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PhD in Physics, Statistics, and Data Science

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Many PhD students in the MIT Physics Department incorporate probability, statistics, computation, and data analysis into their research. These techniques are becoming increasingly important for both experimental and theoretical Physics research, with ever-growing datasets, more sophisticated physics simulations, and the development of cutting-edge machine learning tools. The Interdisciplinary Doctoral Program in Statistics (IDPS)  is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.

Admission to this program is restricted to students currently enrolled in the Physics doctoral program or another participating MIT doctoral program. In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods. Graduates of the program will receive their doctoral degree in the field of “Physics, Statistics, and Data Science.”

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

For access to the selection form or for further information, please contact the IDSS Academic Office at  [email protected] .

Required Courses

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

• IDS.190 – Doctoral Seminar in Statistics and Data Science ( may be substituted by IDS.955 Practical Experience in Data, Systems and Society )
• 6.7700[J] Fundamentals of Probability or
• 18.675 – Theory of Probability
• 18.655 – Mathematical Statistics or
• 18.6501 – Fundamentals of Statistics or
• IDS.160[J] – Mathematical Statistics: A Non-Asymptotic Approach
• 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications or
• 6.7810 Algorithms for Inference or
• 6.8610 (6.864) Advanced Natural Language Processing or
• 6.7900 (6.867) Machine Learning or
• 6.8710 (6.874) Computational Systems Biology: Deep Learning in the Life Sciences or
• 9.520[J] – Statistical Learning Theory and Applications or
• 16.940 – Numerical Methods for Stochastic Modeling and Inference or
• 18.337 – Numerical Computing and Interactive Software
• 8.316 – Data Science in Physics or
• 6.8300 (6.869) Advances in Computer Vision or
• 8.334 – Statistical Mechanics II or
• 8.371[J] – Quantum Information Science or
• 8.591[J] – Systems Biology or
• 8.592[J] – Statistical Physics in Biology or
• 8.942 – Cosmology or
• 9.583 – Functional MRI: Data Acquisition and Analysis or
• 16.456[J] – Biomedical Signal and Image Processing or
• 18.367 – Waves and Imaging or
• IDS.131[J] – Statistics, Computation, and Applications

Grade Policy

C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated.

Unless approved by the PhysSDS co-chairs, a minimum grade of B+ is required in all 12 unit courses, except IDS.190 (3 units) which requires a P grade.

Though not required, it is strongly encouraged for a member of the MIT  Statistics and Data Science Center (SDSC)  to serve on a student’s doctoral committee. This could be an SDSC member from the Physics department or from another field relevant to the proposed thesis research.

Thesis Proposal

All students must submit a thesis proposal using the standard Physics format. Dissertation research must involve the utilization of statistical methods in a substantial way.

PhysSDS Committee

• Jesse Thaler (co-chair)
• Mike Williams (co-chair)
• Isaac Chuang
• Janet Conrad
• William Detmold
• Philip Harris
• Jacqueline Hewitt
• Kiyoshi Masui
• Leonid Mirny
• Christoph Paus
• Phiala Shanahan
• Marin Soljačić
• Washington Taylor
• Max Tegmark

Can I satisfy the requirements with courses taken at Harvard?

Harvard CompSci 181 will count as the equivalent of MIT’s 6.867.  For the status of other courses, please contact the program co-chairs.

Can a course count both for the Physics degree requirements and the PhysSDS requirements?

Yes, this is possible, as long as the courses are already on the approved list of requirements. E.g. 8.592 can count as a breadth requirement for a NUPAX student as well as a Data Analysis requirement for the PhysSDS degree.

If I have previous experience in Probability and/or Statistics, can I test out of these requirements?

These courses are required by all of the IDPS degrees. They are meant to ensure that all students obtaining an IDPS degree share the same solid grounding in these fundamentals, and to help build a community of IDPS students across the various disciplines. Only in exceptional cases might it be possible to substitute more advanced courses in these areas.

Can I substitute a similar or more advanced course for the PhysSDS requirements?

Yes, this is possible for the “computation and statistics” and “data analysis” requirements, with permission of program co-chairs. Substitutions for the “probability” and “statistics” requirements will only be granted in exceptional cases.

For Spring 2021, the following course has been approved as a substitution for the “computation and statistics” requirement:   18.408 (Theoretical Foundations for Deep Learning) .

The following course has been approved as a substitution for the “data analysis” requirement:   6.481 (Introduction to Statistical Data Analysis) .

Can I apply for the PhysSDS degree in my last semester at MIT?

No, you must apply no later than your penultimate semester.

What does it mean to use statistical methods in a “substantial way” in one’s thesis?

The ideal case is that one’s thesis advances statistics research independent of the Physics applications. Advancing the use of statistical methods in one’s subfield of Physics would also qualify. Applying well-established statistical methods in one’s thesis could qualify, if the application is central to the Physics result. In all cases, we expect the student to demonstrate mastery of statistics and data science.

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IDSS students will address societal challenges by combining the fundamentals of statistics, data science, information and decision systems, as well as a rigorous study of social sciences with application domain areas. 

Doctoral Program in Social and Engineering Systems

For the next generation of researchers and practitioners addressing complex systems of societal importance, IDSS offers its signature program, the Doctoral Program in Social and Engineering Systems (SES) . Students receive a thorough preparation in information sciences and computation, statistics, systems, and decision sciences; they focus on a social science; and finally gain experience addressing a concrete societal challenge. Graduates of the program will advance the state of knowledge in theory or in practice, and will take their skills with them to transformative careers in their chosen field.

IDSS hosts the Technology and Policy Program (TPP), which has offered the Master of Science in Technology and Policy at MIT since 1976. TPP’s ongoing mission is to develop leaders who can create, refine, and implement responsible policies that are informed not only by an understanding of technology and its instruments, but also by their broad social contexts. Combining a core in science and engineering with studies in applied social sciences, TPP’s curriculum imparts strength in both a technical field and in the policy process. Moreover, each TPP student is required to complete a research thesis, supplying a significant research experience that equips graduates to be effective leaders in both the public and private sectors, or prepares them for further development of their research skills in a doctoral program.

Statistics and Data Science Programs

The need to analyze data in order to make informed decisions is fundamental to our society. Statistics is the science of making inferences and decisions from data under uncertainty. It is an essential tool for almost every quantitative field. As the home of MIT’s emerging statistics community, IDSS offers academic programs in statistics to MIT’s undergraduate and graduate students, and an online MicroMasters to learners around the world.

The Interdisciplinary PhD in Statistics (IDPS) is designed for students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st century statistics, using concepts of computation and data analysis as well as elements of classical statistics and probability within their chosen field of study.

Undergraduate Minor in Statistics & Data Science

Through six subjects, MIT’s new Minor in Statistics and Data Science will provide students with a working knowledge base in statistics, probability, and computation, and develop their ability to perform data analysis. This program begins September 2016.

MicroMasters program in Statistics and Data Science

The online MicroMasters program in Statistics and Data Science is comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization and will also accelerate your path towards an MIT PhD or a Master’s at other universities.

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6.434j statistics for engineers and scientists, 6.435 system identification ( 1 student review ), 6.437 inference and information ( 2 student reviews ), 6.438 algorithms for inference, 6.867 machine learning ( 2 student reviews ), 6.869 advances in computer vision, 6.870 advanced topics in computer vision, course 15 - management science, 15.034 metrics for managers: big data and better answers, 15.062j data mining: finding the data and models that create value, 15.068 statistical consulting, 15.074j statistical reasoning and data modeling ( 1 student review ), 15.077j statistical learning and data mining ( 1 student review ), 15.097 seminar in operations research and statistics, 15.450 analytics of finance, 15.460 analytics of finance ii, course 18 - mathematics, 18.338 eigenvalues of random matrices, 18.443 statistics for applications, 18.465 topics in statistics, 18.466 mathematical statistics ( 1 student review ), course 14 - economics, 14.381 statistical method in economics, 14.382 econometrics, 14.384 time series analysis ( 1 student review ), 14.385 nonlinear econometric analysis, 14.386 new econometric methods, 14.387 topics in applied econometrics, course 9 - brain and cognitive sciences, 9.073j statistics for neuroscience research, 9.272j topics in neural signal processing, 9.520 statistical learning theory and applications, course 1 - civil engineering, 1.151 probability and statistics in engineering, 1.202j demand modeling, course 12 - earth, atmospheric and planetary sciences, 12.515 data and models, 12.714 computational data analysis, course 16 - aeronautics and astronautics, 16.470j statistical methods in experimental design ( 1 student review ), course 22 - nuclear science and engineering, 22.38 probability and its applications to reliability, quality control, and risk assessment ( 1 student review ), course 7 - biology, 7.410 applied statistics, engineering systems division, esd.86j models, data and inference for socio-technical systems ( 1 student review ).

Doctoral Degrees

A doctoral degree requires the satisfactory completion of an approved program of advanced study and original research of high quality..

Please note that the Doctor of Philosophy (PhD) and Doctor of Science (ScD) degrees are awarded interchangeably by all departments in the School of Engineering and the School of Science, except in the fields of biology, cognitive science, neuroscience, medical engineering, and medical physics. This means that, excepting the departments outlined above, the coursework and expectations to earn a Doctor of Philosophy and for a Doctor of Science degree from these schools are generally the same. Doctoral students may choose which degree they wish to complete.

Applicants interested in graduate education should apply to the department or graduate program conducting research in the area of interest. Some departments require a doctoral candidate to take a “minor” program outside of the student’s principal field of study; if you wish to apply to one of these departments, please consider additional fields you may like to pursue.

Below is a list of programs and departments that offer doctoral-level degrees.

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Interdisciplinary Doctor of Philosophy in Statistics

Interdisciplinary Doctoral Program in Statistics

Interdisciplinary PhD in Statistics

Common core.

All students in the Interdisciplinary Doctoral Program in Statistics are required to complete the common core for a total of 27 units.

Program-specific Requirements

Each student must complete the requirements specified by their home department in the lists below by taking one subject from the Computation and Statistics category and one subject from the Data Analysis category.

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The PDF includes all information on this page and its related tabs. Subject (course) information includes any changes approved for the current academic year.

Understanding the process: Admissions statistics

We love data at MIT. Reliable data, properly contextualized , can help people understand complex systems and make informed decisions. So, a few years ago, we began publishing our own admissions statistics which went beyond the stats already contributed to the MIT’s standard  Common Data Set .

Holistic admissions

It is important to understand that these numbers do not  determine  our admissions process, but are the  result   of  our process. In our  holistic admissions process,  we consider quantifications like  test scores , but we also care deeply about factors like  your match with MIT . Qualitative characteristics like these are much harder to quantify and are therefore not included in the tables below despite their centrality to our process.

The most important thing to remember is that at MIT  we admit people, not numbers . With that in mind, here are some numbers about the people we admit.

Admissions statistics for the Class of 2027

See also  First-year class profile .

Early Action

Regular Action

U.S. citizens/permanent residents

International students

Middle 50% score range of admitted students (25th and 75th percentiles)

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Enrollment Statistics

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All students, October 2023

Undergraduate students (38%)

Graduate students (62%)

International students

In 2023–2024, MIT students come from all 50 states, the District of Columbia, four territories, and 136 foreign countries. Women accounted for 49% of undergraduates (2,231) and 40% of graduate students (2,969). Fifty-eight percent of undergraduates (2,650) and 22% of graduate students (1,617) self-identified as members of one or more US minority groups.

Undergraduates by School/College, 2023–2024

Note : Excludes 1,094 first-year students, five undesignated sophomores, and five special students. MIT students do not enroll in an academic department until the start of their sophomore year and may defer decision on a course of study until the end of that year. * Students in interdisciplinary programs are included in the totals of the school or college that administers the program. Students in joint programs with the College of Computing are included in the totals for Engineering, with the number of shared students in parentheses. See the Registrar’s enrollment reports for details.

Graduate Students by School/College, 2023–2024

* Students in interdisciplinary programs are included in the totals of the school or college that administers the program. Students in joint programs with the College of Computing are included in the totals for Engineering and Management (with the number of shared students in parentheses), but not in the Computing totals. See the Registrar’s enrollment reports for details. **Includes 185 students working on Harvard degrees only through the Harvard-MIT Health Sciences and Technology Program.

US Minority Group Representation among Students, 2023–2024

International Students

There are 3,478 international students enrolled in degree programs at MIT in 2023–2024: 501 undergraduates (11%) and 2,977 graduate students (41%). Additionally, 652 exchange, visiting, and special students participated in MIT programs.

International Students, by Region, 2023–2024

PhD Program

Year after year, our top-ranked PhD program sets the standard for graduate economics training across the country. Graduate students work closely with our world-class faculty to develop their own research and prepare to make impactful contributions to the field.

Our doctoral program enrolls 20-24 full-time students each year and students complete their degree in five to six years. Students undertake core coursework in microeconomic theory, macroeconomics, and econometrics, and are expected to complete two major and two minor fields in economics. Beyond the classroom, doctoral students work in close collaboration with faculty to develop their research capabilities, gaining hands-on experience in both theoretical and empirical projects.

How to apply

Students are admitted to the program once per year for entry in the fall. The online application opens on September 15 and closes on December 15.

Meet our students

Our PhD graduates go on to teach in leading economics departments, business schools, and schools of public policy, or pursue influential careers with organizations and businesses around the world. 

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

Operations Research and Statistics

Phd students.

Students interested in pursuing doctoral studies with members of the Operations Research and Statistics Group are encouraged to apply to the  Operations Research Center  or through another appropriate PhD program at MIT, such as  EECS ,  Math Department ,  LIDS , and  IDSS .

• Core Members
• Affiliate Members
• Interdisciplinary Doctoral Program in Statistics
• Minor in Statistics and Data Science
• MicroMasters program in Statistics and Data Science
• Data Science and Machine Learning: Making Data-Driven Decisions
• Norbert Wiener Fellowship
• Stochastics and Statistics Seminar
• IDSS Distinguished Seminars
• IDSS Special Seminar
• SDSC Special Events
• Online events
• IDS.190 Topics in Bayesian Modeling and Computation
• Past Events
• LIDS & Stats Tea

The online MicroMasters program in Statistics and Data Science is comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization and will also accelerate your path towards an MIT PhD or a Master’s at other universities.

About the program

Hear from learners

MIT Statistics + Data Science Center Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 617-253-1764

• Accessibility
• Interdisciplinary PhD in Aero/Astro and Statistics
• Interdisciplinary PhD in Brain and Cognitive Sciences and Statistics
• Interdisciplinary PhD in Economics and Statistics
• Interdisciplinary PhD in Mathematics and Statistics
• Interdisciplinary PhD in Mechanical Engineering and Statistics
• Interdisciplinary PhD in Physics and Statistics
• Interdisciplinary PhD in Political Science and Statistics
• Interdisciplinary PhD in Social & Engineering Systems and Statistics
• LIDS & Stats Tea
• Spring 2023
• Spring 2022
• Spring 2021
• Fall – Spring 2020
• Fall 2019 – IDS.190 – Topics in Bayesian Modeling and Computation
• Fall 2019 – Spring 2019
• Fall 2018 and earlier

• DACA/Undocumented
• First Generation, Low Income
• International Students
• Students of Color
• Students with disabilities
• Undergraduate Students
• Master’s Students
• PhD Students
• Faculty/Staff
• Family/Supporters
• Career Fairs
• Post jobs, internships, and fellowships
• Build your brand at MIT
• Recruiting Guidelines and Resources
• Connect with Us
• Career Advising
• Distinguished Fellowships
• Employer Relations
• Graduate Student Professional Development
• Prehealth Advising
• Student Leadership Opportunities
• Academia & Education
• Architecture, Planning, & Design
• Arts, Communications, & Media
• Business, Finance, & Fintech
• Computing & Computer Technology
• Data Science
• Energy, Environment, & Sustainability
• Life Sciences, Biotech, & Pharma
• Manufacturing & Transportation
• Health & Medical Professions
• Social Impact, Policy, & Law
• Getting Started & Handshake 101
• Exploring careers
• Networking & Informational Interviews
• Connecting with employers
• Resumes, cover letters, portfolios, & CVs
• Finding a Job or Internship

Post-Graduate and Summer Outcomes

• Professional Development Competencies
• Preparing for Graduate & Professional Schools
• Preparing for Medical / Health Profession Schools
• Interviewing
• New jobs & career transitions
• Career Prep and Development Programs
• Fall Career Fair
• Employer Events
• Outside Events for Career and Professional Development
• Events Calendar
• Career Services Workshop Requests
• Early Career Advisory Board
• Peer Career Advisors
• Student Staff
• Mission, Vision, Values and Diversity Commitments
• News and Reports

The following data resources are compiled by Career Advising and Professional Development, the MIT Registrar’s Office, and other groups to help students, recent alumni and postdocs. Students, recent alumni and postdocs should use the data to explore potential career paths, options for graduate school, prepare for interviewing, and negotiating job offers. Employers are encouraged to review the data to learn about MIT students interests post-graduation and to develop a recruiting strategy for the most success in hiring MIT talent.

Graduating Student Survey

Organized and administered by Career Advising & Professional Development and MIT’s Institutional Research. The MIT Graduate Student Survey (GSS) asks graduating Bachelor’s and Master’s students to share their post-graduation plans and related career issues data. The data includes information on average salaries of graduates, geographical distribution, companies who hire students, and the factors that influence students to accept job offers, among other things. The information contained in the interactive MIT Graduating Student Survey dashboard will increase your understanding of MIT graduates and help you hire them.

Earned Doctorate Survey

Organized and administered by Career Advising & Professional Development and MIT’s Institutional Research . Learn about MIT PhD destinations upon graduation, top industries for PhDs, salary information and much more. Explore IR’s interactive dashboard and CAPD’s supplemental PDF report to get insights and results.

Enrollment and Degree Statistics

Organized by MIT’s Registrar’s Office. This resource provides information on international, gender, and geographic breakdown of students as well as detailed information from the departments and majors across campus.

Summer Experience Survey

Organized and administered by Career Advising & Professional Development  and MIT’s Institutional Research. The Summer Experience Survey is helpful for students as they consider various options during the summer months and for employers who wish to hire MIT students for research experiences, internships and summer jobs.

Brian Trippe arrives as Assistant Professor of Statistics

A new appointment will join our department on July 1. Brian comes to us from a postdoctoral stint with the Columbia University Department of Statistics as well as a visiting researcher post with the University of Washington's Institute for Protein Design in Seattle. He earned his PhD in Computational and Systems Biology from MIT where he was a founding co-organizer of the Machine Learning for Protein Engineering Seminar Series.

Brian's recent research develops and applies statistical machine learning methods to solve challenges that arise in biotechnology and medicine. Through work on computational protein design over the past two years, he and his collaborators have synthesized hundreds of new molecules that have been subsequently validated in laboratory experiments. His future work aims to develop these machine learning methods to enable biotechnology solutions to challenges ranging from disease eradication to climate change-robust agriculture, with a long-term goal of building statistical foundations for data-driven genetic engineering.