I am a computational researcher in applied mathematics, optimization, and machine learning at Oakland University. My work focuses on designing and analyzing optimization algorithms for large-scale machine learning models and translating mathematical optimization into high-performance implementations.

My current research interests include nonsmooth and variational optimization, regularized inverse problems, DC programming, model compression, and fine-tuning methods that make models smaller, faster, and more accurate. I am especially interested in methods that connect rigorous mathematical analysis with practical computational performance.

Research Interests

• Optimization algorithms for machine learning and inverse problems
• Nonsmooth analysis, generalized equations, and Newton-type methods
• Difference-of-convex optimization and proximal algorithms
• Knowledge distillation, adaptive temperature scaling, and model compression
• High-performance numerical implementations for applied mathematics

Selected Highlights

• Ph.D. student in Applied Mathematics at Oakland University.
• M.S. in Applied Statistics from Oakland University.
• Research spanning optimization theory, PDE-constrained optimization, inverse problems, and machine learning.
• Instructor and guest instructor for algebra, pre-calculus, multivariable calculus, and nonlinear optimization courses.

Download my CV