I am a Quantitative Researcher at Headlands Technologies in Chicago where I work on high frequency trading strategies.
I graduated from UC Berkeley in 2024 with degrees in Physics and Computer Science.
As an undergrad, I was apart of the Nachman Group at Berkeley Lab where I worked on transfer learning for particle physics simulations and 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. In fall 2020, I interned as a Software Engineering Intern at AI Dynamics. During summer 2019, I was a Software Engineering Intern at Microsoft in the Azure COSINE (Windows Data Science) organization.
On campus, I was apart of Traders at Berkeley where I directed the Berkeley Trading Competition. I was also a Data Science Consultant at the Student Association for Applied Statistics and worked with ProducePay and Orbital Insight.
In my free time, I play table tennis 🏓, tennis 🎾, and badminton 🏸.
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