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TEACHING

Teaching Assistant @ Duke

Fall 2019 & Spring 2020     STA 101 - Data Analysis and Statistical Inference, prof. C. W. Hilton

Spring 2021                        STA 240L - Probability for Statistical Inference, Modeling, and Data Analysis, prof. G. Reeves

Fall 2021                             STA 521L - Predictive Modeling and Statistical Learning, prof. Y. Chen

Fall                                        COMP64101 - Reasoning and Learning under Uncertainty, w/ profs. Alvarez and Strungaru (slides)

Fall                                        COMP30040 - Third Year Project Laboratory

Spring                                   COMP64202 - Reinforcement Learning, w/ profs. Pan and Sun

Guest Lecture                       COMP34312 - Mathematical Topics in Machine Learning, w/ profs. Brown and Mukherjee (folder)            

Main Instructor @ The University of Manchester

Great Advice to Students of all Levels in STEM by Prof. Chatterjee

A Reflection by Prof. Kosorok on the Future of Statistics and Probability

Research Strategies [1, 2] Suggested by Profs. Littlewood and Mac Lane to Scientists of all Levels

A Poem by Prof. Moffatt on the Importance of Taking a Break

Do Not Despair When Your Favorite Course is Over! 

PhD Students and Postdocs Supervised

Fanyi Wu (Conformal Prediction, co-advised with Prof. Sami Kaski)

Zihan Yu (Optimal Transport)

Amr Mousa (Reinforcement Learning, co-advised with Prof. Richard Allmendinger)

Haozhuo Zhang (Robot Learning, co-advised with Prof. Wei Pan)

Raghad Alamri (Bias/Variance Decomposition, co-advised with Prof. Gavin Brown)

Roubing Tang (Bayesian Experimental Design, co-advised with Prof. Sami Kaski)

Geoffrey Kimani (Weather Predictions, co-advised with Prof. David Schultz and Dr. Eloisa Bentivegna)

Gokula Karthik Arumugam (Causal Inference, co-advised with Prof. Gavin Brown)

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