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Zhu Jun is Bosch AI Professor in the Department of Computer Science and Technology at Tsinghua University, and a Fellow of both IEEE and AAAI. His research focuses on probabilistic machine learning, including Bayesian methods, deep learning, and reinforcement learning. He received his bachelor’s and doctoral degrees from the Department of Computer Science and Technology at Tsinghua University in 2005 and 2009, respectively.

He has long been engaged in research on the fundamental theory of machine learning, efficient algorithms, and their application to complex data analysis. His work includes PAC-Bayes theories and methods for maximum entropy discriminative learning, regularized Bayesian theory, maximum-margin learning algorithms for Bayesian models, and optimal variance theory and efficient algorithms for diffusion models. He also developed the probabilistic programming library ZhuSuan and the reinforcement learning library Tianshou, and led his team to create Vidu, the first video foundation model benchmarked against Sora. He has published more than 200 papers in top-tier venues such as ICML, NeurIPS, ICLR, JMLR, PAMI, and Nature Machine Intelligence, with more than 50,000 citations on Google Scholar. He currently serves as Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence and has previously served as Adjunct Professor at Carnegie Mellon University and as Senior Area Chair or Best Paper Reviewer for conferences including ICML, NeurIPS, and ICLR. His honors include the China Youth Science and Technology Award, the Qiushi Outstanding Young Scholar Award, the Chen Jiageng Young Scientist Award, the Xplorer Prize, the ICLR Outstanding Paper Award, and the First Prize for Outstanding Head Teacher at Tsinghua University.