Teaching
I roughly started academic service during the junior year of my undergraduate studies at UC Berkeley. The positions I have held since then include but are not limited to: TA, course tutor, tutor at other places on campus, volunteer tutor/lecturer, and mentor for projects/advising.
- Note: For any solutions (or course material in general) you find on my teaching page, please email me or any referenced collaborator/lecturer before use. If you are a current student, please refer to our institution’s policy on academic honesty. Any comments on improvements/errata are also greatly appreciated.
CAAM 31020: Mathematical Computation IIB: Nonlinear Optimization
TA
Graduate-level course in numerical optimization.
MATH 21100: Basic Numerical Analysis
TA
First course in the numerical analysis sequence. I wrote solutions to homework assignments and discussion session problems.
Solutions to assignments Course worksheetsSTAT 31100: Numerical PDEs III
TA
Graduate-level numerical PDE course.
Intro to MATLAB (Slides) adv2d.m laplacian2d.mSTAT 31120: Numerical Methods for SDEs
Course Reader
Graduate-level course in numerical solutions to stochastic differential equations. I helped with this course during my first year.
Solution manual (on github)STAT 27700 / CMSC 25300: Mathematical Foundations of Machine Learning
TA
Taught weekly discussion sections, held office hours, answered questions on Ed, created teaching materials and homework, and proctored exams.
DATA 198: Applied Data Science for Research and Discovery
TA
This was an entirely student-run course in data science with the help of DSUS (the data science department). This course can be used to satisfy one of the elective requirements for data science major. I gave two guest lectures on a data analysis case and logistic regression.
MATH 128B: Numerical Analysis
Course Reader
The second course in Berkeley's numerical analysis sequence. I did this during my last semester at Berkeley; I had a lot of fun.
Solution manual (on github)MATH 104: Real Analysis
Course Reader
I wrote solutions and held office hours. This is upper-division undergraduate real analysis.
- (Fall 2019) Prof. Arun Sharma
- (Fall 2020) Prof. Ian Charlesworth
COMPSCI 61A: Structure and Interpretation of Computer Programs
Course Tutor
Official course tutoring for weekly problem sessions on introductory Python programming topics. Graded homework and exams, held office hours. See more details on the course website
COMPSCI 61BL: Data Structures
Course Tutor
A fast-paced version of CS 61B during the summer. I held office hours and tutoring sessions over Zoom every week. The tutoring sessions were usually me presenting some slides for 30 minutes, then students work on a prepared worksheet and ask questions. Below are some sample review materials, arranged by topic.
Sorting algorithms Sorting algorithms II Binary search trees Minimum spanning trees K-d trees and tries Heaps and hash tableUC Berkeley Student Learning Center
Tutor for Math and Stats
Hold weekly office hours on Zoom to answer questions in lower division math and stats courses:
- Math 1A, Math 1B: Calculus I, II
- Math 10A, Math 10B: Methods of Mathematics: Calculus, Statistics and Combinatorics
- Math 32: Precalculus
- Math 16A, Math 16B: Analytic Geometry and Calculus
- Math 53: Multivariable Calculus
- Math 54: Linear Algebra and Differential Equations