PhD Candidate in Computer Science @ Universität Ulm
E-Mail: zeqiang.zhang@uni-ulm.de
About Me
Hello! I'm currently a PhD student at the Institute of Neural Information Processing at Ulm University, where I'm fortunate to be supervised by Prof. Dr. Dr. Daniel Alexander Braun. My research lies at the intersection of reinforcement learning, goal-conditioned learning, intrinsic motivation, and world model learning. I'm particularly interested in how autonomous agents can learn and adapt in complex, dynamic environments driven by internal goals and curiosity. Besides, I am also interested in agent-based modeling and its applications in computational economics.
I hold a Master's degree in Cognitive Systems—an interdisciplinary program combining computer science and psychology—from Ulm University. Before that, I earned my Bachelor's degree in Electronic Science and Technology from Southeast University in China.
Reinforcement learning
Goal-conditioned learning
Intrinsic motivation
World model learning
Agent-based modeling
"Simulating Labor Market Dynamics with Agent-Based Models" (Poster), Ruxin Chen and Zeqiang Zhang, 2025 Japanese Economic Association Spring Meeting, Nagoya, Japan, 2025 Link
"Adaption on the Macro Level in 2LIKE (Exam Preparation Module)" (Short Talk), Zeqiang Zhang, Workshop on Adaptive Learning in Higher Education, Ulm, Germany, 2025
"Learning Contrastively: A Novel Goal-Conditioned Supervised Learning Approach with Dual-evaluation Mechanism" (Poster), Zeqiang Zhang, Fabian Wurzberger, Gerrit Schmid, Sebastian Gottwald and Daniel Braun, FRoBio: Freiburg Robotics and Biology Conference, Freiburg, Germany, 2023 Link
"Temporal goal abstraction for goal-conditioned reinforcement learning" (Poster), Fabian Wurzberger, Zeqiang Zhang, Gerrit Schmid, Sebastian Gottwald and Daniel Braun, FRoBio: Freiburg Robotics and Biology Conference, Freiburg, Germany, 2023 Link
“From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models” (with Ruxin Chen) Preprint Paper
“Autonomous Learning from Success and Failure: Goal-Conditioned Supervised Learning with Negative Feedback” (with Fabian Wurzberger, Gerrit Schmid, Sebastian Gottwald and Daniel A. Braun) Preprint Paper
“Learning Robust Representations for World Models without Reward Signals” (with Fabian Wurzberger, Sebastian Gottwald and Daniel A. Braun, accepted by EWRL 2025) Openreview Preprint Paper
“Characterizing Failure Mechanism of Soft and Hard Rocks: Implication from Acoustic Emission and Machine Learning” (with Zhuang Li, Nuwen Xu, Feng Gao and Biao Li)
“A Cognitive Perspective on Information Frictions in Labor Markets” (with Ruxin Chen)