I'm a fourth-year undergraduate student at UC Berkeley studying Physics and Computer Science. I am interested in graph neural networks, deep learning for particle physics, and quantitative approaches to trading.
After graduation, I will be joining Headlands Technologies in Chicago as a Researcher for quantitative trading strategies.
I am currently apart of the Nachman Group at Berkeley Lab where I work on transfer learning for particle physics simulations. Previously I applied graph neural networks to deep learning for pion reconstruction. In summer 2023 I interned at Balyasny Asset Management in NYC where I worked on quantitative research and development for equities alternative data. In fall 2022 I took a gap semester to intern at DeepMind in London where I researched the effects of scale on GNNs for neural algorithmic reasoning with Borja Ibarz and Petar Veličković. During summer 2022 I worked at Two Sigma in NYC as a Quantitative Research Intern modeling equity trading strategies with alternative data. During summer 2021, I was in West Palm Beach, Florida working as Quantitative Research Intern at Voloridge, a systematic hedge fund. Previously, I interned as a Software Engineering Intern at AI Dynamics. Before that, I was a Software Engineering Intern at Microsoft in the Azure COSINE (Windows Data Science) organization during the summer of 2019.
On campus, I am apart of Traders at Berkeley where I direct the Berkeley Trading Competition. I was also a Data Science Consultant at SAAS, the Student Association for Applied Statistics and worked with ProducePay and Orbital Insight.
In my free time, I play table tennis 🏓, tennis 🎾, and badminton 🏸.
For more details, see my resume.
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