Teaching

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.