About Me
I am a PhD student at the University of New Mexico in the interdisciplinary Nanoscience and Microsystems Engineering program, working within the Smart Tensors Group at Los Alamos National Laboratory. My work is focused on the intersection of mathematical theory, high-performance computing, and machine learning. I find the most fulfillment in “full-stack” research: bridging the gap between abstract mathematical underpinnings, efficient numerical optimization, and scalable implementation to solve complex problems.
Currently, my research focuses on Scientific ML (SciML) and Optimization, specifically investigating how neural networks and second-order optimization can be applied to scientific domains. This includes developing JAX-based optimizers for inverse problems. While my current focus is on SciML, I have a background in information retrieval, recently publishing a Domain-Specific RAG architecture at ICMLA 2024 that has already garnered 34 citations according to Google Scholar.
Prior to starting my PhD, I spent five years working, including as a Machine Learning Engineer and Applied Scientist, where I specialized in developing software systems like learning-to-rank search and agentic RAG chatbots, taking them from prototypes into large-scale production. My foundation is a degree in Discrete Mathematics with a minor in CS-Intellgence from Georgia Tech, complemented by research in additive combinatorics and competitive success in the Putnam Mathematical Competition and ACM ICPC as well as creative hackathons like the Moog Hackathon.
I am currently seeking an ML Research Internship role for Summer 2026 where I can apply my experience in scalable optimization and neural networks to challenging problems.