Courses

• Courses Taught
  • AI Camp
• UCLA Undergraduate Courses (B.S. Data Theory)
  • Machine Learning & Data Science
  • Statistics
  • Mathematics
  • Computational Statistics & Programming

Courses Taught

AI Camp

• Computer Vision (Summer 2023) — Intro to Python programming, neural network architectures (CNNs, ViTs, YOLO), data collection and labeling, model training and evaluation (Pytorch, Weights and Biases), product development, and deployment onto a website.

• Natural Language Processing (Summer 2023) — Intro to Python programming, neural network architectures (LSTMs, Transformers), text processing with NLTK, data collection and labeling, model training and evaluation (Pytorch, Weights and Biases), product development, and deployment onto a website.

UCLA Undergraduate Courses (B.S. Data Theory)

Data Theory: the mathematical and statistical foundations of data science—a collaboration between the Departments of Statistics and Data Science and Mathematics.

Machine Learning & Data Science

• MATH 156: Machine Learning (Spring 2024) — Parametric and nonparametric probability distributions, curse of dimensionality, correlation analysis and dimensionality reduction, decision theory, data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models, and Markov random fields. Course Project

• MATH 118: Mathematical Methods for Data Theory (Fall 2023) — Matrix and tensor factorization, PageRank, linear programming, unconstrained and constrained optimization, integer optimization, dynamic programming, and stochastic optimization.

• STATS 101C: Introduction to Regression and Data Mining (Fall 2022) — Multiple regression, logistic regression, regression diagnostics, bootstrapping, lasso/ridge regularization, random forests, feature importance, and clustering. Course Project

• STATS M148: Experience in Data Science (Winter 2024) — Capstone course solving real data science problems for community- or campus-based clients. Worked in small groups to frame client questions, create mathematical models, analyze data, and report results. Course Project

• STATS C183: Statistical Models in Finance (Fall 2023) — Portfolio management, risk diversification, efficient frontier, single index model, capital asset pricing model (CAPM), beta of a stock, European and American options, Black-Scholes model, and binomial model.

Statistics

• STATS 100A: Introduction to Probability (Winter 2023) — Combinatorics, axioms of probability, conditional probability, Bayes rule, discrete and continuous random variables, joint distributions, covariance, correlation, moment generating functions, law of large numbers, central limit theorem, and simulation.

• STATS 100B: Introduction to Mathematical Statistics (Spring 2023) — Exponential families, moment generating functions, multivariate normal distribution, central limit theorem, chi-squared/t/F distributions, maximum likelihood estimation, Fisher information, sufficient statistics, Rao-Blackwell theorem, confidence intervals, and hypothesis testing.

• STATS 101A: Introduction to Data Analysis and Regression (Winter 2023) — Applied regression, general/generalized linear models, diagnostics, bootstrapping, variable selection, logistic/Poisson regression, kernel regression, and graphical analysis using R. Course Project

• STATS 101B: Introduction to Design and Analysis of Experiment (Spring 2023) — Experimental design, randomization, blocking, completely randomized design, ANOVA, multiple comparisons, power and sample size, and block designs.

Mathematics

• MATH 115A: Linear Algebra (Winter 2022) — Techniques of proof, abstract vector spaces, linear transformations, matrices, determinants, inner product spaces, and eigenvector theory.

• MATH 131A: Real Analysis (Fall 2022) — Foundations of real analysis; real numbers, point set topology in Euclidean space, functions, and continuity.

• MATH 164: Optimization (Fall 2023) — Optimality conditions for unconstrained and constrained problems, Lagrange and KKT multipliers, Newton’s and gradient methods, linear programming, simplex method, and duality theory.

• MATH 151A: Applied Numerical Methods (Spring 2023) — Numerical algorithms and analysis, solution of nonlinear equations, numerical differentiation, integration, interpolation, and direct methods for solving linear systems.

Computational Statistics & Programming

• STATS 102A: Introduction to Computational Statistics with R (Winter 2023) — Statistical graphics, root finding, simulation, randomization testing, and bootstrapping.

• STATS 102B: Introduction to Computation and Optimization (Spring 2024) — Vector/matrix computation, multivariate normal distribution, PCA, clustering, gradient-based optimization, EM algorithm, and dynamic programming.

• COM SCI 31: Introduction to Computer Science I (C++) (Spring 2021) — Data types, control structures, procedural and data abstraction, object-oriented programming, functions, recursion, arrays, strings, and pointers.

• STATS 20: Introduction to Statistical Programming with R (Spring 2022) — Data management, simple programming, and statistical graphics in R.

• STATS 21: Python and Other Technologies for Data Science (Fall 2022) — Python programming with NumPy, pandas, matplotlib, and scikit-learn for data processing, analysis, and machine learning. Jupyter notebooks and Git.