Shaofeng Zou (邹韶峰)
Associate Professor
School of Electrical, Computer and Energy Engineering
Office: GWC 434
Email: zou@asu.edu
PhD student and postdoc positions available
Embedded Files
Research interests:
Research interests:
My research interests include reinforcement learning, machine learning, statistical signal processing and information theory.
Current research projects include:
Machine learning and Reinforcement learning
Robust machine learning and statistical inference
Quickest change detection and sequential analysis
Short Bio:
Short Bio:
I am an Associate Professor with the School of Electrical, Computer and Energy Engineering at Arizona State University. I was an Assistant Professor in the Department of Electrical Engineering at the University at Buffalo, the State University of New York from 2018 to 2024. I was a postdoc at Coordinated Science Lab, University of Illinois at Urbana-Champaign from Jul 2016 to Aug 2018, supervised by Prof. Venugopal V. Veeravalli. I completed the Ph.D. degree in Electrical and Computer Engineering from Syracuse University in May, 2016, supervised by Prof. Yingbin Liang (currently at The Ohio State University). I received the B. E. degree (with honors) from Shanghai Jiao Tong University in 2011. I received the NSF CRII Award (2020), NSF CAREER Award (2024), DARPA YFA Award (2024), the 2023 AAAI Distinguished Paper Award and UB Exceptional Scholar: Young Investigator Award (2024). I am currently serving as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Information Theory. I have also served as Area Chair for Neurips.
To Prospective Students
To Prospective Students
PhD students: Students with strong backgrounds in machine learning, signal processing, mathematics, and statistics are encouraged to email me about potential positions in our group. Please include your resume, undergraduate/graduate transcripts.
Research Opportunities for Undergraduate/Master Students: Both master and undergraduate students at ASU are encouraged to discuss with me if you are interested in my research.
Visiting scholars/students are also highly welcome.
Honers and Awards
Honers and Awards
DARPA YFA Award (2024)
NSF CAREER Award (2024)
UB Exceptional Scholar: Young Investigator Award (2024)
AAAI Distinguished Paper Award (2023)
NSF CRII Award (2020)
News!
News!
June 2025, our paper Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning has been accepted by IEEE Transactions on Information Theory;
Independently-Normalized SGD for Generalized-Smooth Nonconvex Optimization has been accepted by TMLR!April 2025, we receive the DARPA YFA Award! Thanks to DARPA for the support!
Jan. 2025, one paper is accepted to AISTATS 2025, two papers are accepted to ICLR 2025 and one paper is accepted to TMLR! Congrats to Zilong and Qi!
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
MGDA Converges under Generalized Smoothness, Provably
Revisiting Large-Scale Non-convex Distributionally Robust Optimization
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise VarianceDec. 2024, our paper "Data-Driven Quickest Change Detection in (Hidden) Markov Models" has been accepted by IEEE Transactions on Signal Processing! Congrats to Qi!
Sept, 2024, two papers accepted to NeurIPS 2024: "Policy Optimization for Robust Average Reward MDPs" and "A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch"! Congrats to Zhongchang, Yue and our collaborators!
Aug 2024, our group is moving to ASU!
June 2024, our paper "Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach" has been accepted by Transactions on Machine Learning Research! This paper develops a DRO-based approach to solve the offline reinforcement learning problem, and achieves the optimal sample complexity asymptotically!
May 2024, two papers have been accepted at ICML 2024 (acceptance rate 27.5%)! Congrats to Yudan, Zhongchang and our collaborators!
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation
Constrained Reinforcement Learning Under Model MismatchApril 2024, our paper "Model-Free Robust Reinforcement Learning with Sample Complexity Analysis" has been accepted at the 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024). Congrats to Yudan!
Mar 2024, several journal papers have been accepted recently. Congrats to the students!
Quickest Change Detection in Autoregressive Models on IEEE Transactions on Information Theory
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process on Operations Research Letters
Finite-Time Error Bounds for Greedy-GQ on Machine Learning
Robust Average-Reward Reinforcement Learning on Journal of Artificial Intelligence Research
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? on Transactions on Machine Learning ResearchFeb 2024, our paper "Understanding Information Disclosure from Secure Computation Output: A Study of Average Salary Computation" has been accepted for publication at the 14th ACM Conference on Data and Application Security and Privacy (CODASPY) (21.25% acceptance rate)!
Feb. 2024, I am glad to receive the NSF CAREER Award "CAREER: Robust Reinforcement Learning Under Model Uncertainty: Algorithms and Fundamental Limits"! 🎉🎉
Jan 2024, our paper "Sample Complexity Characterization for Linear Contextual MDPs" has been accepted for publication at 2024 AISTATS (acceptance rate: 27.6%)! Congrats to Junze and our collaborators!
Dec 2023, our paper "Large-scale Non-convex Stochastic Constrained Distributionally Robust Optimization" has been accepted for publication at 2024 AAAI (acceptance rate: 23.75%)! Congrats to Qi!
Nov 2023, our paper "Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty" has been accpeted for publication on Journal of Machine Learning Research, thanks to our collaborators, Shaocong, Ziyi and Yi.
July 2023, Yue Wang successfully defended his thesis "Towards Data Efficiency and Robustness of Reinforcement Learning" and is joining University of Central Florida as tenure-track assistant professor, Fall 2023!
June 2023, our paper "A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems" has been accepted for publication at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), thanks to our collaborators Sihong, Shuo and Fei!
April 2023, our paper "Model-Free Robust Average-Reward Reinforcement Learning" has been accepted for publication at 2023 ICML (acceptance rate: 27.9%)!
April 2023, I am glad to receive the award of AFRL VFRP (2023). I will be visiting AFRL, Rome NY, this summer.
April 2023, Qi and Zhongchang's paper "Data-Driven Quickest Change Detection in Hidden Markov Models" has been accepted for publication at 2023 IEEE International Symposium on Information Theory (ISIT)! Congrats to both!
April 2023, Zhongchang's 3rd journal paper "Kernel Robust Hypothesis Testing has been accepted for publication on IEEE Transactions on Information Theory ! Congrats to Zhongchang!
Mar 2023, Zhongchang's 2nd journal paper "Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples" has been accepted for publication on IEEE Transactions on Signal Processing! Congrats to Zhongchang!
Feb 2023, two papers accepted at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)! We also have a special session “Robust Learning and Inference” at ICASSP! Congrats!
🔔Feb 2023, our AAAI paper “Robust Average-Reward Markov Decision Processes” was selected to receive the AAAI 2023 Distinguished Paper Award among the 8,777 submissions and the 1,721 accepted ones. Congrats to Yue Wang and our collaborators!
I am invited to deliver an invited talk “Robust Reinforcement Learning under Model Uncertainty” at the 6TH WORKSHOP ON COGNITION & CONTROL organized by Prof. Sean Meyn, co-organized by Profs. Shreya Saxena and Yuheng Bu, University of Florida! Slides can be found here.
News before 2022
News before 2022
Nov 2022, our paper “Robust Average-Reward Markov Decision Processes” has been accepted by AAAI 2023 (Oral, acceptance rate: 19.6%), congrats to Yue Wang and our collaborators!
I am invited to presented the talk “Policy Gradient Method For Robust Reinforcement Learning” at Allerton Sept. 2022.
I am invited to deliver an invited talk “Policy Gradient Method For Robust Reinforcement Learning” at International Conference on Continuous Optimization (ICCOPT), Lehigh University, July 2022. slides
May 2022, Our paper “Data-Driven Robust Multi-Agent Reinforcement Learning” has been accepted for presentation at IEEE MLSP 2022. Congrats to Yudan and Yue!
April 2022, I am glad to receive the award of AFRL VFRP (2022). I will be visiting AFRL, Rome NY, this summer.
I (together with Dr. Yingbin Liang, Dr. Yi Zhou) successfully delivered a tutorial “Optimization Meets Reinforcement Learning” at 2022 IEEE ICASSP (slides can be found here)!
May 2022, Two papers, “Policy Gradient Method For Robust Reinforcement Learning” and “Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis” have been accepted by ICML 2022 (acceptance rate: 21.9%)!
April 2022, Our paper, “Robust Hypothesis Testing With Kernel Uncertainty Sets” has been accepted to be presented at the 2022 IEEE International Symposium on Information Theory (ISIT). Congrats to Zhongchang!
Feb 2022, Our journal paper, “Robust Sequential Hypothesis Testing in Adversarial Environments” has been accepted for publication on Sequential Analysis!
Jan 2022, Our journal paper, “Quickest Change Detection in Anonymous Heterogeneous Sensor Networks” has been accepted for publication on IEEE Transactions on Signal Processing. Congrats to Zhongchang!
Dec 2021, Dr. Yingbin Liang from OSU, Dr. Yi Zhou from U. Utah and myself delivered the tutorial on “Optimization Meets Reinforcement Learning” at the 2021 IEEE BigData. Slides can be found here.
Sept 2021, two papers, “Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation” and “Online Robust Reinforcement Learning with Model Uncertainty” have been accepted by NeurIPS 2021 (acceptance rate: 26%), congrats to Yue Wang!
Sept 2021, our paper, “Population Risk Improvement with Model Compression: An Information-Theoretic Approach” has been published in Entropy as part of the Special Issue Information Theory and Machine Learning.
Aug 2021, I received grant as PI @ UB in collaboration with Dr. Ruizhi Zhang (UNL): “CCSS: Collaborative Research: Quickest Threat Detection in Adversarial Sensor Networks”. Thanks to NSF!
July 2021, Dr. Yingbin Liang from OSU, Dr. Yi Zhou from U. Utah and myself delivered the tutorial on “Recent Advances in Reinforcement Learning Theory” at the 2021 IEEE International Symposium on Information Theory (IEEE ISIT 2021). Slides can be found here.
May 2021, I received grant as PI @ UB in collaboration with Dr. Veeravalli ( UIUC) and Dr. Atia (UCF): “Collaborative Research: CIF: Medium: Emerging Directions in Robust Learning and Inference”. Thanks to NSF!
April 2021, three papers have been accepted by IEEE ISIT 2021, and one paper has been accepted by IEEE ECCE 2021!
April 2021, our paper, “Sequential (Quickest) Change Detection: Classical Results and New Directions”, has been accepted for publication on IEEE Journal on Selected Areas in Information Theory!
Jan 2021, our paper, “Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity”, has been accepted by ICLR 2021 (acceptance rate: 28.7%)!
Dec 2020, our paper, “Learning Graph Neural Networks with Approximate Gradient Descent”, has been accepted by AAAI 2021 (acceptance rate: 21%)!
Nov 2020, The 2nd Buffalo Day for 5G and Wireless Internet of Things, co-organized by Dr. Zou, is held with a great success!
Sep 2020, our paper, “Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis”, has been accepted by NeurIPS 2020 (acceptance rate: 20.1%)!
June 2020, I received grant as sole PI from NSF: “CIF: Small: Reinforcement Learning with Function Approximation: Convergent Algorithms and Finite-sample Analysis”. Thanks to NSF!
May 2020, Our paper, “Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise” has been accepted by Conference on Uncertainty in Artificial Intelligence (UAI) 2020 (acceptance rate: 27.6%)! Congrats to Yue Wang!
April 2020, Our paper, “Tightening Mutual Information Based Bounds on Generalization Error” has been accepted for publication on IEEE Journal on Selected Areas in Information Theory!
Mar 2020, Our paper, “A Game-Theoretic Approach to Sequential Detection in Adversarial Environments” has been accepted by IEEE ISIT 2020!
Mar 2020, One journal paper, “Sequential algorithms for moving anomaly detection in networks” has been accepted for publication on Sequential Analysis!
Feb 2020, I received NSF CRII award “CRII: CIF: Dynamic Network Event Detection with Time-Series Data”. Thanks to NSF!🎉🎉
Feb 2020, I gave a presentation “Finite-Sample Analysis for SARSA with Linear Function Approximation” at Information Theory and Applications Workshop - ITA, San Diego, USA.
Jan 2020, Zhongchang's paper “Quickest Change Detection in Anonymous Heterogeneous Sensor Networks”, has been accepted by IEEE ICASSP 2020!
Nov 2019, Dr. Zou has been elected to the IEEE Signal Processing for Communications and Networking (SPS SPCOM) Technical Committee (TC) for a 3-year term, effective January 1, 2020.
Nov 2019, One paper, “Information-Theoretic Understanding of Population Risk Improvement with Model Compression” has been accepted by AAAI 2020 (acceptance rate: 20.6%) !
Nov 2019, The 1st Buffalo Day for 5G and Wireless Internet of Things, co-organized by Dr. Zou, is held with a great success!
Oct 2019, One paper, “Quickest Detection of Dynamic Events in Networks”, has been accepted for publication on IEEE Transactions on Information Theory!
Sep 2019, Two papers, “Finite-Sample Analysis for SARSA with Linear Function Approximation” and “Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples”, have been accepted by NeurIPS 2019 (acceptance rate: 21%)! See you in Vancouver!
Mar 2019, Two papers, “Tightening Mutual Information Based Bounds on Generalization Error” and “Quickest Detection of a Moving Target in a Sensor Network”, have been accepted by IEEE ISIT 2019!
Feb 2019, One paper, “Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection”, has been accepted for publication on IEEE Transactions on Signal Processing!
Jan. 2019, One paper, “Distributed Quickest Detection of Significant Events in Networks”, has been accepted by IEEE ICASSP 2019!
Oct. 2018, One paper, “Quickest Change Detection under Transient Dynamics: Theory and Asymptotic Analysis”, has been accepted for publication on IEEE Transactions on Information Theory!
Page updated
Google Sites
Report abuse