I am a PhD candidate in Computer Science at NTU Singapore, broadly interested in Trustworthy Reinforcement Learning and its applications to safety-critical domains. My research focuses on improving the transparency, safety, robustness, and verifiability of RL policies. Currently, I am exploring neurosymbolic approaches, such as programmatic policies, to enhance trustworthiness by leveraging their inherent interpretability and amenability to formal verification.
In parallel, I have a strong interest in Neurosymbolic AI, particularly in developing end-to-end methods that support explicit knowledge representation and reasoning, integrating both System 1 (intuitive) and System 2 (deliberative) processes. To deepen my conceptual understanding of AI, I actively study foundational mathematical topics such as learning theory, logic, and statistics.
Before joining NTU, I completed my Master’s degree in Computer Science at the Indian Statistical Institute (ISI), Kolkata, India, and my Bachelor’s degree in Electronics and Instrumentation at NIT Silchar, India.
Coming Soon!: Giving Back – a series of notebooks implementing NeSy/RLHF/GenAI models from scratch.
News
[Apr 2025] AI4X Workshop paper on Programmatic Reinforcement Learning for Trustworthy Microgrid Management, which discusses the ∂PRL approach for energy management in microgrids.
[Aug 2024] Our DTSemNet paper which proposes a novel method to train oblique decision trees using gradient descent got accepted in ECAI 2024 “Vanilla Gradient Descent for Oblique Decision Trees”.
[Jan 2024] We are organizing a Deep Learning Bootcamp in NTU.
[Aug 2023] Appointed as Director of Career and Development in Graduate Students’ Club (SCSE).
[May 2023] Blog on Interpretable Reinforcement Learning is out.
[Jan 2023] I am starting my Ph.D. (CS) at NTU Singapore. I will be working under the supervision of Arvind and Blaise on interpretable RL policies. The Descartes Program funds my PhD.