Austin, Texas, United States
Backend software engineer working on high-impact, client-facing systems. My interests extend into computational engineering, including physics- and math-based modeling, machine learning, and performance-critical systems. I have hands-on experience with CUDA, and GPU computing through prior projects, internships, and academic work, and enjoy working close to the hardware when performance matters. I’m particularly interested in roles and collaborations at the intersection of ML, high-performance computing, and applied modeling.
Mobility Matching Backend
- Developed efficient large scale non-convex optimization algorithms for training high dimensional learning models using Pytorch, supervised by Professor Nikolaos Bouklas. - Created an accelerated, parallelized, and scalable training algorithm for deep convolutional neural networks, with preliminary results showing improvement over traditional SGD variants.
- Built an application to examine percolation behavior and genetic convergence trends in migration networks - Implemented dynamics solver for the single drug-treatment model in cancer cells, extended implementation and model to include cross-mutation and multi-drugs - Created computational model for spatial cancer cell proliferation under multi-treatment environment, eventually to be used as an RL framework - Fit existing lab data to mathematical models to discover trends in cell growth rates under different conditions using MATLAB and Python
- Developed AI-assisted computational fluid dynamics (CFD) software in a pure Linux environment using CUDA, C, and C++ - Implemented GPU-parallelized ray tracing algorithms for Voronoi mesh generation to accelerate large eddy simulations
-Teaching assistant over the years for Discrete Structures, Embedded Systems, and Foundations of Robotics