About Me

I am a computational neuroscientist studying how biological and artificial systems efficiently learn models of the world and use them flexibly to make optimal decisions. My work focuses on reinforcement learning, representation learning, and biologically plausible learning rules, with the goal of identifying computational principles that support rapid generalization and adaptive behavior. I develop theory-driven agents grounded in experimental data to explain neural computation and failure modes of learning, and to translate these insights into improved artificial systems and tools that support learning and education.

Experience

2026–Present — Postdoctoral Fellow, Max Planck Institute for Biological Cybernetics
2023–2025 — Postdoctoral Fellow, SEAS, Harvard University
2022–2023 — Research Scientist, Centre for Frontier AI Research (CFAR), A*STAR
2017–2018 — Research Engineer, A*STAR Artificial Intelligence Initiative

Education

Ph.D. Computational Neuroscience, National University of Singapore (2022)
B.Sc. Life Sciences, USP & SPS, National University of Singapore (2017)

Honors & Awards

Young Fellow, National University of Singapore (2025)
Spotlight Talk, Computational Psychiatry Conference (2025)
Best Flash Talk, Society for Neuroscience Singapore Chapter (2024)
MIT Center for Brains, Minds, Machines–Fujitsu Laboratories Fellow (2019)
Graduate School Scholarship, National University of Singapore (2018)
NUSSU Medal for Outstanding Achievement (2017)
University Scholars Programme (USP) Senior Honor Roll (2017)
Undergraduate Scholarship, A*STAR (2013)

News

03/2026 — Poster on scaling feature learning in hippocampal place-field model at COSYNE 2026
09/2025 — Talk on meta-reinforcement learning agents for suboptimal behavior at Rick Adams Lab, UCL
08/2025 — Poster on suboptimal agents from meta-RL at CCN 2025