Curriculum Vitae

Education

Harvard University, 2018-2024

Ph.D., Neuroscience: An opponent striatal circuit for distributional reinforcement learning


Yale University, 2014-2018

Phi Beta Kappa, summa cum laude

B.S., Cognitive Science, with Distinction: Seeing Structure: Shape Skeletons Modulate Perceived Similarity

B.S., Molecular, Cellular, & Developmental Biology, with Distinction: The M1 Muscarinic Receptor as a Potential Therapeutic Target for Schizophrenia

Selected Honors and Awards

  • • Nemko Prize in Cellular or Molecular Neuroscience, Society for Neuroscience, 2025
  • • UZH Award for Research in Brain Diseases, UZH Foundation, 2025
  • • Career Development and Transition Funding Award, Open Philanthropy, 2025
  • • Bowdoin Prize for Graduate Essay in the Natural Sciences, 2024, one of "Harvard's oldest and most prestigious student awards," "designed to recognize essays of originality and high literary merit, written in a way that engages both specialists and non-specialists"
  • • Best Extended Abstract Award, Unifying Representations in Neural Models workshop, 37th Conference on Neural Information Processing Systems (NeurIPS), 2023
  • • Japan Neuroscience Society Meeting Travel Award, Society for Neuroscience, 2023
  • • F31 Predoctoral National Research Service Award (NRSA), NIH, 2021–2024

Teaching

  • • Teaching Fellow, Prof. Steven Pinker, GENED 1066: Rationality, Spring 2024
    • ○ Taught two sections of Harvard undergraduates about rationality from both normative and descriptive perspectives. Topics included logic, probability, statistical decision theory, Bayesian reasoning, game theory, rational choice, correlation vs. causation, behavioral economics, and the psychology of decision-making. Applications surveyed included government policy, development aid, sports, journalism, medicine, forecasting, and philanthropy (effective altruism).

  • • Teaching Fellow and Guest Lecturer, Prof. Venkatesh Murthy, GENED 1125: Artificial & Natural Intelligence, Spring 2023 and Spring 2024
    • ○ Taught two sections of Harvard undergraduates about the intersection of artificial and biological intelligence. Topics included intelligence, neurobiology, neuroanatomy, receptive fields, motor control, communication, neural decoding, supervised learning, unsupervised learning, reinforcement learning, convolutional neural networks, language models, and robotics.
    ○ In Spring 2024, delivered a guest lecture on reinforcement learning in brains and machines

Full curriculum vitae available upon request.