My teaching interests span undergraduate and graduate applied mathematics, including calculus, linear algebra, differential equations, numerical methods, optimization, mathematical modeling, data science, machine learning, and algebraic/geometric methods in applied mathematics.
In addition to standard courses in the applied mathematics curriculum, I am interested in designing modern topic courses that connect algebra, geometry, optimization, computation, machine learning, and control.
Teaching Experience
AM3 -Precalculus for the Social Sciences -Fall'25 (UCSC).
AM30-Multivariable Calculus for Engineers-Winter'26, Winter '25, Spring'25 (UCSC)
Math 2550-Intro. to Multi. Calculus -Spring 2024, Spring 2023 (Georgia Tech)
Math 2552-Differential Equations-Fall 2023, Fall 2022 (Georgia Tech)
Math 4100/7100- Differential Equations-Fall 2019, Spring 2020, Fall 2020 (Univ. of Missouri, Columbia)
Math 4720-Introduction to Abstract Algebra-Spring 2021 (Univ. of Missouri, Columbia)
Math 1700-Calculus 2-Fall 2021 (Univ. of Missouri, Columbia)
Proposed/Designed Courses
I have also designed several advanced undergraduate and graduate-level topic courses aligned with my research interests in applied algebraic geometry, optimization, scientific machine learning, and data-driven control such as:
• Algebraic Techniques and Semidefinite Programming in Data Science
• Geometric and Combinatorial Methods for Machine Learning
• Matrix Computation
• Algebraic and Geometric Methods in Optimization
• Tropical Geometry using R and Python
• Polynomial Paradigms in Algorithms, Combinatorics, and Optimization
• Machine Learning, Control Theory, and Data-Driven Science
• Algebraic Techniques in Data-Driven Control