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, bridging AI/ML theory and neuroscience. My work develops theory-driven and data-driven machine learning methods to uncover how the brain learns. More broadly, I aim to advance both AI/ML and neuroscience by identifying general principles of efficient and robust learning with long-term impact on AI and health.
Research areas: NeuroAI, AI4Science, computational neuroscience, deep learning theory, decision-making, learning dynamics
Teaching interests: Machine learning (including deep learning and neural networks), data science, computational neuroscience, NeuroAI, applied mathematics (numerical linear algebra, probability, differential equations), scientific computing, and lower-division service 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. Pursuing opportunities with a strong commitment to both research and teaching.