Scholar
Zongzhang Zhang
Google Scholar ID: sG7WEAgAAAAJ
Nanjing University
Citations & Impact
Citations
1,855
H-index
22
i10-index
46
Publications
20
Co-authors
33
list available
Publications
Resume (English only)
Academic Achievements
- Selected Publications:
- 1. Generalizable Multi-modal Adversarial Imitation Learning for Non-stationary Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025
- 2. Learning to Coordinate with Different Teammates via Team Probing, IEEE Transactions on Neural Networks and Learning Systems, 2025
- 3. Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models, IEEE Transactions on Neural Networks and Learning Systems, 2025
- 4. Efficient Multi-Agent Cooperation Learning through Teammate Lookahead, Transactions on Machine Learning Research, 2025
- 5. Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning, ICML-2025
- 6. Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation, ICLR-2025
- 7. Multi-Agent Domain Calibration with a Handful of Offline Data
Research Experience
- Served as an Associate Professor at the School of Artificial Intelligence, Nanjing University (Jul. 2019 - Dec. 2024); Associate Professor at the School of Computer Science and Technology, Soochow University (Jul. 2014 - Jun. 2019). Visiting Scholar at Stanford Intelligent Systems Laboratory (SISL) (Sept. 2018 – Mar. 2019); Research Fellow at the School of Computing, National University of Singapore (Nov. 2012 – Jun. 2014); Visiting Student at Rutgers Laboratory for Real-Life Reinforcement Learning (RL³) (Oct. 2010 – Oct. 2011); Research Engineer at Huawei's Noah's Ark Lab (2012).
Education
- Received a bachelor's degree in mathematics from Central South University in 2007; obtained a Ph.D. in Computer Science from the University of Science and Technology of China in 2012, supervised by Prof. Xiaoping Chen.
Background
- Research interests: artificial intelligence and machine learning. Current research areas include reinforcement learning (e.g., deep RL, transfer RL), multi-agent systems, probabilistic planning, and imitation learning.
