Interdisciplinary PhD in Physics and Statistics
Requirements:
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 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 12 page 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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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: B+ in all required courses (see options below)
Students must complete their primary program’s degree requirements along with the IDPS requirements. Students must maintain a 4.5 MIT graduate GPA. Statistics requirements must not unreasonably impact performance or progress in a student’s primary degree program.
IDPS/Physics CoChairs: 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 NonAsymptotic 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 