Much of my what has gone “right” in my life can be traced back to exceptional teaching, both inside and outside of the classroom. I care deeply about passing on what I’ve learned in order to help make CS and machine learning (and related endeavors) more approachable fields for those aspiring to be both traditional computer scientists and “non-computer” scientists.
Teaching Assistant, CS 159 (Adv. Topics in ML: Uncertainty Quantification): [Caltech] [Spring 2023]. TA for Caltech’s advanced topics in ML course (rotates topics each spring), this year on Uncertainty Quantification. Helped with course organization and curriculum design, gave lecture, wrote homework assignment, held office hours, advised and supported students on their own independent mini-research projects on UQ-adjacent topics. [Lecture slides on MCMC and (deep) uncertainty models]
Head TA, CS 156b (Learning Systems), ~80 students: [Caltech] [Spring 2022, 2023]. TA for Caltech’s main machine learning project course, in which students apply machine learning to a dataset in teams over the course of a term. Help organize course and competition, choose and setup new dataset, and hold technical office hours. As Head TA Spring 2023, hired other TAs and delegated responsibilities.
Head TA, CS 2 (Data Structures and Algorithms): [Caltech] [Winter 2022, 2023]. Head TA for CS 2!! Involved heavily in course organization and hiring/training/supporting/managing 18 other TAs. Pioneered new office hours, ticketing system to improve learning outcomes for large core CS class. Also helped organize and support “bridge-group”, a DEI initiative to help disadvantaged students with the transition from CS1 to CS2 (poster at RESPECT 2022). See below for general description.
Teaching Assistant, CS 156a (Learning Systems), ~200 students: [Caltech] [Fall 2021, 2022]. TA for Caltech’s introductory machine learning course, focusing on conceptual/mathematical background for common methods as well as basic implementations. Hold office hours.
Teaching Assistant, FSRI Intro CS, ~50 students: [Caltech] [Summer 2022] Summer-before-college course for incoming freshman from disadvantaged backgrounds. Helped develop assignments (including an NLP essay scorer and an autotuner!), held daily office hours, advised students on creative capstone projects. Preparing submission for SIGCSE 2023. [Python]
Teaching Assistant, CS 24 (Computing Systems), ~100 students: [Caltech] [Fall 2021]. TA for core CS class on computing systems and low-level programming. Hold weekly office hours and help out with course organization. [C]
Teaching Assistant, CS 3 (Software Design), ~120 students: [Caltech] [Spring 2021]. TA for class in which students are introduced to software design principles/techniques and develop a significant code base & game. Hold weekly office hours and code reviews, lead lab section. Help with course vision and organization. [C]
Teaching Assistant, CS 2 (Data Structures and Algorithms), ~200 students: [Caltech] [Winter 2021: 4.85/5 student rating]. TA for second Caltech CS course, in which students code fundamental data structures and related algorithms (and learn to apply them!). Hold weekly office hours, lead lab section, help with lecture. Help with course vision and optimizing pair programming, learning in online setting. [Java]
Teaching Assistant, CS 1 (Intro Programming), ~220 students: [Caltech] [Fall 2020: 4.94/5 student rating]. TA for the first CS course that students take at Caltech. Hold weekly office hours during busiest times and grade assignments. [Python]
Dean’s Tutor: [Caltech] [2020-2023]. Tutor for the following Caltech courses: Ma1abc, Ph1abc, Ch1ab, CS21.
Web Scraping Tutorials: [Caltech CS Education (CS 42)] [Summer 2020]. Wrote, got professor/peer feedback on, and revised tutorials as a part of CS Education course. Part 1: Where is my data, and how can I access it? covers the basics of scraping, web data (HTML), inspecting, and requests. Part 2: But how do I extract the data I want? covers string parsing, BeautifulSoup (HTML parsing), and wget (downloading data files from cmd line). [Python] [Jupyter Notebook] [Bash]
Teacher, Fundamentals of Investing: [Wave Learning Festival] [Summer 2020: 4.5/5 student rating]. Taught 1100+ students (ranging from middle school to undergraduate) over the course of 4 waves (2-3 week long sessions) and received overwhelmingly positive feedback. Slides available with notes covering the basics of investing, the stock market, indicators, and options.
Miscellaneous CS teaching artifacts: