Behavior in the Human Brain

MyYSPH.Yale.Edu

Department of Statistics and Data Science

Terminal ma/ms programs.

The Department offers a broad training program comprised of the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods.

With this background, graduates of the program have found excellent positions in industry, and government. See the list of M.A. alumni for examples.

We now offer both the 8-course M.A. in Statistics and a new 12-course M.S. in Statistics & Data Science!  Students may apply to either program but are also allowed to change between programs during their study.  Petitions for such changes should be done at the start of a new semester using the Departmental Transfer Form .

Recent placements

Very recent graduates of the terminal Master’s program have continued their graduate education at Yale, the University of Michigan School of Business, the University British Columbia, Stanford University, and Purdue University.  Other recent graduates have been employed by Oliver Wyman, Captrust, Deutsch Bank, RAAP, Progressive Leasing, Mathematica, McKinsey, 7 Eleven, and Facebook.  These are just a sample of recent graduate activity, and we encourage all alumni to keep in touch and let us know of any updates!

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• A seminar series convened humanities scholars across disciplines to discuss the pervasive nature of data and explore its place in the humanities.
• The next seminar takes place on May 30 and will address what digitization means for the study of antiquity.
• The series will conclude with a symposium on Friday, May 31, from 9 a.m. to 5 p.m. at Wallenberg Hall.

In today’s digital age, data is everywhere. From our smartphones to our voting behaviors, the sheer amount of data generated and collected today is unprecedented.

This data holds the power to reveal profound insights about our world, yet it also raises many questions: What biases influence particular data sets? How do we assign authentic and accurate meaning to data? What are its social and political implications? Where should it be stored, and who should own it? 

These and many other questions are the focus of a year-long seminar series titled “The Data that Divides Us: Recalibrating Data Methods for New Knowledge Frameworks Across the Humanities.” Supported by the Andrew W. Mellon Foundation Sawyer Seminar award and hosted by the Center for Spatial and Textual Analysis (CESTA), the series convenes humanities scholars across disciplines to discuss the pervasive nature of data and to explore its place in the humanities.

“This series is a venue that allows humanists to bring the conversation around data more cogently together across different humanities fields and in relation to data science,” said Giovanna Ceserani, associate professor of classics in the School of Humanities and Sciences , CESTA faculty director, and one of the faculty leads of the series. 

The next seminar will take place on May 30, followed by a symposium on May 31. 

Humanists and data

While data is often associated with the work of engineers, mathematicians, and data scientists, humanities scholars argue that much can be lost when a subject – such as historical events, demographics, or human behaviors – is analyzed strictly by the numbers it produces.

If we start to understand the world only in terms of data, we may lose the more complex interpretations that come with a traditional humanistic inquiry.” Giovanna Ceserani Associate Professor of Classics

“If we start to understand the world only in terms of data, we may lose the more complex interpretations that come with a traditional humanistic inquiry,” Ceserani said.

Humanities scholarship can analyze subjects and their data through unique lenses, producing novel insights. In the first seminar, “The Place of Data,” scholars Alan Liu and Roopika Risam explored how data can reflect or cause modern social divisions. Their research analyzes data related to geography, race, and gender, examining how this information intersects with social divisions and how data can address these issues.

In another seminar titled “Catastrophe, Data, and Transformation,” historian Jessica Otis discussed her NSF-funded project Death by Numbers . This project involved transcribing, publishing, and analyzing London’s mortality statistics to understand how the lived experiences of plague outbreaks intersected with the mentality of early modern England, while Dagomar Degroot discussed modern climate data.

The fifth part of the series featured Marlene Daut, a professor of French and African American studies at Yale University, in a seminar called “Recuperating Forgotten Narratives,” which explored what happens to text when it is digitized and turned into data.

Daut discussed her work to collect and digitize early 19th-century Haitian print media, such as old newspaper articles and other rare or inaccessible materials, documenting Haiti around the time of the country’s revolution. By making such documents accessible online, she found they often contradicted conventional portrayals of Haiti or corrected false narratives about, for example, France’s involvement in slavery there. She also found that Haitians visiting the site were using it to identify relatives for genealogical research.

“The papers [and] the almanacs contained all these names, and so people are using this digital archive the way that they use municipal archives and physical archives, which is to help complete their family trees,” Daut said.

Discussion of the significance of data for family tree research continued in the following seminar, “The Data of Enslavement,” presented by historians Greg O’Malley and Alex Borucki. They shared their project of the Intra-American Slave Trade Database , an online research tool that documents more than 35,000 slave trading voyages from one port in the Americas to another. In related seminars, scholar Lauren Klein discussed different ethical approaches to the data of the slave trade illustrating how visual choices can humanize the stories data tells, and scholar Ayesha Hardison discussed her work collecting and preserving thousands of novels and other texts by Black authors. 

Approaches to data

Data scientists often acquire a large data set from a particular source, then organize and analyze it. Humanities research tends to be more selective, explained Chloé Brault, a Stanford PhD candidate in comparative literature.

“We’re often in the practice of creating our own data,” she said. “For example, that might look like a literary scholar selecting 100 novels to analyze.”

Brault, who is a Mellon Sawyer dissertation fellow, is working on a dissertation investigating the literary production of 1970s Montreal. Using computational tools and carefully selected texts, she measures how literary language changed, or remained the same, between the 1970s and 2020s. 

Ceserani said that asking “ What is the place of data in our work? ” forces humanists to look at the objects they study differently and more critically.

“It forces us to ask questions of data scientists, of their received sources, and their evidence, and to clarify what rises to the status of evidence in their inquiries,” she said. "It also forces us to enter into productive conversations with data scientists, and ask questions of their received data sources." 

Upcoming events

The final seminar is titled “ Ancient Data and Its Divisions ” and presented by Chiara Palladino, assistant professor of classics at Furman University, Eric Harvey, a researcher collaborating with CESTA, and Chris Johanson, associate professor of classics and digital humanities at the University of California, Los Angeles. It will take place Thursday, May 30, from 5:30 to 7 p.m. at Wallenberg Hall. 

The Mellon Sawyer Seminar Series will conclude with a symposium on Friday, May 31, from 9 a.m. to 5 p.m. at Wallenberg Hall. In addition to Ceserani, the faculty PIs for the series are Mark Algee-Hewitt , associate professor of English and digital humanities, Grant Parker , associate professor of classics and African and African American studies, and Laura Stokes , associate professor of history. The series also supports Mellon Sawyer graduate dissertation fellow Matthew Warner, a PhD candidate in English, and postdoctoral scholar Dr. Nichole Nomura, who studies 20th-century American literature and digital humanities. 

In addition to CESTA, the series and symposium are supported by the Stanford Humanities Center (SHC) and the Dean’s Office of the School of Humanities and Sciences.

For more information

Alan Liu is a professor of English at the University of California, Santa Barbara; Roopika Risam is a professor of film and media studies and of comparative literature at Dartmouth College; Jessica Otis is an assistant professor of history at George Mason University; Dagomar Degroot, an associate professor of environmental history at Georgetown University; Greg O’Malley is a professor of history at the University of California, Santa Cruz; Alex Borucki is a professor of history at the University of California, Irvine; Lauren Klein, a professor of quantitative theory and methods and of English at Emory University; and Ayesha Hardison is an associate professor of English and of women, gender, and sexuality studies at the University of Kansas.

Calculate for all schools

Your chance of acceptance, your chancing factors, extracurriculars, yale university admission statistics.

I'm planning on applying to Yale next year and would like to know what their admission statistics look like. Can someone provide information on things like acceptance rates, average GPAs, test scores, and other related data? Thanks in advance!

Sure! Keep in mind that these statistics may change slightly from year to year, but here are some general admissions statistics for Yale University to give you a sense of what their applicant pool looks like:

- Acceptance Rate: For the Class of 2027, Yale's acceptance rate was around 4.5%, making it one of the most selective schools in the country.

- Average High School GPA: Yale typically accepts students with very strong academic records. The average high school GPA of admitted students is is usually 3.95+ on an unweighted 4.0 scale, which indicates a high level of academic achievement. A typical admitted student will also have taken the most rigorous schedule their school offers (usually involving a large number of AP or IB courses).

- Test Scores: Although Yale University was test-optional for the 2020-2024 application cycles, it has switched back to requiring standardized test scores for the 2024-2025 application cycle. For reference, for previous cycles, the middle 50% range of SAT scores for admitted students was as follows: 1500-1560. For the ACT, the middle 50% range was between 33 and 35.

Keep in mind that Yale's admissions process is highly competitive and holistic, meaning they consider many factors beyond test scores and GPAs. This includes things like personal essays, extracurricular activities, letters of recommendation, and demonstrated interest in the school. Even if you meet the average GPA and test scores, there's no guarantee of acceptance, so it's important to pay close attention to all aspects of your application. Good luck with your application!

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2023/2024 PhD Recipients Thesis Titles

2022-2023 PhD Thesis Titles    2021-2022 PhD Thesis Titles    2020-2021 PhD Thesis Titles   

2018-2019 PhD Thesis Titles    2017-2018 PhD Thesis Titles    2019-2020 PhD Thesis Titles   

2018-2019 PhD Thesis Titles    2017-2018 PhD Thesis Titles    2016-2017 PhD Thesis Titles  

2015-2016 PhD Thesis Titles    2013-2014 PhD Thesis Titles    2012-2013 PhD Thesis Titles   

2011-2012 PhD Thesis Titles  

Candidates for the Degree Doctor of Philosophy

Solomon abiola, b.s. princeton university, m.s. carnegie mellon university; translational biomedical science.

Thesis: The Rise of Temperature and Fall of Fever: A 21st-Century Translational Science Approach to Infectious Disease Forecast using Machine Learning Transformers, mHealth Application Node and Wearable Device Edge

Advisor: Dr. Benjamin Miller  

Sara Ali, B.S. Rochester Institute Of Technology, M.S. University of Rochester; Biophysics

Thesis: A Bioinformatics Pipeline for Identifying Structurally Conserved ncRNAs: From Prediction to Validation

Advisor: Dr. David Mathews  

Naemah Alkhars, B.S. Kuwait University, M.S. University of Rochester; Translational Biomedical Science

Thesis: Three-dimensional Maternal influence on Children at High Risk of Severe Early Childhood

Advisor: Dr. Jin Xiao  

Katherine Andersh, B.S. University Of Arizona, M.S. University of Rochester; Neuroscience

Thesis: The role of proinflammatory cytokines in glaucomatous neurodegeneration

Advisor: Dr. Richard Libby  

Uday Baliga, B.S. Colorado State University, M.S. University of Rochester; Pathology

Thesis: Gene Delivery:  Multigenic approaches

Advisor: Dr. David Dean  

Sara Blick-Nitko, B.S. Rochester Institute of Technology, M.S. University of Rochester; Pathology

Thesis: Platelet Ido1 in Plasmodium yoelii Uncomplicated Malaria Infection

Advisor: Dr. Craig Morrell  

Zachary Brehm, B.M. SUNY College Potsdam, M.S. SUNY College Potsdam; Statistics

Thesis: Statistical Methods for the Analysis of Complex Tissue Bulk RNA-seq Data

Advisor: Dr. Matthew McCall  

Tina Bui-Bullock, B.S. The University Of Texas At Austin, M.S. University of Rochester; Microbiology and Immunology

Thesis: Elucidating Host Factors That Modulate Staphylococcus aureus Osteomyelitis Severity in Obesity-Related Type 2 Diabetes

Advisor: Dr. Steven Gill  

Kimberly Burgos Villar, B.A. Daemen College, M.S. University of Rochester; Pathology

Thesis: Expression and Function of SPRR1A, a Novel Marker of the Ischemic Cardiac Border Zone

Advisor: Dr. Eric Small  

Wesley Chiang, B.S. University Of California-Irvine, M.S. University of Rochester; Biophysics

Thesis: Nano for Neuro: Developing Hybrid Quantum Dot Nano-Bio Assemblies to Probe Neuroinflammatory Activation

Advisor: Dr. Todd Krauss  

Jessica Ciesla, B.S. SUNY College of Environmental Science and Forestry, M.S. University of Rochester; Biochemistry

Thesis: Mechanisms Through Which Metabolism and the Human Cytomegalovirus UL26 Protein Contribute to Anti-Viral Signaling

Advisor: Dr. Joshua Munger  

Martin Cole, B.E. The Open University, M.S. University of Rochester; Statistics

Thesis: Scratching the Surface: Surface-Based Cortical Registration and Analysis of Connectivity Functions

Advisor: Dr. Xing Qiu  

Luke Duttweiler, B.A. Houghton College, M.A. SUNY Brockport, M.A. University of Rochester; Statistics

Thesis: Spectral Bayesian Network Theory: Graph Theoretic Solutions to Problems in Bayesian Networks

Advisor: Dr. Sally Thurston and Dr. Anthony Almudevar  

Esraa Furati, M.B.B.S. University of Dammam, M.S. University of Rochester; Pharmacology

Thesis: Insights into the Roles of Aging and Chemokine Signaling During Neuromuscular Regeneration

Advisor: Dr. Joe Chakkalakal

Erin Gibbons, B.S. University Of Connecticut, M.S. University of Rochester; Microbiology and Immunology

Thesis: Investigation of mTORC1-mediated genes Neutrophil Elastase and Glycoprotein-NMB  Demonstrates Tumor Promotion and GPNMB as a Serum Biomarker for  Lymphangioleiomyomatosis (LAM)

Advisor: Dr. Stephen Hammes  

Christie Gilbert Klaczko, B.S. SUNY College of Environmental Science and Forestry; Translational Biomedical Science

Thesis: Oral Cross-kingdom Bacterial-fungal Interactions in a Cross-sectional Pregnant Population Living in Low Socioeconomic Status in Rochester, New York

Jimin Han, B.S. Duquesne University, M.S. University of Rochester; Pathology

Thesis: Investigating the role of CLN3 in retinal pigment epithelium dysfunction in CLN3-Batten

Advisor: Dr. Ruchira Singh  

Jarreau Harrison, B.S. CUNY Medgar Evers College, M.S. University of Rochester; Pharmacology

Thesis: HSPB8 Attenuates Pathological Tau Accumulation

Advisor: Dr. Gail Johnson  

Alicia Healey, B.S. Simmons College, M.S. University of Rochester; Microbiology and Immunology

Thesis:Aryl hydrocarbon receptor activation modulates monocytic cell responses during respiratory viral infection

Advisor: Dr. B. Paige Lawrence  

Omar Hedaya, B.S. Kuwait University, M.S. University of Rochester; Biochemistry

Thesis: uORF-mediated Translational Regulation of GATA4 in the Heart

Advisor: Dr. Peng Yao  

Emma House, B.S. Wayne State University, M.S. University of Rochester; Pathology

Thesis: Investigating the Role of CD4+ T Cells in Flavorings-Related Lung Disease

Advisor: Dr. Matthew D. McGraw  

Yechu Hua, B.A. Shanghai Jiao Tong University; Health Services Research and Policy

Thesis: Did Greater Price Transparency of Hospital Care Lower Health Care Costs?

Advisor: Dr. Yue Li  

Feng Jiang, B.S. Wuhan University, M.S. University of Rochester; Biochemistry

Thesis: The Molecular Mechanism and Biological Impact of Cis-acting Elements and Trans-acting Factors in mRNA Translation Regulation

Amber Kautz, B.S. Cornell University, M.S. Boston University; Epidemiology

Thesis: Maternal Non-Adherence to the Dietary Fat Recommendations During Pregnancy and Neonatal Adiposity and Infant Weight Gain: The Role of Inflammation

Advisor: Dr. Diana Fernandez  

Gabrielle Kosoy, B.S. SUNY College At Geneseo, M.S. University of Rochester; Biophysics

Thesis: Understanding vaccine antibody response: high throughput measurements of equilibrium affinity constants for influenza, cross-reactivity of SARS antibodies, and asthmatic response

Thomas Lamb Jr., B.S. St Josephs College, M.S. University of Rochester; Toxicology

Thesis: Chemical Characterization and Lung Toxicity of Humectants and Flavored E-cigarettes

Advisor: Dr. Irfan Rahman  

Linh Le, B.S. Truman State University, M.S. Truman State University, M.S. University of Rochester; Neuroscience

Thesis: The effects of microglial adrenergic signaling and microglial renewal on Alzheimer’s disease pathology

Advisor: Dr. Ania Majewska  

Jiatong Liu, B.S. Huazhong University of Science and Technology, M.S. University of Rochester; Pathology

Thesis: The Role of Senescent Cells in Aging Fracture Healing

Advisor: Dr. Lianping Xing  

Daniel Lopez, B.A. University Of California-Los Angeles, M.A. Stanford University. MPH Cuny Hunter College; Epidemiology

Thesis: The Neurobiological Correlates of Problematic Gaming Behaviors in Adolescents

Advisor: Dr. Edwin van Wijngaarden  

Ferralita Madere, B.S. Xavier University Of Louisiana, M.S. University of Rochester; Microbiology

Thesis: Elucidating Complex Transkingdom Interactions in the Female Reproductive Tract Microbiome in Health and Disease

Advisor: Dr. Cynthia Monaco  

Courtney Markman, B.S. Rochester Institute Of Technology, M.S. University of Rochester; Pathology

Thesis: The role(s) of JAG1 during Embryonic Cochlear Development

Advisor: Dr. Amy Kiernan  

Andrew Martin, B.S. North Adams State College, M.S. University of Rochester; Microbiology

Thesis: Mechanism and Consequence of IFN--mediated Loss of Tissue Resident Macrophages on Host Immunity to Toxoplasma gondii

Advisor: Dr. Felix Yarovinsky  

Alyssa Merrill, B.S. Nazareth College Of Rochester, M.S. University of Rochester; Toxicology

Thesis: Pregnancy-dependent Cardiometabolic Effects of Anti-estrogenic Endocrine Disrupting Chemicals

Advisor: Dr.Marissa Sobolewski and Dr. Deborah Cory-Slechta  

Briaunna Minor, B.S. Xavier University Of Louisiana, M.S. University of Rochester; Microbiology

Thesis: Implications for Targeting Tumor Associated Neutrophils to Attenuate Estrogen Mediated Lymphangioleiomyomatosis Progression

Mostafa Mohamed, M.S. Alexandria University, M.D. Alexandria University; Epidemiology

Thesis: Association Between Chemotherapy Dosing, Treatment Tolerability, and Survival Among Older Adults with Advanced Cancer

Advisor: Dr. David Rich  

Adrián Moisés Molina Vargas, B.S. University of Alcala, M.S. University of Rochester; Genetics

Thesis: Developing Design Strategies for Efficient and Specific CRISPR Cas13 RNA-Targeting Applications

Advisor: Dr. Mitchell O'Connell  

Teraisa Mullaney, B.S. Rochester Institute Of Technology, M.S Rochester Institute Of Technology, M.S. University of Rochester; Health Services Research and Policy

Thesis: Understanding the Role of Navigation Capital in Health Services and Social Determinants of Health: A Health Capability Explanation

Advisor: Dr. Peter Veazie  

Daxiang Na, B.S. Peking University, M.S. Peking University, M.S. Brandeis University, M.S. University of Rochester; Genetics

Thesis: An Investigation of the Relationship between Auditory Dysfunctions and Alzheimer’s Disease Using Amyloidosis Mouse Models

Advisor: Dr. Patricia White  

Thomas O'Connor, B.S. SUNY University at Buffalo, M.S. University of Rochester; Genetics

Thesis: Adaptive and Protective Responses of Skeletal Muscle to Endurance Exercise in the Context of Aging, Juvenile Radiotherapy, and Tubular Aggregate Myopathy

Advisor: Dr. Robert Dirksen and Dr. James Palis  

Raven Osborn, B.A. University Of Missouri-Columbia; Translational Biomedical Science

Thesis: Single-cell gene regulatory network analysis reveals cell population-specific responses to SARS-CoV-2 infection in lung epithelial cells

Advisor: Dr. Juilee Thakar and Dr. Stephen Dewhurst  

Emily Przysinda, B.A. Skidmore College, M.S. University of Rochester; Neurobiology and Anatomy

Thesis: Social processing and underlying language deficits in schizophrenia during naturalistic video viewing

Advisor: Dr. Edmund Lalor  

Emily Quarato, B.S. University Of Alabama At Birmingham, M.S. University of Rochester; Program

Thesis: High levels of mesenchymal stromal cell efferocytosis induces senescence and causes bone loss

Advisor: Dr. Laura Calvi  

Zahíra Quiñones Tavárez, B.S. Pontificial Catholic University Mother and Teacher, M.P.H. University of Rochester; Translational Biomedical Science

Thesis: Linking Exposure to Flavors in Electronic Cigarettes and Coughing

Advisor: Dr. Deborah Ossip  

Matthew Raymonda, B.S. University Of North Carolina At Wilmington, M.S. University of Rochester; Biochemistry

Thesis: Identifying Metabolic Vulnerabilities Associated with Viral Infections

Savanah Russ, B.A. SUNY Geneseo, M.P.H Yale University; Epidemiology

Thesis: Association Between Community-Level Socioeconomic Status and Spatiotemporal Variation in COVID-19 Vaccine Uptake

Advisor: Dr. Yu Liu  

Cooper Sailer, B.S. University at Buffalo, M.A. University at Buffalo, M.S. University of Rochester; Pathology

Thesis: Characterization of CAR-T cell phenotypes to augment response against solid tumors

Advisor: Dr. Minsoo Kim  

Jishyra Serrano, B.S. Universidad Adventista De Las Antillas; Translational Biomedical Science

Thesis: Prenatal Maternal Stress and Inflammation: Association to Childhood Temperament

Advisor: Dr. Thomas O'Connor  

Yuhang Shi, B.A. Henan Agricultural University, M.S. University of Rochester; Microbiology

Thesis: Interactions Between Viruses and the Innate Antiviral Factors SERINC5, BST2 and BCA2

Advisor: Dr. Ruth Serra-Moreno  

Anjali Sinha, B.E. PES Institute of Technology, M.S. University at Buffalo, M.S. University of Rochester; Neuroscience

Thesis: Role of mAChR signaling and M-current in EVS mediated responses of mammalian vestibular afferents

Advisor: Dr. J. Chris Holt  

Celia Soto, B.S. SUNY Geneseo, M.S. University of Rochester; Pathology

Thesis: Elevated Lactate in Acute Myeloid Leukemia (AML) Bone Marrow Microenvironment Dysfunction

Advisor: Dr. Benjamin Frisch  

Michael Sportiello, B.S. University Of Wisconsin-Milwaukee, M.S. University of Rochester; Microbiology

Thesis: Investigating CD8 T cell tissue resident memory phenotype, function, metabolic activity, and differentiation

Advisor: Dr. David Topham  

Kumari Yoshita Srivastava, B.S. National Institute of Science Education And Research, M.S. National Institute of Science Education And Research, M.S. University of Rochester; Biophysics

Thesis: Structure and Function Analysis of Bacterial Riboswitches that Control Translation

Advisor: Dr. Joseph Wedekind  

Kathryn Toffolo, B.S. SUNY College at Buffalo, M.S. University of Rochester; Neuroscience

Thesis: Semantic Language Processing: Insight into Underlying Circuitry and Development using Neurophysiological and Neuroimaging Methods

Advisor: Dr. John J. Foxe  

Megan Ulbrich, B.S. University Of Pittsburgh, M.S. University of Rochester; Microbiology and Immunology

Thesis: The Activity of Vibrio cholerae Effector VopX Targets Host Cell Pathways that Reorganize the Actin Cytoskeleton

Advisor: Dr. Michelle Dziejman  

Erik Vonkaenel, B.S. Slippery Rock University Of Pennsylvania, M.A. University of Rochester; Statistics

Thesis: Methods for Microglia Image Analysis

Amanda Wahl, B.S. Saint John Fisher College, M.S. University of Rochester; Pharmacology

Thesis: Redefining the function of salivary duct cell populations utilizing a structural, functional, and computational approach

Advisor: Dr. David Yule  

Yunna Xie, B.S. Sichuan University, M.S. Universität Heidelberg; Health Services Research & Policy

Thesis: Is Physician Expertise Working as a Barrier to the Implementation of New Clinical Interventions? A Neural Network Approach

Shen Zhou, B.S. Shanghai University, M.S. Brandeis University, M.S. University of Rochester; Genetics

Thesis: The Study of c-Cbl in Clear Cell Sarcoma

Advisor: Dr. Mark Noble

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Five MIT faculty elected to the National Academy of Sciences for 2024

Guoping feng, piotr indyk, daniel kleitman, daniela rus, senthil todadri, and nine alumni are recognized by their peers for their outstanding contributions to research..

The National Academy of Sciences has elected 120 members and 24 international members, including five faculty members from MIT. Guoping Feng, Piotr Indyk, Daniel J. Kleitman, Daniela Rus, and Senthil Todadri were elected in recognition of their “distinguished and continuing achievements in original research.” Membership to the National Academy of Sciences is one of the highest honors a scientist can receive in their career.

Among the new members added this year are also nine MIT alumni, including Zvi Bern ’82; Harold Hwang ’93, SM ’93; Leonard Kleinrock SM ’59, PhD ’63; Jeffrey C. Lagarias ’71, SM ’72, PhD ’74; Ann Pearson PhD ’00; Robin Pemantle PhD ’88; Jonas C. Peters PhD ’98; Lynn Talley PhD ’82; and Peter T. Wolczanski ’76. Those elected this year bring the total number of active members to 2,617, with 537 international members.

The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.

Guoping Feng

Guoping Feng is the James W. (1963) and Patricia T. Poitras Professor in the Department of Brain and Cognitive Sciences. He is also associate director and investigator in the McGovern Institute for Brain Research, a member of the Broad Institute of MIT and Harvard, and director of the Hock E. Tan and K. Lisa Yang Center for Autism Research.

His research focuses on understanding the molecular mechanisms that regulate the development and function of synapses, the places in the brain where neurons connect and communicate. He’s interested in how defects in the synapses can contribute to psychiatric and neurodevelopmental disorders. By understanding the fundamental mechanisms behind these disorders, he’s producing foundational knowledge that may guide the development of new treatments for conditions like obsessive-compulsive disorder and schizophrenia.

Feng received his medical training at Zhejiang University Medical School in Hangzhou, China, and his PhD in molecular genetics from the State University of New York at Buffalo. He did his postdoctoral training at Washington University at St. Louis and was on the faculty at Duke University School of Medicine before coming to MIT in 2010. He is a member of the American Academy of Arts and Sciences, a fellow of the American Association for the Advancement of Science, and was elected to the National Academy of Medicine in 2023.

Piotr Indyk

Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor of Electrical Engineering and Computer Science. He received his magister degree from the University of Warsaw and his PhD from Stanford University before coming to MIT in 2000.

Indyk’s research focuses on building efficient, sublinear, and streaming algorithms. He’s developed, for example, algorithms that can use limited time and space to navigate massive data streams, that can separate signals into individual frequencies faster than other methods, and can address the “nearest neighbor” problem by finding highly similar data points without needing to scan an entire database. His work has applications on everything from machine learning to data mining.

He has been named a Simons Investigator and a fellow of the Association for Computer Machinery. In 2023, he was elected to the American Academy of Arts and Sciences.

Daniel J. Kleitman

Daniel Kleitman, a professor emeritus of applied mathematics, has been at MIT since 1966. He received his undergraduate degree from Cornell University and his master’s and PhD in physics from Harvard University before doing postdoctoral work at Harvard and the Niels Bohr Institute in Copenhagen, Denmark.

Kleitman’s research interests include operations research, genomics, graph theory, and combinatorics, the area of math concerned with counting. He was actually a professor of physics at Brandeis University before changing his field to math, encouraged by the prolific mathematician Paul Erdős. In fact, Kleitman has the rare distinction of having an Erdős number of just one. The number is a measure of the “collaborative distance” between a mathematician and Erdős in terms of authorship of papers, and studies have shown that leading mathematicians have particularly low numbers.

He’s a member of the American Academy of Arts and Sciences and has made important contributions to the MIT community throughout his career. He was head of the Department of Mathematics and served on a number of committees, including the Applied Mathematics Committee. He also helped create web-based technology and an online textbook for several of the department’s core undergraduate courses. He was even a math advisor for the MIT-based film “Good Will Hunting.”

Daniela Rus

Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, is the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She also serves as director of the Toyota-CSAIL Joint Research Center.

Her research on robotics, artificial intelligence, and data science is geared toward understanding the science and engineering of autonomy. Her ultimate goal is to create a future where machines are seamlessly integrated into daily life to support people with cognitive and physical tasks, and deployed in way that ensures they benefit humanity. She’s working to increase the ability of machines to reason, learn, and adapt to complex tasks in human-centered environments with applications for agriculture, manufacturing, medicine, construction, and other industries. She’s also interested in creating new tools for designing and fabricating robots and in improving the interfaces between robots and people, and she’s done collaborative projects at the intersection of technology and artistic performance.

Rus received her undergraduate degree from the University of Iowa and her PhD in computer science from Cornell University. She was a professor of computer science at Dartmouth College before coming to MIT in 2004. She is part of the Class of 2002 MacArthur Fellows; was elected to the National Academy of Engineering and the American Academy of Arts and Sciences; and is a fellow of the Association for Computer Machinery, the Institute of Electrical and Electronics Engineers, and the Association for the Advancement of Artificial Intelligence.

Senthil Todadri

Senthil Todadri , a professor of physics, came to MIT in 2001. He received his undergraduate degree from the Indian Institute of Technology in Kanpur and his PhD from Yale University before working as a postdoc at the Kavli Institute for Theoretical Physics in Santa Barbara, California.

Todadri’s research focuses on condensed matter theory. He’s interested in novel phases and phase transitions of quantum matter that expand beyond existing paradigms. Combining modeling experiments and abstract methods, he’s working to develop a theoretical framework for describing the physics of these systems. Much of that work involves understanding the phenomena that arise because of impurities or strong interactions between electrons in solids that don’t conform with conventional physical theories. He also pioneered the theory of deconfined quantum criticality, which describes a class of phase transitions, and he discovered the dualities of quantum field theories in two dimensional superconducting states, which has important applications to many problems in the field.

Todadri has been named a Simons Investigator, a Sloan Research Fellow, and a fellow of the American Physical Society. In 2023, he was elected to the American Academy of Arts and Sciences

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