During my Ph.D. at UT Austin, I pursued advanced coursework across mathematics, computer science, and engineering to build strong theoretical foundations in probability, optimization, numerical analysis, and inverse problems.

Probability and Learning Theory

• EE 381J Probability and Stochastic Processes (Prof. Gustavo de Veciana)
• EE 381V Online Learning (Prof. Sanjay Shakkottai)
• CS 388R Randomized Algorithms (Prof. Eric Price)
• CSE 382M Foundational Techniques of Machine Learning and Data Sciences (Prof. Rachel Ward)

Optimization and Large-Scale Computation

• EE 381V Large Scale Optimization (Prof. Constantine Caramanis)
• CSE 392 Parallel Algorithms in Scientific Computation (Prof. George Biros)

Functional Analysis

• M 383C Methods of Applied Mathematics I (Prof. Leszek Demkowicz)
• M 383D Methods of Applied Mathematics II (Prof. Todd Arbogast)
• CSE 386M Functional Analysis in Theoretical Mechanics (Prof. Leszek Demkowicz)

Numerical Analysis and PDEs

• M 387D Numerical Analysis: Differential Equations (Prof. Thomas Hughes)
• M 383E Numerical Analysis: Linear Algebra (Prof. George Biros)

Mathematical Modeling

• CSE 397 Computational and Variational Methods for Inverse Problems (Prof. Omar Ghattas)
• CSE 389C Introduction to Mathematical Modeling in Science and Engineering I (Prof. Robert Moser)
• CSE 389D Introduction to Mathematical Modeling in Science and Engineering II (Prof. Feliciano Giustino)