# 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 worksheets##### STAT 31100: Numerical PDEs III

###### TA

Graduate-level numerical PDE course.

Intro to MATLAB (Slides) adv2d.m laplacian2d.m##### STAT 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 table##### UC 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