معرفی
Shibei Zhu is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. Their research focuses on Probabilistic Machine Learning, Preference Learning, Decision Utility, Interactive Visualization, Human Behavior, and Cognitive Science. They actively collaborate on international research projects, contributing to advancements in human-AI interaction and autonomous systems.
Research interests include developing human-like models for preferential choice, interactive preference elicitation techniques, and multimodal policy generation from diverse behavioral data. Their work bridges machine learning with cognitive science to enhance decision-making systems.
Publications span topics like latent decision utilities, reward tuning in robotics, and data-driven autonomous systems, reflecting a strong emphasis on practical applications of AI. No awards are listed, but their doctoral research involves significant contributions to conference proceedings in top venues.
Shibei advises no formal students but collaborates with research groups such as the Kaski Samuel Professorship. Their work aligns with labs focused on probabilistic machine learning and human-centered AI.
