Hrishikesh ViswanathI am a PhD student at Purdue University, advised by Aniket Bera. My overarching goal is to bridge physical modeling and scalable surrogate models, with a focus in robotics and reinforcement learning (RL). I am broadly interested in scalable models for physics and their potential applications in robotics. A core problem driving my research is how to effectively integrate Partial Differential Equation (PDE) formulations and generative priors to provide geometric structure and stabilize continuous control and RL policies. At a foundational level, I want to address the challenge of learning high-fidelity dynamics in sparse data regimes. My work relies on two core pillars: efficient modeling techniques like model-order reduction, and scalable learning strategies such as lightweight physics-informed learning, leveraging alternatives to autodiff residuals such as Monte-Carlo methods (Walk-on-Spheres). Outside the lab, I was the 2024 Vice President of Purdue SEARCH. I mentored our NASA SUITS team to the competition finals, earning a NASA Artemis I mission flag that flew around the moon. |
|
Posts
Machine Learning Essentials (Jul 2024)A comprehensive list of resources to build an undergraduate-level foundation in ML, Linear Algebra, AI, and Statistics.
Reading List (Oct 2022)A curated collection of papers, books, and resources related to my core research interests. |
ResearchI'm interested in robotics, reinforcement learning, operator learning and PINN. |
|
Neural Implicit Energy Optimization for LiDAR MappingHrishikesh Viswanath, Md Ashiqur Rahman, Chi Lin, Damon Conover, Aniket Bera IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026 project page code LiDAR mapping as energy optimization over the curvature of a neural signed distance field |
|
Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific MLHrishikesh Viswanath, Md Ashiqur Rahman, Abhijeet Vyas, Andrey Shor, Beatriz Medeiros, Stephanie Hernandez, Suhas Eswarappa Prameela, Aniket Bera IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026 project page paper A survey of advances and challenges in data-driven Scientific ML |
|
Operator Learning Using Weak Supervision from Walk-on-SpheresHrishikesh Viswanath*, Hong Chul Nam*, Julius Berner, Anima Anandkumar, Aniket Bera Arxiv, 2026 arxiv code A mesh-free training scheme that amortizes the cost of Monte Carlo walks by using the Walk-on-Spheres algorithm to provide cheap, unbiased stochastic supervision for neural operators. |
|
Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order ReductionHrishikesh Viswanath, Yue Chang, Aleksey Panas, Julius Berner, Peter Yichen Chen, Aniket Bera Transactions on Machine Learning Research, 2026 project page arxiv code A sampling-based framework that evolves Lagrangian systems directly in physical space using data-driven neural PDE operators and learnable kernel ROM parameterization |
|
Physics Informed Viscous Value RepresentationsHrishikesh Viswanath, Juanwu Lu, S. Talha Bukhari, Damon Conover, Ziran Wang, Aniket Bera Arxiv, 2026 arxiv code We introduce a physics-informed regularization for offline GCRL derived from the Hamilton-Jacobi-Bellman (HJB) equation’s viscosity solution. By applying the Feynman-Kac theorem, we recast value estimation as a stable Monte Carlo expectation, ensuring geometric consistency and superior performance in high-dimensional navigation and manipulation tasks. |
|
Gradient-Free Physics-informed Operator Learning using Walk-on-SpheresHrishikesh Viswanath*, Hong Chul Nam*, Julius Berner, Anima Anandkumar, Aniket Bera NeurIPS 2025 AI for Science Workshop, 2025 arxiv A PINO technique for training operators using Monte-Carlo Walk-on-Spheres |
|
Physics-informed adaptive fourier neural interpolation operator for synthetic frame generationHrishikesh Viswanath*, Md Ashiqur Rahman, Rashmi Bhaskara, Aniket Bera US Patent App. 18/758,927, 2025 patent We introduce a resolution-independent neural operator architecture for synthetic frame generation that leverages global convolutions in the Fourier spectral domain via Fast Fourier Transform (FFT). |
|
Graph-based decentralized task allocation for multi-robot target localizationJuntong Peng, Hrishikesh Viswanath, Aniket Bera IEEE Robotics and Automation Letters, 2024 project page arxiv code A decentralized graph neural operator approach for robust and scalable task allocation in heterogeneous UGV-UAV teams. |
|
Trajectory prediction for robot navigation using flow-guided markov neural operatorRashmi Bhaskara*, Hrishikesh Viswanath*, Aniket Bera 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024 project page arxiv An Optical Flow-Integrated Markov Neural Operator that models pedestrian trajectory prediction as a Markovian process, eliminating the need for historical state storage. |
|
FairPy: A Toolkit for Evaluation of Prediction Biases and their Mitigation in Large Language ModelsHrishikesh Viswanath, Tianyi Zhang Arxiv, 2023 arxiv code We present a comprehensive survey and a modular toolkit of mathematical frameworks for quantifying and mitigating prediction bias in Large Language Models (LLMs). |
Selected Coursework
|
Reviewing
2026: ICLR, ICML, RA-L |
||||||||||
Mentorship & Leadership
|
||||||||||
Open Source Contributions |
|
Design and source code from Jon Barron's website |