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 uptodate CV, current transcript, and a 12 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 CoChairs: Jesse Thaler and Michael Williams
Seminar  
IDS.190  Doctoral Seminar in Statistics 
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 NonAsymptotic 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 
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 
8.334  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 