I’m a PhD candidate in Computer Science at NTU Singapore, advised by Arvind Easwaran (NTU) and Blaise Genest (CNRS IPAL, France), working on trustworthy reinforcement learning for safety-critical systems: making RL policies transparent, safe and verifiable. In particular, I work on neurosymbolic techniques, especially symbolic policies that are amenable to formal verification, and on learning methods to train them.
More broadly, I’m interested in neurosymbolic AI that integrates intuitive (System 1) learning with explicit, deliberative (System 2) reasoning, and in the maths underneath it all: learning theory and statistics.
Before NTU, I did an M.Tech in Computer Science at ISI Kolkata and a B.Tech in Electronics & Instrumentation at NIT Silchar.
An exact, approximation-free encoding of oblique decision trees as neural networks, so trees train end-to-end with vanilla gradient descent. The resulting tree policies are interpretable and amenable to formal safety verification.
Neurosymbolic hierarchical RL that builds its abstract world model from incrementally added knowledge, rather than learning it upfront, improving sample efficiency on sparse-reward, long-horizon tasks.