Your Name
About Me
I am an undergraduate researcher in Applied Mathematics at the University of California, Merced, specializing in Computational and Data Sciences. My research focuses on adaptive parameter estimation and data-driven modeling for nonlinear dynamical systems, with applications in control and autonomous systems. I study how algorithms such as Recursive Least Squares (RLS), and neural networks can be used to identify governing dynamics and enable real-time learning in complex environments. Broadly, my work bridges numerical analysis, system identification, and scientific machine learning to develop computational frameworks that unify data and physics for robust prediction and control of dynamical processes.
Research Interests
- Adaptive System Identification: real-time parameter estimation (Recursive Least Squares, Kalman Filtering) for nonlinear and time-varying dynamical systems
- Learning-Based Control: integrating data-driven estimation with feedback control for adaptive and predictive autonomous systems
- Scientific Machine Learning: hybrid modeling combining physics-based and data-driven methods for system prediction and control
- Numerical Methods for Stiff Systems: studying the stability and convergence of implicit ODE solvers for stiff and chaotic systems
- Computational Linear Algebra: large-scale least squares, matrix factorizations (SVD, QR, eigendecomposition), and low-rank approximations in modeling
News
- [Jul. 2025] Featured in The Mercury News in an article on the impact of science funding cuts, highlighting my research efforts and challenges Article.
Powered by Jekyll and Minimal Light theme.