Teaching and Mentroing
Teaching
University of California, Davis
MAT 108 Introduction to Abstract Math, Fall 2022, Instructor
Class size: 109 undergraduate students
Student evaluation: 4.0/5.0
MAT 133 Mathematical Finance, Spring 2023, Instructor
Class size: 28 undergraduate students
Student evaluation: 4.2/5.0
MAT 170 Mathematics for Data Analytics and Decision Making, Spring 2023, Instructor
Class size: 35 undergraduate students
Student evaluation: 4.4/5.0
MAT 167 Applied Linear Algebra, Fall 2023, Instructor
Class size: 70 undergraduate students
Student evaluation: 4.0/5.0
MAT 180 Special Topics: Modeling and Analytics for Operations Research, Fall 2023, Instructor
Class size: 11 undergraduate students
Student evaluation: 4.3/5.0
MAT 108 Introduction to Abstract Math, Winter 2024, Instructor
Class size: 59 undergraduate students
Student evaluation: 4.4/5.0
MAT 170 Mathematics for Data Analytics and Decision Making, Spring 2024, Instructor
Class size: 39 undergraduate students
Student evaluation: 4.3/5.0
University of Michigan
IOE 202 Operations Engineering and Analytics , Fall 2021, Instructor
Selected for a mentored teaching opportunity to be responsible for a small class section
Class size: 12 undergraduate students
Student evaluation: 4.2/5.0
IOE 510 Linear Programming, Fall 2020, Graduate Student Instructor
Class size: 36 graduate students
Student evaluation: 4.8/5.0
Mentoring
Project: Matroid theory and optimization
co advised with Matthias Koeppe
Δ-modular matrices recently gain increasing interests in the analysis of optimization problems. They are integral matrices with pure combinatorial structure: the absolute value of the subdeterminants is bounded by a fixed constant Δ. In this project, we will start from the special case Δ=1, which is well-known as Totally-Unimodular (TU) matrices. We will learn Seymour’s decomposition theorem for TU matrices via matroid theory (which is an abstract theory of dependence capturing and generalizing properties from both linear algebra and graph theory), and learn how the TU property benefits integer optimization. Then we will learn recent results for bimodular (Δ=2) and beyond that. At the meantime, we will also research on the application of these structural properties, especially in the computational complexity of integer optimization with Δ-modular coefficient matrices. In this REU project, students will learn about the structure of Δ-modular matrices and explore the applications in combinatorics and optimization.
Products:
Moises Reyes Rivas, Determining sharp proximity bounds for low row rank and Δ-modularity
Javier Santillan, Incorporating an existing TU recognition algorithm into Sage