I have been fortunate to work as a teaching assistant (TA) several times during my undergrad, Ph.D. and Postdoc. My role as a TA in the class varied a fair bit across the different classes. The title for TA varies from school to school. At IIT Bombay, I worked as an undergraduate teaching assistant (UG-TA), at UC Berkeley as a graduate student instructor (GSI), and at Harvard as a teaching fellow (TF).

Teaching inspires research

My teaching experiences have contributed to the beginning or progression of several research projects. My experience with EECS 189 at Berkeley inspired me to develop a better understanding of Expectation maximization algorithms starting a research thread that lasted for two years. The experience with STAT 154 led me to dive deeper into kernel methods, and is continuing till date. Finally, STAT 234 at Harvard has excited me about various questions at the intersection of reinforcement learning, causal inference and mobile health and I am very excited to explore these areas in the near future.

Harvard University

At Harvard, I have been a TF for one graduate-level course on reinforcement learning.

Stat 234: Sequential Decision Making, Spring 2022: I taught this graduate-level class under Prof. Susan Murphy (who is also one of my two amazing postdoc advisors) along with another TF Eura Shin. Besides the usual duties, I primarily spent time on the guest lectures on off-policy reinforcement learning (one lecture), and Bayesian regret analysis of posterior sampling (3 lectures) — the latter lecture series was called ‘‘the regret of the posterior trilogy’’ and the three parts were titled (i) The fellowship of Bayesian regret, (ii) the two Bellman operators, and (iii) the return of the martingale. In the second half of the class, a significant amount of time was spent on mentoring several teams with their half-semester long course projects, which involved multiple rounds of proposal submission, a poster session and a final report.
Feedback from the class is available here: Entire class + Section

UC Berkeley

At Berkeley, I was a full-time GSI for two upper-division undergraduate courses on machine learning, one in computer science, and one in statistics.

EECS 189: Introduction to Machine Learning, Spring 2018 (350+ students): I taught this upper-division undergraduate class under Prof. Jennifer Listgarten and Prof. Anant Sahai and a team of 20 graduate and undergraduate student instructors. I was one of the head GSIs. My primary contributions were developing content for the class and ensuring smooth coordination across the content development team. Besides supervising the team's coordination, I worked on designing some new problems, touching-up some old problems, occasionally interacting with the students in teaching sections, conducting office hours, and attending homework parties.
Feedback from the class is available here.

STAT 154: Modern Statistical Prediction and Machine Learning, Spring 2019 (150+ students): I taught this upper-division undergraduate class under Prof. Bin Yu (who was also one of my two super cool Ph.D. advisors). For this class, my role was much more involved than a usual TA. Yuansi Chen and I were the two GSIs who handled the load of 150 undergraduate students, besides a serious contribution in re-designing of the class. The two of us not only designed the bi-weekly homework, weekly discussion, and exam content from scratch, we also developed two mini-projects based on real-data to facilitate a real hands-on experience for the students. I was later awarded the outstanding GSI award for my contributions to this class.
Feedback from the class is available here: Entire class, Section 1 and Section 2.

IIT Bombay

At my undergrad, I worked as a UG-TA in total 9 times for 5 different institute-level UG math and physics courses. The duties for these classes included weakly teaching sections of 40-50 students ( class of size ~880 split into 20 sections) and evaluating the exams. I used to conduct several extra sessions where the attendance would often exceed 200 (~ 25% of the students in the first year).

1. MA 105: Calculus (Fall 2011, Fall 2012); Feedback*: Scores, Comments

2. MA 106: Linear Algebra (Spring 2012, Spring 2013, Spring 2014)

3. MA 108: Ordinary Differential Equations (Spring 2012, Spring 2013)

4. MA 207: Partial Differential Equations (Summer 2012)

5. PH 103: Electromagnetism (Fall 2011); Feedback*: Scores, Comments

In addition, I also worked as a teaching assistant for an online course on Linear Algebra with Prof. Neela Nataraj for 400+ undergraduate colleges, organized by the Ministry of Human Resource Development of the Government of India.

*Feedback is based on data collected by me during my first teaching experience at IIT Bombay in Fall 2011 (maintaining the anonymity of the students). There was no formal review system at IIT Bombay for TAs during my undergrad.