I am a postdoctoral researcher at Princeton University, mentored by Dr. Jonathan Pillow. I completed my PhD in Applied Mathematics at the University of Washington, advised by Dr. Eric Shea-Brown. I have also held visiting research roles at Mila – AI Institute (with Dr. Guillaume Lajoie), MIT (with Drs. Robert Yang and Chris Cueva), and the Allen Institute (with Drs. Uygar Sümbül, Stefan Mihalas, and Stephen Smith).
I study learning in brains and machines through the growing availability of brain data and rapid advances in machine learning. I leverage deep learning to both model biological systems, drawing insights from theoretical frameworks to explain how neural circuits learn, and analyze data to uncover the principles of brain learning.
Research areas: NeuroAI, AI4Science, computational neuroscience, deep learning theory, decision-making, learning dynamics
Teaching interests: Machine learning, deep learning, neural networks, data science, computational neuroscience, applied mathematics, linear algebra, scientific computing, introductory engineering and computing courses
I was named a 2024 Rising Star in EECS (area: AI for Healthcare and Life Sciences) and a 2024 Rising Star in Computational and Data Sciences. I have published as the first author in NeurIPS, ICLR, ICML, PNAS, and IEEE, and my work has been supported by fellowships such as the NSERC PGS-D, FRQNT B2X, Pearson, and NSF AccelNet IN-BIC. I have also taught and mentored extensively, earning a departmental teaching award.
🚀 On the faculty job market for 2025–2026. Seeking roles in AI4Science, NeuroAI, and computational neuroscience with interests in research and teaching.