Optimizing Gradient Bounds of Torsion Functions Among Various Shapes
scholarship.claremont.edu ↗Applied Statistics & Data Science · UCLA
Mathematics, statistics, and data science.
I'm Mark Z. Wang — a graduate student in Applied Statistics & Data Science at UCLA and an Operations Research Analyst at the United States Space Force, with research interests in constrained optimization and an industry focus on quantitative finance.
About
Research at the intersection of math and data.
I am a graduate student in the Statistics & Data Science Department at UCLA (intended graduation: December 2026) and an Operations Research Analyst at the United States Space Force. I received my B.A. in Honors Mathematics from Pitzer College in May 2025.
My research focuses on numerical methods and data science applied to partial differential equations (PDEs), with an emphasis on constrained optimization. Current projects span plasma physics for nuclear fusion, epidemiological modeling of environmentally transmitted diseases, and quantitative finance.
Research
Publications & Presentations
Publications
Using mathematical modeling to study the dynamics of Legionnaires' disease and consider management options
10.3934/mbe.2025045 ↗Presentations
Optimizing Gradient Bounds for the Torsion Function Among Various Shapes — poster
Honors Senior Thesis in Mathematics
Optimizing Gradient Bounds for the Torsion Function Among Various Shapes — poster
Optimizing Gradient Bounds for the Torsion Function Among Various Shapes
Understanding the Dynamics of Legionnaires’ Disease Through Mathematical Modeling and Management Options
Using Mathematical Modeling to Study the Dynamics of Legionnaires’ Disease and Consider Management Options — poster
Modeling the Dynamics of Environmentally Transmitted Diseases
Extensions of the j-function to the real boundary of the upper half plane
Toolkit
Subject expertise
Python
MATLAB
R
Mathematical Modeling
Machine Learning
Reinforcement Learning
Ordinary & Partial Differential Equations
Constrained Optimization
Numerical Methods
Operations Research
Probability & Statistical Theory
Stochastic Processes
Bayesian & Statistical Inference
Linear Algebra
Calculus
Contact