Coursework | Jongmin Mun

Trustworthy Machine Learning from an Optimization Lens

Optimization techniques for modern considerations such as privacy, robustness and fairness in machine learning.

Privacy-Preserving Machine Learning

Foundations of privacy-preserving machine learning, focusing on maximizing utility while protecting individual privacy against modern attacks.

Causal Inference with Modern Machine Learning Methods

DSO 603 USC Marshall Graduate

Doctoral-level introduction to causal inference at the intersection of machine learning, focusing on theoretical foundations and recent research developments.

Advanced Data Science: Modeling, Computing, & Optimization

DSO 699 USC Marshall Graduate TBD Jacob Bien

Ph.D. level course on statistical machine learning, covering the core process from data to model, algorithm, and insight.

Optimization for the Information and Data Sciences

EE 588 USC Viterbi Graduate

Graduate level course on convex optimization, first-order algorithms, and their applications in data science.

Investment Strategies and Portfolio Management

Investment USC Marshall Graduate

An overview of investment strategies, focusing on asset allocation, portfolio management, and the unique approaches of institutional investors like insurance companies.

Applied Generative AI for Enterprises

ISE-547 USC Viterbi Graduate

Master's level course on Applied Generative Artificial Intelligence for Enterprises at USC.

Graduate Mathematical Statistics

Math 541A/B, STA6010 USC, Yonsei University Graduate

Foundational sequences in graduate mathematical statistics covering probability, estimation, hypothesis testing, and asymptotics.

Statistical Computing for Data Science II

STA6172 Yonsei University Graduate

Advanced Monte Carlo statistical methods for Bayesian data analysis and data science.

High-Dimensional Statistics and Modern Inference

DSO 607 USC Marshall Graduate

Doctoral-level course on high-dimensional statistical inference covering penalized regression, knockoffs, random matrix theory, network inference, nonparametric methods, and agentic AI applications.

Generalized Linear Mixed Models

STA6640 Yonsei University Graduate

Theory and data analysis methods for Generalized Linear Mixed Models (GLMM), extending regression to exponential families and random effects.

Machine Learning for/with Mixed-Integer Optimization

ISE 617 / CSCI 617 USC Viterbi Graduate (Doctoral) Spring 2026

Ph.D. level course exploring the intersection of Machine Learning and Mixed-Integer Optimization, covering how MIO enhances ML and how ML improves MIO solvers.

Modern Statistical Inference

DSO 699 USC Marshall Graduate Fall 2023 Adel Javanmard

Ph.D. level course on Modern Statistical Inference, focusing on high-dimensional settings and statistical learning theory.