Requirements:
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
PhD Earned on Completion: Physics, Statistics, and Data Science
IDPS/Physics Co-Chairs: Jesse Thaler and Michael Williams
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 | |
| IDS.190 | Doctoral Seminar in Statistics – *only available in Fall* |
| Probability (pick one) | |
| 6.7700 (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) | |
| 6.7810 (6.438) | Algorithms for Inference |
| 6.7900 (6.867) | Machine Learning |
| 6.8610 (6.864) | Advanced Natural Language Processing |
| 6.8710 (6.874) | Computational Systems Biology: Deep Learning in the Life Sciences |
| 6.C01 | Modeling with Machine Learning: From Algorithms to Applications |
| 2.C01/2.C51 | Machine Learning for Physical Systems (to be taken consecutively after 6.C01/6.C51) |
| 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) | |
| 6.8300 (6.869) | Advances in Computer Vision |
| 8.316 | Data Science in Physics |
| 8.308 (8.S308) | Statistical Physics |
| 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 |