Prashant Govindarajan

Prashant Govindarajan

PhD Student at Mila-Quebec AI Institute and Polytechnique Montréal

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Hello and வணக்கம் 👋! I am Prashant Govindarajan, a third year Computer Engineering PhD candidate at Mila-Quebec AI Institute and Polytechnique Montréal (engineering school of UdéM), working under Sarath Chandar. I am keenly interested in AI for scientific discovery focusing on drug and material design. I am primarily exploring reinforcement learning approaches. My current project, which is in collaboration with Intel, is on developing offline and online reinforcement learning methods for crystalline material design using first-principles. I am also working with Ansys on LLMs for automating Computer-Aided Design (CAD). I was previously a dual degree student at the Indian Institute of Technology Madras, where I worked under Balaraman Ravindran and Karthik Raman on target-specific drug design. My research is supported by the PBEEE scholarship for international students.

Besides academics, I like playing frisbee, reading, and cooking. Lately, I’ve been learning to snowboard 🏂 and play the flute 🪈. Feel free to reach out to me if you wish to have a chat about research and beyond 😁! Also, I am always looking forward to strengthening my foundations in crystallography, density functional theory, and solid-state physics, and getting domain-related inputs for my research. So if you have a background in these areas or wish to discuss about the RL aspects of my research, I’d love to have a conversation some time!

Publications

  • Govindarajan, Prashant, Mathieu Reymond, Antoine Clavaud, Mariano Phielipp, Santiago Miret, and Sarath Chandar. CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning. In AI for Accelerated Materials Design-ICLR 2025.
  • Govindarajan, Prashant, Mathieu Reymond, Santiago Miret, Mariano Phielipp, and Sarath Chandar. Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach. In AI for Accelerated Materials Design-NeurIPS 2024.
  • Govindarajan, Prashant, Mathieu Reymond, Santiago Miret, Antoine Clavaud, Mariano Phielipp, and Sarath Chandar. A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation. In AI for Accelerated Materials Design-Vienna 2024.
  • Govindarajan, Prashant, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, and Sarath Chandar. Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning. Digital Discovery (2024).
  • Govindarajan, Prashant, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, and Sarath Chandar. Behavioral Cloning for Crystal Design.” In Workshop on Machine Learning for Materials, ICLR 2023.

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