I love to teach. At Berkeley, I was a teaching assistant for 5 semesters.

CS170: Efficient Algorithms and Intractable Problems (Spring 2018)

CS189: Machine Learning (Spring 2017, Fall 2017)

CS70: Discrete Mathematics and Probability (Spring 2016, Fall 2016)

I was also a mentor for Computer Science Mentors. Some resources I’ve written:

- Markov Chains
- Bayesian approach to decision boundaries, discussion 3
- Regression, vectorizing objectives, and gradients, discussion 5
- Solution to Spring 2016 Midterm Isocontour Question
- Solution to Spring 2016 Final k-d tree problem

Here are some notes I’ve written / plan to write at some point on reinforcement learning:

- Intro to Reinforcement Learning
- Imitation Learning
- Model-free Reinforcement Learning
- Model-based Reinforcement Learning
- A guide to guided policy search

I received the Berkeley Oustanding GSI award in Spring 2017.