Teaching & Mentorship

classes and research mentorship

University of California, Berkeley

Algorithmic Foundations of Human-Robot Interaction
Spring 2021: Graduate Student Instructor

    CS 287H is a graduate-level introduction to algorithmic HRI taught by Prof. Anca Dragan. The course combines lectures with paper presentations by students, encouraging both fundamental knowledge acquisition as well as open-ended discussions. As the only teaching assistant, I had an opportunity to directly influence the course: I created weekly quizzes, developed homework assignments that allowed students to implement the concepts we discussed as an HRI researcher would, brainstormed and provided feedback on project proposals, graded all materials in the course, and led some of the lectures. I also guest lectured both in 2020 (slides here) and 2021. Course materials can be found here.

Introduction to Artificial Intelligence
Fall 2019: Graduate Student Instructor

    CS 188 is an upper-division introduction to artificial intelligence taught by Prof. Anca Dragan. Throughout the course, I covered concepts ranging from search algorithms, game trees, Markov decision processes, reinforcement learning, probabilistic graphical modeling, and machine learning. I held regular office hours, designed homework and exams, and led weekly one-hour discussion sections. Course materials can be found here.

Massachusetts Institute of Technology

Introduction to Software Engineering in Java
Winter 2016: Instructor and Lecturer

    6.178 is a month-long course on software engineering in Java organized during the Independent Activities Period (IAP). I co-organized and taught the course, held regular office hours, and designed and graded homework. Course materials can be found here.

Design and Analysis of Algorithms
2015-2017: Tutor

    6.046 is an intermediate undergraduate course teaching algorithm design and analysis concepts. After I took the course, I continued tutoring active students every semester through TBP's tutoring program.

Introduction to Electrical Engineering and Computer Science
Spring 2014: Student Lab Assistant

    In the introductory course to EECS taught by Adam Hartz, students learn about programming, signals and systems, circuits, probability, search, and planning by programming a robot to complete challenges in weekly design labs. As a student lab assistant, I would test and complete the design labs early, then mentor my peers as they worked through the weekly labs themselves.

Research Mentorship

Master's Students
  • Yi Liu (EECS at UC Berkeley)
    Research on learning rewards by first learning task-agnostic representations from human input (2021-present).
  • Arjun Sripathy (now ML scientist at Tesla)
    Research on meta-planning with a fleet of human models (2020-2021), and learning representations for expressive motions using human input (2021-2022).
Undergraduate Students
  • Regina Wang (now Master's student at Stanford University)
    Research on robot reward learning from multiple types of human input (2021-present).
  • David Zhang (EECS at UC Berkeley)
    Research on a more efficient interface for learning rewards from human input (2021-present).
  • Matthew Zurek (now PhD student at the University of Wisconsin–Madison)
    Research on confidence-aware shared autonomy (2020-2021).
  • Sampada Deglurkar (now PhD student at UC Berkeley)
    Research on confidence-aware learning from human input (2018-2019).