Teaching

Explores Markowitz portfolio theory and modern extensions — robust optimization, risk parity, sparse index tracking, and alternative risk measures — with strong emphasis on Python programming. [Syllabus] Uses the dedicated textbook Portfolio Optimization: Theory and Application (Cambridge University Press, 2025).

Introduces convex optimization theory and its application across FinTech — portfolio optimization, data cleaning, machine learning, and graph learning — with equal emphasis on mathematical foundations and Python/R implementation. [Syllabus]

Explores Markowitz portfolio theory and modern extensions in depth — robust methods, risk parity, index tracking, and high-order portfolios — with weekly R programming sessions and a live portfolio game throughout the semester.

Graduate-level convex optimization following a case-study approach. Covers convex theory, duality, algorithms, and nonconvex methods, with applications spanning signal processing, finance, machine learning, and big data drawn from recent top-journal papers.