Tutoring

CSE 21 Mathematics for Algorithms and Systems Taught 3 times Tutor

Course Summary

CSE 21 covers mathematical concepts essential for modeling and analyzing algorithms and computer systems. Topics include counting techniques such as inclusion-exclusion, recursive counting, permutations and combinations, data representations, order notation, time complexities, loop invariants, recurrence relations, graphs, trees, and introductory probability for algorithm design and analysis.

Responsibilities

Led weekly office hour sessions for large groups of students to cover course content, homework questions, and exam prep
Held 1-on-1 review sessions to help students strengthen their understanding of course material
Graded weekly homework assignments and exams for courses with 100-200+ students, providing timely comments and feedback
Proctored midterm/final exams
Managed office hour sign-ups and disability accommodations using Excel

DSC 40A Theoretical Foundations of Data Science Taught 2 times Tutor

Course Summary

DSC 40A is the first course in a two-part sequence introducing the mathematical principles behind data science. It covers foundational machine learning ideas such as empirical risk minimization, optimization, regression, classification, and discrete probability, with emphasis on rigorous justification, mathematical communication, and creative problem-solving.

Responsibilities

Hosted office hours and discussion sections for groups of up to 15 students at a time
Graded weekly homework assignments and exams for 100-200+ students, providing timely comments and feedback
Proctored midterm/final exams
Contributed detailed explanations for Summer Session 2024 past midterm and final problems on the DSC 40A practice site.

CSE 151A Introduction to Supervised Machine Learning Taught 1 time Tutor

Course Summary

CSE 151A gives a broad introduction to machine learning, especially supervised learning. Topics include k-nearest neighbor classifiers, logistic regression, perceptrons, support vector machines, decision trees, ensemble methods, and neural networks, with focus on both the algorithms and the principles behind them.

Responsibilities

Led weekly office hour sessions for large groups of students to cover course content, homework questions, and exam prep
Graded weekly homework assignments and exams for 100-200+ students, providing timely comments and feedback
Proctored midterm/final exams

MATH 173A Optimization Methods for Data Science I Reader

Course Summary

MATH 173A introduces convexity, including convex sets, convex functions, hyperplane geometry, and support functions for convex sets. It also covers linear and quadratic programming, optimality conditions, duality, primal and dual forms of linear support vector machines, active-set methods, and interior methods.

Responsibilities

Graded weekly homework assignments and exams for 100-200+ students, providing timely comments and feedback
Reviewed answer keys to apply consistent partial-credit decisions

MATH 180B Introduction to Stochastic Processes I Reader

Course Summary

MATH 180B introduces stochastic processes through topics such as random vectors, multivariate densities, covariance matrices, the multivariate normal distribution, random walks, Poisson processes, and Markov chains.

Responsibilities

Graded weekly homework assignments and exams for 100-200+ students, providing timely comments and feedback
Reviewed answer keys to apply consistent partial-credit decisions