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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

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Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

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Doctoral thesis: guiding deep probabilistic models, doctoral thesis: designing for participation and power in data collection and analysis.

• Current MIT Graduate Students

Please Note: The CSE PhD application deadline was December 1; the portal is now closed and we are no longer accepting applications for 2024 enrollment.

Thank you for considering the academic programs of the MIT Center for Computational Science and Engineering (CCSE) as part of your graduate education options.

CCSE offers a master’s degree and two doctoral programs in computational science and engineering (CSE) – one leading to a standalone doctoral degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an i nterdisciplinary doctoral degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD). Information about the application and admission process for each program is available via the links of the left and summarized below. MIT Registrar’s Office provides graduate tuition and fee rates as set by the MIT Corporation and the Graduate Admissions section of MIT’s Office of Graduate Education (OGE) website contains additional information about costs of attendance and funding .

The standalone CSE PhD program is intended for students who plan to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the Schwarzman College of Computing. Applicants to the standalone CSE PhD program are expected to have an undergraduate degree in CSE, applied mathematics, or another field that prepares them for an advanced degree in CSE.

In contrast, the interdisciplinary Dept-CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline. Applicants to the Dept-CSE PhD program should have an undergraduate degree in a related core disciplinary area as well as a strong foundation in applied mathematics, physics, or related fields.

When completing the MIT CSE graduate application , students are expected to declare which of the two programs they are interested in. Admissions decisions will take into account these declared interests, along with each applicant’s academic background, preparation, and fit to the program they have selected.

The Master of Science in Computational Science and Engineering (CSE SM) program , which is currently not accepting external applications,  is an interdisciplinary program for students interested in the development, analysis, and application of state-of-the-art computational approaches to science and engineering problems. Examples of such approaches include but are not limited to: numerical methods for integral and partial differential equations; molecular and stochastic simulation; model reduction; uncertainty quantification; computational statistics; optimization; high-performance computing; and machine learning applied to science and engineering problems. The curriculum comprises a core set of subjects focused on cross-cutting computational methodologies and an elective component encompassing specific disciplinary topics in science and engineering. Additional information about CSE SM admissions, including any future plans to restart external admissions, will be posted on the CSE SM Admissions webpage if/when they become available. In the interim, external requests for information about the CSE SM program will not be addressed. Thank you in advance for your understanding.

CSE’s graduate offerings require some background in computational science and engineering (CSE) and lead to degrees in CSE. They are not programs in computer science (CS). In particular, they have a different research and education focus from MIT’s computer science programs. Students who are instead interested in a graduate degree in computer science should apply to the graduate program of the Department of Electrical Engineering and Computer Science (EECS) .

MIT Graduate Admissions Statement March 26, 2020

In response to the challenges of teaching, learning, and assessing academic performance during the global COVID-19 pandemic, MIT has adopted the following principle: MIT’s admissions committees and offices for graduate and professional schools will take the significant disruptions of the COVID-19 outbreak in 2020 into account when reviewing students’ transcripts and other admissions materials as part of their regular practice of performing individualized, holistic reviews of each applicant.

In particular, as we review applications now and in the future, we will respect decisions regarding the adoption of Pass/No Record (or Credit/No Credit or Pass/Fail) and other grading options during the unprecedented period of COVID-19 disruptions, whether those decisions were made by institutions or by individual students. We also expect that the individual experiences of applicants will richly inform applications and, as such, they will be considered with the entirety of a student’s record.

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3 Questions: A new PhD program from the Center for Computational Science and Engineering

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This fall, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, is introducing a new standalone PhD degree program that will enable students to pursue research in cross-cutting methodological aspects of computational science and engineering. The launch follows approval of the center’s degree program proposal at the May 2023 Institute faculty meeting.

Doctoral-level graduate study in computational science and engineering (CSE) at MIT has, for the past decade, been offered through an interdisciplinary program in which CSE students are admitted to one of eight participating academic departments in the School of Engineering or School of Science. While this model adds a strong disciplinary component to students’ education, the rapid growth of the CSE field and the establishment of the MIT Schwarzman College of Computing have prompted an exciting expansion of MIT’s graduate-level offerings in computation.

The new degree, offered by the college, will run alongside MIT’s existing interdisciplinary offerings in CSE, complementing these doctoral training programs and preparing students to contribute to the leading edge of the field. Here, CCSE co-directors Youssef Marzouk and Nicolas Hadjiconstantinou discuss the standalone program and how they expect it to elevate the visibility and impact of CSE research and education at MIT.

Q: What is computational science and engineering?

Marzouk: Computational science and engineering focuses on the development and analysis of state-of-the-art methods for computation and their innovative application to problems of science and engineering interest. It has intellectual foundations in applied mathematics, statistics, and computer science, and touches the full range of science and engineering disciplines. Yet, it synthesizes these foundations into a discipline of its own — one that links the digital and physical worlds. It’s an exciting and evolving multidisciplinary field.

Hadjiconstantinou: Examples of CSE research happening at MIT include modeling and simulation techniques, the underlying computational mathematics, and data-driven modeling of physical systems. Computational statistics and scientific machine learning have become prominent threads within CSE, joining high-performance computing, mathematically-oriented programming languages, and their broader links to algorithms and software. Application domains include energy, environment and climate, materials, health, transportation, autonomy, and aerospace, among others. Some of our researchers focus on general and widely applicable methodology, while others choose to focus on methods and algorithms motivated by a specific domain of application.

Q: What was the motivation behind creating a standalone PhD program?

Marzouk: The new degree focuses on a particular class of students whose background and interests are primarily in CSE methodology, in a manner that cuts across the disciplinary research structure represented by our current “with-departments” degree program. There is a strong research demand for such methodologically-focused students among CCSE faculty and MIT faculty in general. Our objective is to create a targeted, coherent degree program in this field that, alongside our other thriving CSE offerings, will create the leading environment for top CSE students worldwide.

Hadjiconstantinou: One of CCSE’s most important functions is to recruit exceptional students who are trained in and want to work in computational science and engineering. Experience with our CSE master’s program suggests that students with a strong background and interests in the discipline prefer to apply to a pure CSE program for their graduate studies. The standalone degree aims to bring these students to MIT and make them available to faculty across the Institute.

Q: How will this impact computing education and research at MIT?  

Hadjiconstantinou: We believe that offering a standalone PhD program in CSE alongside the existing “with-departments” programs will significantly strengthen MIT’s graduate programs in computing. In particular, it will strengthen the methodological core of CSE research and education at MIT, while continuing to support the disciplinary-flavored CSE work taking place in our participating departments, which include Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Materials Science and Engineering; Mechanical Engineering; Nuclear Science and Engineering; Earth, Atmospheric and Planetary Sciences; and Mathematics. Together, these programs will create a stronger CSE student cohort and facilitate deeper exchanges between the college and other units at MIT.

Marzouk: In a broader sense, the new program is designed to help realize one of the key opportunities presented by the college, which is to create a richer variety of graduate degrees in computation and to involve as many faculty and units in these educational endeavors as possible. The standalone CSE PhD will join other distinguished doctoral programs of the college — such as the Department of Electrical Engineering and Computer Science PhD; the Operations Research Center PhD; and the Interdisciplinary Doctoral Program in Statistics and the Social and Engineering Systems PhD within the Institute for Data, Systems, and Society — and grow in a way that is informed by them. The confluence of these academic programs, and natural synergies among them, will make MIT quite unique.

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Computational and Systems Biology PhD Program

Computational and systems biology.

The field of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the human genome project and massively parallel approaches to probing biological samples, have created new opportunities to understand biological problems from a systems perspective. Systems modeling and design are well established in engineering disciplines but are newer in biology. Advances in computational and systems biology require multidisciplinary teams with skill in applying principles and tools from engineering and computer science to solve problems in biology and medicine. To provide education in this emerging field, the Computational and Systems Biology (CSB) program integrates MIT's world-renowned disciplines in biology, engineering, mathematics, and computer science. Graduates of the program are uniquely prepared to make novel discoveries, develop new methods, and establish new paradigms. They are also well-positioned to assume critical leadership roles in both academia and industry, where this field is becoming increasingly important.

Computational and systems biology, as practiced at MIT, is organized around "the 3 Ds" of description, distillation, and design. In many research programs, systematic data collection is used to create detailed molecular- or cellular-level descriptions of a system in one or more defined states. Given the complexity of biological systems and the number of interacting components and parameters, system modeling is often conducted with the aim of distilling the essential or most important subsystems, components, and parameters, and of obtaining simplified models that retain the ability to accurately predict system behavior under a wide range of conditions. Distillation of the system can increase the interpretability of the models in relation to evolutionary and engineering principles such as robustness, modularity, and evolvability. The resulting models may also serve to facilitate rational design of perturbations to test understanding of the system or to change system behavior (e.g., for therapeutic intervention), as well as efforts to design related systems or systems composed of similar biological components.

CSB Faculty and Research

More than 70 faculty members at the Institute participate in MIT's Computational and Systems Biology Initiative (CSBi). These investigators span nearly all departments in the School of Science and the School of Engineering, providing CSB students the opportunity to pursue thesis research in a wide variety of different laboratories. It is also possible for students to arrange collaborative thesis projects with joint supervision by faculty members with different areas of expertise. Areas of active research include computational biology and bioinformatics, gene and protein networks, regulatory genomics, molecular biophysics, instrumentation engineering, cell and tissue engineering, predictive toxicology and metabolic engineering, imaging and image informatics, nanobiology and microsystems, biological design and synthetic biology, neurosystems biology, and cancer biology.

The CSB PhD Program

The CSB PhD program is an Institute-wide program that has been jointly developed by the Departments of Biology, Biological Engineering, and Electrical Engineering and Computer Science. The program integrates biology, engineering, and computation to address complex problems in biological systems, and CSB PhD students have the opportunity to work with CSBi faculty from across the Institute. The curriculum has a strong emphasis on foundational material to encourage students to become creators of future tools and technologies, rather than merely practitioners of current approaches. Applicants must have an undergraduate degree in biology (or a related field), bioinformatics, chemistry, computer science, mathematics, statistics, physics, or an engineering discipline, with dual-emphasis degrees encouraged.

CSB Graduate Education

All students pursue a core curriculum that includes classes in biology and computational biology, along with a class in computational and systems biology based on the scientific literature. Advanced electives in science and engineering enhance both the breadth and depth of each student's education. During their first year, in addition to coursework, students carry out rotations in multiple research groups to gain a broader exposure to work at the frontier of this field, and to identify a suitable laboratory in which to conduct thesis research. CSB students also serve as teaching assistants during one semester in the second year to further develop their teaching and communication skills and facilitate their interactions across disciplines. Students also participate in training in the responsible conduct of research to prepare them for the complexities and demands of modern scientific research. The total length of the program, including classwork, qualifying examinations, thesis research, and preparation of the thesis is roughly five years.

The CSB curriculum has two components. The first is a core that provides foundational knowledge of both biology and computational biology. The second is a customized program of electives that is selected by each student in consultation with members of the CSB graduate committee. The goal is to allow students broad latitude in defining their individual area of interest, while at the same time providing oversight and guidance to ensure that training is rigorous and thorough.

Core Curriculum

The core curriculum consists of three classroom subjects plus a set of three research rotations in different research groups. The classroom subjects fall into three areas described below.

Modern Biology (One Subject): A term of modern biology at MIT strengthens the biology base of all students in the program. Subjects in biochemistry, genetics, cell biology, molecular biology, or neurobiology fulfill this requirement. The particular course taken by each student will depend on their background and will be determined in consultation with graduate committee members.

Computational Biology (One Subject): A term of computational biology provides students with a background in the application of computation to biology, including analysis and modeling of sequence, structural, and systems data. This requirement can be fulfilled by 7.91[J] / 20.490[J] Foundations of Computational and Systems Biology.

Topics in Computational and Systems Biology (One Subject): All first-year students in the program participate in / 7.89[J] Topics in Computational and Systems Biology, an exploration of problems and approaches in the field of computational and systems biology through in-depth discussion and critical analysis of selected primary research papers. This subject is restricted to first-year PhD students in CSB or related fields in order to build a strong community among the class. It is the only subject in the program with such a limitation.

Research Group Rotations (Three Rotations): To assist students with lab selection and provide a range of research activities in computational and systems biology, students participate in three research rotations of one to two months' duration during their first year. Students are encouraged to gain experience in experimental and computational approaches taken across different disciplines at MIT.

Advanced Electives

The requirement of four advanced electives is designed to develop both breadth and depth. The electives add to the base of the diversified core and contribute strength in areas related to student interest and research direction. To develop depth, two of the four advanced electives must be in the same research area or department. To develop breadth, at least one of the electives must be in engineering and at least one in science. Each student designs a program of advanced electives that satisfies the distribution and area requirements in close consultation with members of the graduate committee.

Additional Subjects: As is typical for students in other doctoral programs at MIT, CSB PhD students may take classes beyond the required diversified core and advanced electives described above. These additional subjects can be used to add breadth or depth to the proposed curriculum, and might be useful to explore advanced topics relevant to the student's thesis research in later years. The CSB Graduate Committee works with each graduate student to develop a path through the curriculum appropriate for his or her background and research interests.

Training in the Responsible Conduct of Research: Throughout the program, students will be expected to attend workshops and other activities that provide training in the ethical conduct of research. This is particularly important in interdisciplinary fields such as computational and systems biology, where different disciplines often have very different philosophies and conventions. By the end of the fourth year, students will have had about 16 hours of training in the responsible conduct of research.

Qualifying Exams: In addition to coursework and a research thesis, each student must pass a written and an oral qualifying examination at the end of the second year or the beginning of the third year. The written examination involves preparing a research proposal based on the student's thesis research, and presenting the proposal to the examination committee. This process provides a strong foundation for the thesis research, incorporating new research ideas and refinement of the scope of the research project. The oral examination is based on the coursework taken and on related published literature. The qualifying exams are designed to develop and demonstrate depth in a selected area (the area of the thesis research) as well as breadth of knowledge across the field of computational and systems biology.

Thesis Research: Research will be performed under the supervision of a CSBi faculty member, culminating in the submission of a written thesis and its oral defense before the community and thesis defense committee. By the second year, a student will have formed a thesis advisory committee that they will meet with on an annual basis.

Degree programs

Mit offers a wide range of degrees and programs..

All graduate students, whether or not they are participating in an interdepartmental program, must have a primary affiliation with and be registered in a single department. Every applicant accepted by MIT is admitted through one of the graduate departments. MIT has a number of established interdepartmental programs, and there are many more opportunities for students to arrange interdepartmental programs with interested faculty members.

All MIT graduate degree programs have residency requirements, which reflect academic terms (excluding summer). Some degrees also require completion of an acceptable thesis prepared in residence at MIT, unless special permission is granted for part of the thesis work to be accomplished elsewhere. Other degrees require a pro-seminar or capstone experience.

Applicants interested in graduate education should apply to the department or graduate program conducting research in the area of interest. Below is an alphabetical list of all the available departments and programs that offer a graduate-level degree.

Interested in reading first-hand accounts of MIT graduate students from a variety of programs? Visit the Grad Blog . Prospective students who want to talk with a current student can reach out to their department(s) of interest for connections or, if they are interested in the MIT experience for diverse communities, can reach out to a GradDiversity Ambassador .

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• Chemistry (Course 5)
• Earth, Atmospheric and Planetary Sciences (Course 12)
• Mathematics (Course 18)
• Mathematics with Computer Science (Course 18-​C)
• Physics (Course 8)
• Department of Electrical Engineering and Computer Science
• Institute for Data, Systems, and Society
• Chemistry and Biology
• Climate System Science and Engineering
• Computation and Cognition
• Computer Science and Molecular Biology
• Computer Science, Economics, and Data Science
• Humanities and Engineering
• Humanities and Science
• Urban Science and Planning with Computer Science
• African and African Diaspora Studies
• American Studies
• Ancient and Medieval Studies
• Applied International Studies
• Asian and Asian Diaspora Studies
• Biomedical Engineering
• Energy Studies
• Entrepreneurship and Innovation
• Environment and Sustainability
• Latin American and Latino/​a Studies
• Middle Eastern Studies
• Polymers and Soft Matter
• Public Policy
• Russian and Eurasian Studies
• Statistics and Data Science
• Women's and Gender Studies
• Advanced Urbanism
• Computational and Systems Biology
• Computational Science and Engineering
• Design and Management (IDM &​ SDM)
• Joint Program with Woods Hole Oceanographic Institution
• Leaders for Global Operations
• Microbiology
• Music Technology and Computation
• Operations Research
• Real Estate Development
• Social and Engineering Systems
• Supply Chain Management
• Technology and Policy
• Transportation

School of Architecture and Planning

School of engineering.

• Aeronautics and Astronautics Fields (PhD)
• Artificial Intelligence and Decision Making (Course 6-​4)
• Biological Engineering (PhD)
• Nuclear Science and Engineering (PhD)

School of Humanities, Arts, and Social Sciences

• Humanities (Course 21)
• Humanities and Engineering (Course 21E)
• Humanities and Science (Course 21S)

Sloan School of Management

School of science.

• Brain and Cognitive Sciences (PhD)
• Earth, Atmospheric and Planetary Sciences Fields (PhD)
• Interdisciplinary Programs (SB)
• Climate System Science and Engineering (Course 1-​12)
• Computer Science, Economics, and Data Science (Course 6-​14)
• Interdisciplinary Programs (Graduate)
• Computation and Cognition (MEng)
• Computational Science and Engineering (SM)
• Computational Science and Engineering (PhD)
• Computer Science, Economics, and Data Science (MEng)
• Leaders for Global Operations (MBA/​SM and SM)
• Music Technology and Computation (SM and MASc)
• Real Estate Development (SM)
• Statistics (PhD)
• Supply Chain Management (MEng and MASc)
• Technology and Policy (SM)
• Transportation (SM)
• Aeronautics and Astronautics (Course 16)
• Aerospace Studies (AS)
• Civil and Environmental Engineering (Course 1)
• Comparative Media Studies /​ Writing (CMS)
• Comparative Media Studies /​ Writing (Course 21W)
• Computational and Systems Biology (CSB)
• Computational Science and Engineering (CSE)
• Concourse (CC)
• Data, Systems, and Society (IDS)
• Earth, Atmospheric, and Planetary Sciences (Course 12)
• Economics (Course 14)
• Edgerton Center (EC)
• Electrical Engineering and Computer Science (Course 6)
• Engineering Management (EM)
• Experimental Study Group (ES)
• Global Languages (Course 21G)
• Health Sciences and Technology (HST)
• Linguistics and Philosophy (Course 24)
• Management (Course 15)
• Media Arts and Sciences (MAS)
• Military Science (MS)
• Music and Theater Arts (Course 21M)
• Naval Science (NS)
• Science, Technology, and Society (STS)
• Special Programs
• Supply Chain Management (SCM)
• Urban Studies and Planning (Course 11)
• Women's and Gender Studies (WGS)

The purpose of the academic program at MIT is to give students a solid command of basic principles, a versatility of insight and perspective concerning natural and social phenomena, the habit of continued learning, and the power that comes from a thorough and systematic approach to learning. From these attributes comes the best assurance for continued professional and personal growth, especially in today's rapidly changing world.

The undergraduate academic program is based on a core of General Institute Requirements (GIRs)  and on the specific curricula offered by departments for undergraduate majors. All undergraduate Courses at MIT lead to the Bachelor of Science (SB) degree. For most undergraduates, degree-granting programs require four years of full-time study.

Graduate degrees include Master of Architecture (MArch), Master of Science (SM), Master of Applied Science (MASc), Master of Business Administration (MBA), Master of Business Analytics (MBAn), Master in City Planning (MCP), Master of Engineering (MEng), Master of Finance (MFin), Engineer, Doctor of Philosophy (PhD), and Doctor of Science (ScD). Graduate students may also take advantage of a number of standing interdisciplinary graduate programs or develop individually tailored programs in consultation with the faculty.

Engineer degrees include Civil Engineer (CE), Electrical Engineer (EE), Engineer in Aeronautics and Astronautics (EAA), Engineer in Computer Science (ECS), Environmental Engineer (EnvE), Materials Engineer (MatE), Mechanical Engineer (MechE), Naval Engineer (NavE), and Nuclear Engineer (NuclE).

Each of the academic departments and units listed below offers one or more degree-granting programs, as described in the Schools and College section of this Bulletin (additional degree-granting programs are described in the Interdisciplinary Programs section ). More detailed information can be obtained from the program and department offices.

Architecture Media Arts and Sciences Urban Studies and Planning

Aeronautics and Astronautics Biological Engineering Chemical Engineering Civil and Environmental Engineering Electrical Engineering and Computer Science (joint with the MIT Schwarzman College of Computing) Materials Science and Engineering Mechanical Engineering Nuclear Science and Engineering Institute for Medical Engineering and Science

Anthropology Comparative Media Studies/Writing Economics Global Languages History Humanities Linguistics and Philosophy Literature Music and Theater Arts Political Science Science, Technology, and Society

Biology Brain and Cognitive Sciences Chemistry Earth, Atmospheric, and Planetary Sciences Mathematics Physics

MIT Stephen A. Schwarzman College of Computing

Electrical Engineering and Computer Science (joint with the School of Engineering) Institute for Data, Systems, and Society

Accreditation

MIT is accredited by the New England Commission of Higher Education (formerly the Commission on Institutions of Higher Education of the New England Association of Schools and Colleges, Inc.).

Inquiries regarding MIT's accreditation status by the Commission should be directed to [email protected] . Individuals may also contact:

New England Commission of Higher Education 3 Burlington Woods Drive, Suite 100 Burlington, MA 01803-4514 telephone: 781-425-7785 email: [email protected]

Many degree programs at MIT are accredited by specialized professional accrediting bodies, including ABET, the Association to Advance Collegiate Schools of Business, the American Chemical Society, the National Architectural Accrediting Board, and the Planning Accreditation Board. Academic departments can provide information on the accreditation of the specific degree programs they offer.

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