Teaching

16.S948 is a graduate seminar that covers mathematical human models, reinforcement learning from human feedback, imitation learning, active learning, representation learning, human intent prediction, shared autonomy, cooperation algorithms, and safety in HRI.

16.410 is an intermediate introduction to autonomy and decision making, covering search algorithms, game trees, Markov decision processes, reinforcement learning, probabilistic graphical modeling, model-based reasoning, and machine learning.

CS 287H is a graduate-level introduction to algorithmic HRI that combines lectures with paper presentations by students, encouraging both fundamental knowledge acquisition and open-ended discussions. As a TA, I created weekly quizzes, developed hands-on homework programming assignments, brainstormed and provided feedback on project proposals, graded all materials in the course, and led some of the lectures.

CS 188 is an upper-division introduction to AI covering search algorithms, game trees, Markov decision processes, reinforcement learning, probabilistic graphical modeling, and machine learning. As a TA, I held regular office hours, designed homework and exams, and led weekly one-hour discussion sections.