Prashant Govindarajan

Prashant Govindarajan

PhD Student at Mila-Quebec AI Institute and Polytechnique Montréal

Prashant Govindarajan

Hello and வணக்கம்! I am a fourth-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 reinforcement learning and large language models for scientific discovery and beyond. In collaboration with Intel, I developed offline and online reinforcement learning methods for crystalline material design using first-principles. I also worked with Ansys on LLMs for automating Computer-Aided Design (CAD). Currently, I am working on improving exploration in RL fine-tuning of LLMs. My research was supported by the PBEEE scholarship for international students.

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. Besides academics, I like playing chess, frisbee, traveling, and cooking. Lately, I’ve been learning to snowboard and play the flute. I organize social activites and outings in my lab. Check out some cool photos here: https://chandar-lab.github.io/photos/ !

I am always open to discussing research and potential collaborations. Please feel free to reach out 😁!

I am actively seeking research internships in the areas of LLMs, reinforcement learning, and AI for Science.

Publications

  • Woo, Kowen, Prashant Govindarajan, and Sarath Chandar. Benchmarking Machine Learning Potentials for Crystal Structure Relaxation. In NeurIPS 2025 AI for Science Workshop.
  • Govindarajan, Prashant*, Davide Baldelli*, Jay Pathak, Quentin Fournier, and Sarath Chandar. 2026. “CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design.” Transactions on Machine Learning Research.
  • 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.

News

Projects

Fine-tuning LLMs for automating Computer-aided Designing

With Ansys, we developed CADmium, a new open-source dataset consisting of natural language descriptions for 170k+ 3D CAD objects. We propose a novel LLM fine-tuning workflow with code LLMs, and augment existing evaluation schemes with new metrics.

Reinforcement Learning for Crystal Structure Design

Currently exploring offline and online reinforcement learning methods for learning a policy for sequentially designing crystal structures.

Graph generative models for binding site-specific molecule generation

Designing novel deep graph generative model for generating new ligand molecules that can bind to a given target receptor binding site.

Analysis of drug response and gene expression data of AML cells

Computational methods to identify drug-gene correlations and molecules that can induce leukemic cell maturation.

Incorporating Geometry into Score-Based Model for Crystal Structure Design

Two ways to incorporate crystal symmetry information as an inductive bias into a generative model for crystal structure design.

Analyzing the effects of visual representations in visual navigation tasks

Two ways for encoding visual inputs for navigation task using RL – pretraining a contrastive learning-based SimCLR model and VAE-based generative approach.

Deep generative models for single-cell gene expression analysis

Evaluated state-of-the-art unsupervised deep learning techniques including variational autoencoders for single-cell gene expression data analysis.

Generating drug-like molecules from gene expression signatures using transformer

Designed an attention-based transformer model for de novo generation of drug-like molecules that can induce a desired transcriptomic profile. Accepted as poster at MLCSB COSI, ISMB 2022.

Parallel analyses of canonic polyadic tensor decomposition algorithm

CPU- and GPU-level parallelization of tensor decomposition algorithm using OpenMP and OpenACC.