Interdisciplinary PhD in Social & Engineering Systems and Statistics

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

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 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