Research Team

Dr. Shuxin Zheng graduated from the MSRA-USTC Joint PhD Program. He currently serves as the Deputy Director of Zhongguancun Institute of Artificial Intelligence, Director of the Strategic Planning Office at the Beijing Zhongguancun Academy, Enterprise Mentor at Chinese Academy of Sciences, and Associate Editor of "AI for Science" journal. Previously, he was a Principal Researcher at Microsoft Research and Head of Microsoft Scientific Foundation Models. He has won multiple world championships in artificial intelligence and trained the largest scientific foundation model to date. Dr. Zheng has published over 20 first-author and corresponding-author papers in Nature subsidiary journals and top international AI conferences, with more than 5,000 citations. He serves as a visiting lecturer at Tsinghua University, Chinese Academy of Sciences, and Microsoft AI Academy, where he has long taught courses such as "Fundamentals of Machine Learning Methods and Applications" and "Advanced Machine Learning."

He holds a Ph.D. in Computer Science from the Institute of Cross-Disciplinary Information Sciences at Tsinghua University. His research focuses on the intersection of deep learning and quantitative finance, with a particular focus on cutting-edge applications of large language model agents. He has served as president of the Tsinghua University Quantitative Investment Association, has led scientific research cooperation projects for many securities firms and fund companies, and has published many papers on the intersection of AI and finance at top international conferences on artificial intelligence.

Yu Shi is currently a researcher at Zhongguancun Academy. His research focuses on multimodal scientific foundation models and high-performance machine learning systems. He specializes in joint pretraining of machine learning force fields and structure generation, and multimodal integration with large language models. Additionally, he has long been dedicated to addressing performance bottlenecks in machine learning systems, including low-precision and distributed training strategies, as well as GPU operator design. His work has been published in top-tier machine learning conferences and natural science journals. He has also delivered lectures on Advanced Machine Learning at institutions such as the Institute of Computing Technology, Chinese Academy of Sciences (ICT-CAS), and Tsinghua University. Moreover, he is a long-term contributor to open-source machine learning tools like LightGBM.

Yuanhe Tian is currently a Researcher at the Zhongguancun Institute of Artificial Intelligence, Beijing, China. He received the Ph.D. degree from the University of Washington, Seattle, WA, USA, in 2025. His research focuses on natural language processing, multimodal information processing, and medical information processing. He has published more than 60 papers (Citations: 1900+, H-index: 23) in top journals and conferences (e.g., TMM, TNNLS, TMI, ACL, AAAI) and served as a long-term Area Chair for the ACL conference series. Related work: [Google Scholar](https://scholar.google.com/citations?user=5GCwWZ8AAAAJ)

Jiyan He is a faculty member at Zhongguancun Academy (ZGCA) and a researcher at the Zhongguancun Institute of Artificial Intelligence (ZGCI). His main research interests include large language models, agents, and AI safety. He received both his bachelor's and Ph.D. degrees from the University of Science and Technology of China (USTC) and previously conducted research on machine learning and scientific intelligence at Microsoft Research Asia. Dr. He has published multiple papers in top international journals and conferences such as Nature Machine Intelligence, ICLR, ICML, and NeurIPS. His research spans the fundamental theory of artificial intelligence, system design, and interdisciplinary applications. He formerly served as CEO of the Linux User Group and the Microsoft Student Club at USTC, and has received several honors including the National Scholarship and the Guo Yonghuai Scholarship. He has also won championships in multiple international and domestic algorithm, supercomputing, and cybersecurity competitions. His work focuses on large language models, generative AI, AI for Science, and privacy and security, aiming to advance the theoretical foundations and algorithmic design of next-generation AI models and agents. Related work: [Google Scholar](https://scholar.google.com/citations?user=Ep5qE5QAAAAJ)