Anirudh Satheesh

Undergraduate Student, University of Maryland, College Park

anirudhs [AT] terpmail.umd.edu

About

Hello! I recently graduated summa cum laude from the University of Maryland, College Park with degrees in Computer Science (Honors) and Applied Mathematics. My research interest focuses on developing robust agents and agentic systems. I'm currently working on scaling robust reinforcement learning to foundation models and developing theoretical guarantees for such algorithms. I am incoming at Qlabs.

I've been fortunate to work with and be advised by the following professors: Furong Huang on LLM safety and reasoning, Radu Balan on physics-informed machine learning, Hua Wei on multi-agent reinforcement learning, and Vaneet Aggarwal on robust reinforcement learning.

Publications

Most recent publications on Google Scholar.
‡ indicates equal contribution.

Compositional Adversarial Training for Robust Visual Watermarking

Anirudh Satheesh, Michael-Andrei Panaitescu-Liess, Andrew Xu, Georgios Milis, Heng Huang, Zikui Cai, Furong Huang

CompLearn ICML 2026 Workshop
Under Submission to NeurIPS 2026

Paper Code Website

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities

Zora Che, Stephen Casper, Robert Kirk, Anirudh Satheesh, Stewart Slocum, Lev E McKinney, Rohit Gandikota, Aidan Ewart, Domenic Rosati, Zichu Wu, Zikui Cai, Bilal Chughtai, Yarin Gal, Furong Huang, Dylan Hadfield-Menell

TMLR 2025

Paper

A Technical Report on 'Erasing the Invisible': The 2024 NeurIPS Competition on Stress Testing Image Watermarks

Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang

NeurIPS D&B 2025

Paper

MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning

Zikui Cai, Andrew Wang, Anirudh Satheesh, Ankit Nakhawa, Hyunwoo Jae, Keenan Powell, Minghui Liu, Neel Jay, Sungbin Oh, Xiyao Wang, Yongyuan Liang, Tom Goldstein, Furong Huang

Under Review at ECCV 2026

Paper

EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?

Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang

NeurIPS 2024 Safe Generative AI Workshop
ACL 2026 Findings

Paper

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems

Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang

EMNLP 2025 Findings

Paper

Regret Analysis of Unichain Average Reward Constrained MDPs with General Parameterization

Anirudh Satheesh, Vaneet Aggarwal

Under Submission to NeurIPS 2026

Paper

Provably Efficient Algorithms for S-and Non-Rectangular Robust MDPs with General Parameterization

Anirudh Satheesh, Ziyi Chen, Furong Huang, Heng Huang

Under Submission to NeurIPS 2026

Paper

Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization

Anirudh Satheesh, Pankaj Kumar Barman, Washim Uddin Mondal, Vaneet Aggarwal

Under Submission to UAI 2026

Paper

Distributionally Robust Self Paced Curriculum Reinforcement Learning

Anirudh Satheesh, Keenan Powell, Vaneet Aggarwal

RLC 2026

Paper

Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs

Anirudh Satheesh‡, Sooraj Sathish‡, Swetha Ganesh, Keenan Powell, Vaneet Aggarwal

arXiv

Paper

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending

Anirudh Satheesh, Keenan Powell, Hua Wei

CIKM 2025

Paper Code

A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control

Anirudh Satheesh, Keenan Powell

ACM Journal of Autonomous Transportation Systems 2025

Paper

PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training

Anirudh Satheesh, Anant Khandelwal, Mucong Ding, Radu Balan

TMLR 2025

Paper

SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation

Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang

ICLR 2024

Paper

CV

Full Resume in PDF.

Acknowledgements

This website is made using a template by Martin Saveski.
Anirudh Satheesh
anirudhs [AT] terpmail.umd.edu