# PhD in Physics, Statistics, and Data Science

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.”

## Selection

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 idss_academic_office@mit.edu.

## 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.

**SEMINAR****PROBABILITY****STATISTICS****COMP & STAT**- 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

- 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications
**DATA ANALYSIS**- 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

- 6.8300 (6.869) Advances in Computer Vision

## 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.

## Advising

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)

**Advisors:**

## FAQs

### 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.