Published multiple papers including 'InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics' and 'Characterizing control between interacting subsystems with deep Jacobian estimation'. Received support from the National Science Foundation Graduate Research Fellowship and had papers accepted at NeurIPS, ICML workshops, and other conferences.
Research Experience
Worked in medicine (as an EMT), experimental neuroscience, computational neuroscience, and artificial intelligence (in industry). Currently a Machine Learning Research intern on the Neuromotor Interfaces Team at Meta in New York City.
Education
Graduated from Yale in 2021; started PhD at MIT in 2022 and joined Ila Fiete's lab for thesis work in 2023.
Background
PhD candidate in Computational Neuroscience at MIT, interested in bridging systems neuroscience, cognitive science, and deep learning through the lens of dynamical systems theory and machine learning. Designs and uses quantitative methods to understand computations performed by both biological and artificial neural networks.
Miscellany
Freelance editor, especially for college admission essays and graduate school statements of purpose. Has a strong interest in music.