## Graduate School Coursework

I maintain a list of courses I’ve taken in the time I’ve spent in graduate school at UC Berkeley. For each course listed, names of instructors are given in parentheses, wherever appropriate.

### Before Graduate School

*Before starting graduate school officially, I completed a small number of
graduate courses during my undergraduate years at UC Berkeley. Many of these
courses were foundational in helping me find my way into and through the
Biostatistics PhD program. Here is a list of those courses:*

*Statistics 245A:*Modern Biostatistical Theory and Practice (Alan Hubbard and Mark van der Laan)*Statistics 245B:*Biostatistical Methods: Survival Analysis and Causality (Mark van der Laan)*Statistics 245D:*Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine (Sandrine Dudoit)*Statistics 247C:*Longitudinal Data Analysis (Nicholas Jewell)*Statistics 259:*Reproducible and Collaborative Statistical Data Science (Jarrod Millman)*Public Health 245:*Introduction to Multivariate Statistics (Lexin Li)*Public Health 290:*Big Data - A Public Health Perspective (Lexin Li) [pilot offering]

### Fall 2016

*Statistics 210A:*Theoretical Statistics (Will Fithian)*Statistics 215A:*Statistical Models: Theory and Application (Philip Stark)*Statistics 278B:*Causal Inference in High Dimensions (Peng Ding, Avi Feller, and Will Fithian) [auditing]*Public Health 295:*Targeted Learning in Biomedical Big Data (Mark van der Laan) [pilot offering]

### Spring 2017

*Statistics 210B:*Theoretical Statistics (Michael Jordan)*Statistics 245F:*Statistical Genomics (Sandrine Dudoit)*Statistics 298:*Causal Inference with Interference (Peng Ding, Avi Feller, Will Fithian, and Alex D’Amour) [auditing]*Public Health 252D:*Introduction to Causal Inference (Maya Petersen)

### Fall 2017

*Computer Science 294:*Deep Time-Series Learning with Finance Applications (Laurent El Ghaoui and Francois Belletti)*Computer Science 294:*Fairness in Machine Learning (Moritz Hardt)*Statistics 245B:*Biostatistical Methods: Survival Analysis and Causality (Mark van der Laan) [auditing]*Public Health 290:*Biomedical Big Data Training Program Seminar (Mark van der Laan and Alan Hubbard)*Public Health 295:*Infectious Disease Modeling Seminar (John Marshall)

### Spring 2018

*Computational Biology 293:*Doctoral Seminar in Computational Biology (Nir Yosef)*Statistics 260:*Observational Study Design and Causal Inference (Sam Pimentel)*Public Health 290:*Biomedical Big Data Training Program Capstone (Mark van der Laan and Alan Hubbard)

### Fall 2018

*Computational Biology 293:*Doctoral Seminar in Computational Biology (Priya Moorjani and Daniel Rokhsar)*Computer Science 294:*Machine Learning and Statistics Meet Biology (Jennifer Listgarten)*Statistics 232:*Experimental Design (Sam Pimentel)*Public Health 243D:*Adaptive Designs (Mark van der Laan)

### Spring 2019

*Statistics 210B:*Theoretical Statistics (Michael Jordan) [auditing]*Statistics 215B:*Statistical Models: Theory and Application (Jon McAuliffe)*Statistics 260:*Probabilistic Modeling in Genomics (Yun Song)*Statistics 298:*Semiparametric Approaches in Causal Inference (Peng Ding, Avi Feller, Will Fithian, and Sam Pimentel)*Public Health 290:*Current Topics in Causal Inference (Maya Petersen)

### Fall 2019

*Computer Science 285:*Deep Reinforcement Learning, Decision Making and Control (Sergey Levine)*Statistics 260:*Robust Statistics in High Dimensions (Jacob Steinhardt)*Statistics 298:*Causal Inference and Time (Peng Ding, Avi Feller, Will Fithian, and Sam Pimentel)