Song-Chun Zhu is the Director of the Beijing Institute for General Artificial Intelligence and Dean of the Institute for Artificial Intelligence at Peking University, where he also holds joint professorships at Peking University and Tsinghua University. A threetime recipient of the Marr Prize--the highest honor in computer vision--he has also been awarded the Sloan Fellowship and the Helmholtz Prize, and has twice served as Program Chair of CVPR. His research has played a foundational role in unifying computer vision, cognitive science, and artificial intelligence through a coherent mathematical framework. Yujia Peng received her Ph.D. from the University of California, Los Angeles, and is currently an Assistant Professor in the School of Psychological and Cognitive Sciences at Peking University, as well as a Research Fellow at both the Institute for Artificial Intelligence at Peking University and the Beijing Institute for General Artificial Intelligence. Her work spans computational psychiatry, cognitive neuroscience, social cognition, and artificial intelligence, with several influential publications bridging human cognition and machine intelligence. She serves as Guest Editor for Psychological Review and Journal of Anxiety Disorders, and as Editorial Board Member for Behaviour Research and Therapy and Psychological Science.

Zhenliang Zhang is currently a full-time research scientist at the Beijing Institute for General Artificial Intelligence. He received his Ph.D. in Optical Engineering from the Beijing Institute of Technology. His research focuses on virtual/mixed reality, intelligent wearable systems, and artificial general intelligence. Yizhou Wang is a Boya Distinguished Professor and doctoral advisor at Peking University, where he serves as Deputy Director of the Center for Frontiers of Computing. His research covers computer vision, artificial intelligence, statistical modeling, cognitive computing, medical image analysis, and computational art. Wang has led multiple highimpact projects at the intersection of perception, learning, and computation, contributing directly to the development of AGIrelated architectures and evaluation methods.