About

I grew up in Valley Stream, NY, where my early curiosity for science was nurtured through museum visits and trips to the observatories across Long Island. That curiosity evolved into a passion for physics during high school, leading me to pursue a degree in physics at the University at Buffalo (UB).

At UB, I was exposed to a broad range of fields — from cosmology to condensed matter to particle physics. I discovered my interest in theoretical particle physics through hands-on labs and independent studies. I even designed a cosmic ray detector, which gave me a deep appreciation for the precision and ambition of high-energy physics experiments. A research experience at UB introduced me to the mathematical structure underlying quantum field theory and solidified my path toward graduate study.

I went on to earn my PhD in physics from Southern Methodist University (SMU), where I focused on studying the internal structure of pion, a particle that mediates the strong force between nucleons (protons and neutrons) at low energies. My research combined theoretical physics, statistical modeling, and computational tools to extract meaningful insights from various experimental datasets and theoretical predictions. I developed a novel approach using Bézier curves to represent parton distribution functions (PDFs), a probability density function of any given partons (constituent particle) within the pion. This allows us to systematically explore model uncertainty that has never been accounted for, specifically the pion in high-energy physics.

Today, I’m looking to apply the same analytical thinking, statistical analysis, coding expertise, and problem-solving mindset to data science, machine learning, and applied research challenges.