Interdisciplinary PhD in Social & Engineering Systems and Statistics

Requirements:
Students must complete their primary program’s degree requirements along with the IDPS requirements. Statistics requirements must not unreasonably impact performance or progress in a student’s primary degree program.

Grade Requirements:  B+ in all required courses (see options below)

PhD Earned on Completion: Social and Engineering Systems and Statistics

IDPS/SES ChairAli Jadbabaie

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)
6.7220 (6.252/15.084) Nonlinear Optimization
6.7810 (6.438) Algorithms for Inference
6.7900 (6.867) Machine Learning
9.520 Statistical Learning Theory and Applications
14.380/14.381 Statistical Methods in Economics/Estimation and Inference for Linear Causal and Structural Models
14.382 Econometrics
15.077 Statistical Learning and Data Mining
16.391 Statistics for Engineers and Scientists
17.802 Quantitative Research Methods II:  Casual Inference
17.804 Quantitative Research Methods III:  Generalized Linear Models and Extensions
17.806 Quantitative Research Methods IV:  Advanced Topics
Data Analysis (pick one)
6.8300 (6.869) Advances in Computer Vision
9.073/HST.460 Statistics for Neuroscience Research
9.272/HST.576 Topics in Neural Signal Processing
6.8800 (6.555, 16.456/HST.582) Biomedical Signal and Image Processing
18.367 Waves and Imaging
6.3732 (IDS.131/6.439) Statistics, Computation and Applications
IDS.957 Practical Experience in Data Analysis

MIT Statistics + Data Science Center
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139-4307
617-253-1764