Resume

Resume

Sreejeet Maity
Ph.D. Student in Electrical Engineering · North Carolina State University · Raleigh, NC, U.S.A
I develop provably robust finite-sample guarantees for reinforcement learning (RL) under uncertainty and adversarial corruption. My current research interests include corruption-tolerant reinforcement learning, robust policy evaluation, distributed and federated reinforcement learning, and minimax lower bounds that characterize the fundamental limits of robust learning.
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Robust RL Statistical Learning Theory Control Theory Federated RL Robust Statistics
Collaboration Note. I am always happy to engage with researchers working on broad areas of reinforcement learning, control theory, federated learning, and trustworthy machine learning. If you are interested in discussing possible collaborations or exchanging research ideas, please reach out to me via .
Prospective Opportunities. I will be entering the academic and industry research job market next year (2027), and I would be grateful to hear about opportunities aligned with my interests in robust and safe RL, reliable decision-making and control. I am also open to postdoctoral opportunities beginning in Fall 2027, as well as opportunities in subsequent academic cycles. Prospective recruiters, search committees, and researchers are warmly welcome to reach out.

Education

North Carolina State University
Ph.D. in Electrical Engineering · Aug 2023--Present · Raleigh, NC
Advisor: Dr. Aritra Mitra.
Indian Institute of Science, Bangalore
M.Tech. in Robotics and Autonomous Systems · Aug 2021--June 2023 · Bangalore, India
Jadavpur University
B.E. in Electrical Engineering · Aug 2017--July 2021 · Kolkata, India

Research Experience

Graduate Research / Teaching Assistant
North Carolina State University · Aug 2023--Present
  • Robust optimal policy learning from corrupted and correlated observations. Showed that vanilla Q-Learning is provably fragile under reward corruption, designed robust Bellman-update methods, and established finite-time convergence with matching minimax lower bounds. Results disseminated across ICML 2026, NeurIPS 2025, and IEEE CDC 2024.
  • Robust policy evaluation under adversarial influences and Markovian data. Developed finite-time theory for robust temporal-difference learning with Markovian noise and function approximation, including upper bounds and near-tight lower bounds. This research is published in AISTATS 2025.
  • Robust federated and multi-agent reinforcement learning. Developed adversarially robust and communication-efficient reinforcement learning algorithms for federated multi-agent settings, including Byzantine-resilient methods with collaborative speedups. Two papers published at ACC 2026.
Research Collaboration · Neuromuscular Rehabilitation Engineering Laboratory
University of North Carolina at Chapel Hill · April 2026--Present
  • Developing personalized reinforcement learning methods for subject-specific tuning of commercial powered knee prostheses.
  • Formulating personalized MDP/CMDP models with user-specific dynamics, time-varying reward design, and safety-comfort constraints.
  • Exploring offline-to-online adaptation schemes that warm-start from population priors and enable safe, data-efficient personalization.

Selected First-Authored Publications

Corruption-Tolerant Optimal Asynchronous Q-Learning
Sreejeet Maity†, Aritra Mitra
International Conference on Machine Learning, ICML 2026
Adversarially-Robust TD Learning with Markovian Data
Sreejeet Maity†, Aritra Mitra
International Conference on Artificial Intelligence and Statistics, AISTATS 2025
Robust Q-Learning under Corrupted Rewards
Sreejeet Maity†, Aritra Mitra
IEEE Conference on Decision and Control, CDC 2024
Robust Federated Q-Learning with Almost No Communication
Sreejeet Maity†, Aritra Mitra
American Control Conference, ACC 2026
Variance-Reduced Q-Learning over Static and Time-Varying Networks
Sreejeet Maity†, Feng Zhu, Robert Heath, Aritra Mitra
American Control Conference, ACC 2026

Workshop Presentations and Invited Talks

  • Multi-Agent Robust FRL with Sparse Communication, NCSU Robotics Symposium 2026.
  • Corruption-Tolerant Agnostic Q-Learning, NeurIPS 2025 Reliable ML Workshop.
  • Robust Federated RL with Byzantine Agents, Applied AI Symposium 2025.
  • Theoretical Limits of Robust TD Learning, New York RL Workshop, NYRL 2025, Amazon.
  • Towards Finite-Time Theory for Adversarially-Robust RL: Mathematical Guarantees and Fundamental Limits, Northeast Systems and Control Symposium, NESCW 2025.
  • Adversarially-Robust Deep Q-Network for Algorithmic Trading, MLSS 2025, NCSU.
  • Robust Algorithms for Adversarial Reinforcement Learning, Applied AI Symposium 2024.
  • Towards Finite-Time Rates for Adversarially-Robust RL, CORAL Seminar, NCSU.

Works Under Review / Preparation

Resilient RL Algorithms using Batch Robustification Under Corruptive Events
Robust Q-Learning methods for adversarially corrupted rewards and transitions, using batch-level robust Bellman updates with finite-time guarantees.
Byzantine-Resilient Federated Q-Learning with Logarithmic Communication
Federated reinforcement learning with Byzantine agents, near-optimal finite-time sample complexity, collaborative speedups, and minimal communication.
Decentralized Q-Learning over Random Networks with Near-Optimal Rates
Decentralized reinforcement learning over random communication graphs with finite-time guarantees and collaboration benefits.
Learning Robust Trading Policies under Adversarial Market Signal Corruption
Robust deep Q-Learning methods for algorithmic trading under noisy, corrupted, or adversarially manipulated market signals.

Projects

Federated MARL-GYM
We introduce a custom multi-agent reinforcement learning environment built with Gymnasium and Pygame, designed for evaluating federated RL (FRL) algorithms. The environment models a grid world where multiple agents navigate to accomplish spatially distributed tasks, like reaching delivery points.

Academic and Professional Service

  • Head Teaching Assistant for ECE 516: Systems and Control Engineering and ECE 308: Elements of Control Systems, Department of Electrical and Computer Engineering, NC State.
  • Served as a reviewer for 30+ papers in multiple flagship control/ ML venues, including the American Control Conference (ACC), the IEEE Conference on Decision and Control (CDC), Learning for Dynamics and Control (L4DC), Annual Conference on Neural Information Processing Systems(NeuRIPS), Journal of Machine Learning Research (JMLR), Transactions in Machine Learning Reserach (TMLR), IEEE Transactions in Automatic Control (TACON), Transactions in Signal and Information Processing over Networks (TSIPN), and Transactions in Signal Processing (TSP).

Awards

  • ACC 2026 Travel Award May 2026
  • L4DC Student Support Grant May 2025
  • NESCW 2025 Student Support Grant May 2025
  • IEEE CDC 2024 Student Support Award August 2024
  • NC State ECE Student Research Support Award August 2024
  • College of Engineering Graduate Merit Award 2023--24, 2024--25

Skills

Programming
Python, MATLAB, Simulink, C++.
Software and Libraries
PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, Gymnasium, MuJoCo.
Mathematics
Linear Algebra, Probability Theory, Robust Statistics, Stochastic Optimization.
Research
Reinforcement Learning, Statistical Learning Theory, Optimization, Control Theory.

Relevant Coursework

  • Learning Theory: Theoretical Foundations of Large-Scale Machine Learning, Machine Learning for Signal Processing, Bayesian Learning, Physics Modelling with Neural Networks, Deep Learning and Neural Networks.
  • Mathematics: Analysis, Probability and Stochastic Processes, Stochastic Models and Applications, Convex Optimization for Data Science, Detection and Estimation Theory.
  • Control Theory: Dynamics of Linear Systems, Networked and Distributed Control, Formal Analysis for Control Theory.