I am a PhD candidate in Computer Science at NTU, Singapore, with a broad interest in Trustworthy Reinforcement Learning and its applications in safety-critical domains. My research focuses on enhancing the transparency, safety, robustness, and verifiability of RL policies. Currently, I am exploring programmatic policies (e.g. Decision Trees) for trustworthy RL, leveraging their interpretability and amenability for verification to achieve trustworthiness. In addition, I have a strong interest in Neurosymbolic AI and dedicate time to honing my theoretical skills by studying Learning Theory and mathematical topics relevant to AI.
Before joining NTU, I completed my graduate degree in Computer Science at ISI Kolkata, India, and my undergraduate degree in Electronics and Instrumentation at NIT Silchar, India.
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.