Interdisciplinary PhD in Physics and Statistics
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.
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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
Seminar | |
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 |
6.864 | Advanced Natural Language Processing |
6.874 | Computational Systems Biology: Deep Learning in the Life Sciences |
6.C01 | 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 |