I am a PhD student at the University of Wisconsin–Madison, developing machine learning methods for theoretical physics. My current focus is on developing new ML techniques for symbolic reasoning in theoretical physics calculations.
I graduated from MIT (Physics & Mathematics ’25), where I conducted undergraduate research under Professor Phil Harris and through the NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), developing ML methods for high-energy physics. My research experience has been diverse, spanning geometric ML for jet physics, custom ML algorithms for experimental detector readout, and theoretical QFT calculations—bridging experimental and theoretical physics.
I was born in Charlotte, North Carolina, and attended local schools until high school when I was accepted to the North Carolina School of Science and Mathematics (NCSSM ‘20). At NCSSM, I continued to study mathematics and physics. I was accepted to MIT in 2020 but chose to delay my admission by one year due to COVID-19.
