Interdisciplinary PhD in Physics and Statistics


A full list of the requirements is also available on the Physics page:

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.

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.

Grade Requirements: Students must complete their primary program’s degree requirements along with the IDPS requirements. 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

IDPS/Physics Co-Chairs: Jesse Thaler and Michael Williams

IDS.190 Doctoral Seminar in Statistics
Probability (pick one)
6.436 Fundamentals of Probability
18.675 Theory of Probability
Statistics (pick one)
18.655 Mathematical Statistics
18.6501 Fundamentals of Statistics
IDS.160 Mathematical Statistics – A Non-Asymptotic Approach
Computation & Statistics (pick one)
6.438 Algorithms for Inference
6.867 Machine Learning
6.862 Applied Machine Learning
6.864 Advanced Natural Language Processing
6.866 Machine Vision
6.874 Computational Systems Biology: Deep Learning in the Life Sciences
6.883 Modeling with Machine Learning: From Algorithms to Applications
9.520 Statistical Learning Theory and Applications
16.940 Numerical Methods for Stochastic Modeling and Inference
18.337 Numerical Computing and Interactive Software
Data Analysis (pick one)
6.869 Advances in Computer Vision
8.334 Statistical Mechanics II
8.591 Systems Biology
8.592 Statistical Physics in Biology
8.371 Quantum Information Science
8.942 Cosmology
9.583 Functional MRI: Data Acquisition and Analysis
16.456 Biomedical Signal and Image Processing
18.367 Waves and Imaging
IDS.131 Statistics, Computation, and Applications