IDEAS on Generative AI Symposium

Yi Ma is a Chair Professor in Artificial Intelligence, the inaugural director of the School of Computing and Data Science and the Institute of Data Science of the University of Hong Kong since 2023. His research interests include computer vision, high-dimensional data analysis, and integrated intelligent systems. Yi received his two bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two master’s degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He served on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He was on the faculty of UC Berkeley EECS Department from 2018–2023, where he continues to be a visiting professor. He has published over 65 journal papers, 150 conference papers, and four textbooks on 3D vision, generalized PCA, high-dimensional data analysis, and machine intelligence. He received the NSF Career Award in 2004 and the ONR Young Investigator Award in 2005. He also received the David Marr Prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.

Jia Deng is a Professor of Computer Science at Princeton University. His research focuses on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of a number of awards including the Sloan Research Fellowship, the NSF CAREER award, the ONR Young Investigator award. 

Jason Eisner is Professor of Computer Science at Johns Hopkins University and a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Mathematical Institute for Data Science, the Cognitive Science Department, and the Data Science and AI Institute. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure, and to connect existing models (such as LLMs) to commonsense reasoning, formal reasoning, and downstream applications. His 180+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation; and principled methods for conversational AI, including neural language modeling and semantic parsing. From 2019-2024 he was Director of Research at Microsoft Semantic Machines, which developed new approaches to conversational AI. He is also the lead designer of Dyna, a declarative programming language that provides an infrastructure for AI algorithms. He has received 3 school-wide awards for excellence in teaching, most recently in 2025, as well as recent Best Paper Awards at ACL 2017, EMNLP 2019, and NAACL 2021 and Outstanding Paper Awards at ACL 2022, EMNLP 2024, and COLM 2025.

Mengdi Wang is a Professor at the Center for Statistics and Machine Learning in the Department of Electrical and Computer Engineering at Princeton University, with additional affiliations in the Department of Computer Science and the Omenn-Darling Bioengineering Institute. Wang received a PhD in Electrical Engineering and Computer Science, with a minor in Mathematics, from MIT in 2013, where Wang worked with Dimitri P. Bertsekas at the Laboratory for Information and Decision Systems (now IDSS). Prior to that, Wang completed undergraduate studies in Automation at Tsinghua University. Wang works closely with scientists and practitioners on implementing algorithms, working with real-world data and systems, and applying AI to accelerate scientific research, and collaborates with students at Princeton and beyond. Wang is also a proud mother of two and has an Erdős number of 3.

Jiatao Gu is an Assistant Professor at the University of Pennsylvania and a part-time Research Scientist at Apple. Previously, he was a Staff Research Scientist at Meta and Apple, and earned his PhD from the University of Hong Kong. His research spans generative modeling, language, computer vision, and multimodal AI, with a particular interest in world models for physically grounded intelligence. His recent work focuses on scalable models for vision and action that support prediction, planning, and interaction in the physical world.

Antonio Loquercio is an assistant professor at the University of Pennsylvania in the Department of Electrical and Systems Engineering, with a courtesy appointment in the Department of Computer and Information Sciences. He received his PhD and M.Sc. from UZH and ETH Zurich in 2021 and 2017. His research interests include learning-based robotics, computer vision, and machine learning. His work includes seminal results on simulation-to-real-world transfer in sensorimotor control. He is the recipient of several awards (the 2017 ETH Medal, the 2022 Georges Giralt PhD Award, and the 2025 ISNAFF Mario Gerla Award). Additionally, he has won several awards for his publications (2018 CORL Best Systems Paper, 2020 RSS Best Paper Honorable Mention, and the 2020 T-RO Best Paper Honorable Mention).  His article on superhuman drone racing was featured on the cover of Nature.

Lingjie Liu is an assistant professor at the University of Pennsylvania whose research focuses on computer graphics, vision, and AI, especially neural rendering, human modeling, and 3D reconstruction. Her work has earned major honors, including the Meta Distinguished Faculty Award and best paper honorable mentions at SIGGRAPH and SCA.

Eric Wong is an assistant professor at the Department of Computer and Information Science at the University of Pennsylvania. He leads Brachio Lab on debugging machine learning and making systems actually do what we want them to do. He is also a part of the ASSET Center on safe, explainable, and trustworthy AI systems. Previously, Eric Wong completed his PhD at CMU advised by Zico Kolter, and did a postdoc with Aleksander Madry.