Bo An is a Professor in the College of Computing & Data Science, and Co-Director of Artificial Intelligence Research Institute (AI.R) at Nanyang Technological University, Singapore. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His current research interests include artificial intelligence, multiagent systems, computational game theory, reinforcement learning, and optimization. His research results have been successfully applied to many domains including infrastructure security and e-commerce. He has published over 100 referred papers at AAMAS, IJCAI, AAAI, ICAPS, KDD, UAI, EC, WWW, ICLR, NeurIPS, ICML, JAAMAS, AIJ and ACM/IEEE Transactions. Dr. An was the recipient of the 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award, an Operational Excellence Award from the Commander, First Coast Guard District of the United States, the 2012 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, and 2018 Nanyang Research Award (Young Investigator). His publications won the Best Innovative Application Paper Award at AAMAS’12, the Innovative Application Award at IAAI’16, and the best paper award at DAI’20. He was invited to give Early Career Spotlight talk at IJCAI’17. He led the team HogRider which won the 2017 Microsoft Collaborative AI Challenge. He was named to IEEE Intelligent Systems' "AI's 10 to Watch" list for 2018. He is PC Co-Chair of AAMAS’20 and will be General Co-Chair of AAMAS’23. He is a member of the editorial board of JAIR and is the Associate Editor of AIJ, JAAMAS, IEEE Intelligent Systems, ACM TAAS, and ACM TIST. He was elected to the board of directors of IFAAMAS, senior member of AAAI, and Distinguished member of ACM.

I am a Professor in the Department of Computer Science and Engineering, and the Director of the Center for Innovation, Technology, and Aging at the University of South Florida (Assistant Professor 2009-2015, Associate Professor 2015-2020, Associate Chair of Graduate Affairs 2018-2020). I was a Visiting Associate Professor at Stanford University from 2016 to 2017. I received his Ph.D. degree in Computer Science from the University of Utah in 2007. Then I had his Postdoctoral training at Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA (2007-2008) and the University of Utah (2008-2009). I initiated the IEEE RAS Technical Committee on Robotic Hands, Grasping, and Manipulation and served as its first co-Chair. I have also served on several editorial boards as an Associate Editor and Senior Editor, including IEEE Transactions on Robotics, IEEE Robotics and Automation Letters (RA-L), ICRA, and IROS.

My research interests are in robotics, machine learning and applied nonlinear control. They encompass robot learning and control, object grasping and manipulation, human-robot interaction, robotic hand and tactile sensing, and neuroscience. I am particularly interested in finding the deep connections between dynamics of intelligent systems and learning algorithms (learning from humans or from optimizations), which enables adaptive, efficient and robust control design for complex systems. The goal of my research is to enable robots to perform skills with the level of dexterity and flexibility that humans demonstrate in similar tasks. I am also particularlly interested in finding the novel application of robotic grasping and manipulation in the real world. Short Bio: Before joining Wuhan University as an associate professor, I was a PhD student at the Learning Algorithms and Systems Laboratory (LASA) at EPFL in Switzerland, with Professor Aude Billard, working on dynamic grasp adaptation- from humans to robots. Before that, I spent several years of my undergraduate and graduate studies at Huazhong University of Science and Technology, China.

博士毕业于美国加州大学伯克利分校，博后曾就职于美国斯坦福大学。研究方向为具身智能与机器人学、强化学习、模仿学习等。围绕具身人工智能的关键环节，系统性地研究了视觉深度强化学习、模仿学习和机器人操作，对解决具身人工智能领域中数据效率低和泛化能力弱等核心问题做出多项贡献。发表顶级会议论文五十余篇，代表性工作曾被MIT Tech Review，Stanford HAI等媒体报道。 I am a Tenure-Track Assistant Professor at Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University. I am leading the Tsinghua Embodied AI Lab (TEA Lab, logo), where we build robots and then bring intelligence to robots. I was a postdoctoral researcher at Stanford Vision and Learning Lab (SVL) advised by Prof. Jiajun Wu. I obtained my Ph.D. in Berkeley AI Research (BAIR) advised by Prof. Trevor Darrell. I obtained my bachelor degree from Tsinghua University (major in EE, minor in Management). My research focuses on modeling the dynamics of the world, leveraging/finding human priors for policy learning, and further enabling algorithms to learn in a sample-efficient manner and generalize to unseen scenarios. I am also interested in solving complex real robot applications with deep learning and reinforcement learning.

Jianlin (Jack) Cheng is a Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri. He received his PhD in information and computer science from the University of California, Irvine in 2006. His research is focused on developing machine learning and artificial intelligence (AI) methods and tools for big biomedical data analysis. His research group has developed numerous bioinformatics tools for analyzing protein structure and function, biological networks, and 3D genome structure, which are used by scientists around the world. His research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF), and the U.S. Department of Energy (DOE). Cheng was a recipient of the NSF CAREER award and the MU College of Engineering’s junior and senior faculty research awards.

仉尚航现为北京大学计算机学院任长聘系列助理教授（研究员），博士生导师。于2018年博士毕业于美国卡内基梅隆大学，后于2020年初加入加州大学伯克利分校 Berkeley AI Research Lab (BAIR) 从事博士后研究。其研究方向为开放环境泛化机器学习理论与系统，同时在计算机视觉和类脑智能方向拥有丰富研究经验。在人工智能顶级期刊和会议上发表论文50余篇，并申请5项美中专利。荣获世界人工智能顶级会议AAAI’2021 最佳论文奖，该工作曾列世界最大学术源代码仓库Trending Research 榜单第一，受到十余家媒体报道推广，开源代码被访问7万余次、2600余次Star。作为编辑和作者由Springer Nature出版英文书籍《Deep Reinforcement Learning》，至今电子版全球下载量超十二万次，入选中国作者年度高影响力研究精选，并出版中文译本。Google Scholar引用数3100次，h-index 23, i10-index 35。于2018年入选美国“EECS Rising Star”，曾获得Adobe学术合作基金，Qualcomm创新奖提名。获国际人脑多模态计算模型响应预测竞赛第一名，NeurIPS 2021 Visual Domain Adaptation 竞赛第三名。曾多次在国际顶级会议NeurIPS、ICML上组织Workshop，多次作为国际顶级期刊和会议的审稿人或程序委员，担任AAAI 2022/2023 高级程序委员。 Dr. Shanghang Zhang is a Tenure Track Assistant Professor at the School of Computer Science, Peking University. She has been the postdoc research fellow at Berkeley AI Research Lab (BAIR), UC Berkeley. Her research focuses on OOD Generalization that enables the machine learning systems to generalize to new domains, categories, and modalities using limited labels, with applications to autonomous driving and robotics, as reflected in her over 50 papers on top-tier journals and conference proceedings (Google Scholar Citations: 4321, H-index: 28, I10-index: 38). She has also been the author and editor of the book “Deep Reinforcement Learning: Fundamentals, Research and Applications” published by Springer Nature. This book is selected to Annual High-Impact Publications in Computer Science by Chinese researchers and its Electronic Edition has been downloaded 150,000 times worldwide. Her recent work “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting” has received the AAAI 2021 Best Paper Award. It ranks the 1st place of Trending Research on PaperWithCode and its Github receives 3,300+ Stars. Shanghang has been selected to “2018 Rising Stars in EECS, USA”. She has also been awarded the Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship. Her research outcomes have been successfully productized into real-world machine learning solutions and filed 5 patents. Dr. Zhang has been the chief organizer of several workshops on ICML/NeurIPS, and the special issue on ICMR. Dr. Zhang received her Ph.D. from Carnegie Mellon University in 2018, and her Master's from Peking University.

Lin Shao is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research interests lie at the intersection of Robotics and Artificial Intelligence. His long-term goal is to build general-purpose robotic systems that intelligently perform a diverse range of tasks in a large variety of environments in the physical world. Specifically, his group is interested in developing algorithms and systems to provide robots with the abilities of perception and manipulation. He is a co-chair of the Technical Committee on Robot Learning in the IEEE Robotics and Automation Society and serves as the Associated Editor at ICRA 2024. His work received the Best System Paper Award finalist at RSS 2023. Previously, he received his PhD at Stanford University, advised by Jeannette Bohg and co-advised by Leonidas J. Guibas. He received his BS in Geochemistry from Nanjing University.

国家杰出青年基金获得者，Fellow of IAPR/IET，曾任商汤科技首席研发总监/研究院执行院长。长期从事多模态人工智能、机器学习等领域的应用基础研究，作为首席科学家/项目负责人，承担国家2030科技创新重大项目，入选国家万人计划青拔人才；曾带领商汤科技研发团队搭建大规模AI基础设施，开拓新兴行业。在国际顶级学术期刊和会议发表论文300余篇，论文被引用近3万次(谷歌学术统计)，多次入选全球高被引学者榜单；获权威期刊Pattern Recognition年度最佳论文奖，多媒体计算旗舰会议ICME最佳论文钻石奖，计算机视觉旗舰会议ICCV最佳论文奖提名；获中国图像图形学会科学技术一等奖、吴文俊人工智能自然科学奖，省级自然科学一等奖；指导博士生(梁小丹、王可泽等)获得CCF优秀博士论文奖、ACM China优秀博士论文奖及CAAI优秀博士论文奖。（曾）担任知名期刊IEEE Trans. Human-Machine Systems, IEEE Trans. Multimedia, IEEE Trans. Neural Networks and Learning Systems 编委 (Associate Editor)，十余次担任IEEE CVPR、ICCV、NeurIPS、KDD、ACM Multimedia 等国际会议的领域主席。

Dr. Nan Duan is the head of the Vision and Multimodal Lab at JD Explore Academy, where he leads a research team focused on vision and multimodal foundation models. Prior to this, he served as the Technical Fellow at StepFun (2024-2025) and as a Senior Principal Researcher and Research Manager in the Natural Language Computing Group at Microsoft Research Asia (2012-2024). Dr. Duan is a world-renowned expert in natural language processing (NLP), code intelligence, multilingual multimodal foundation models, and AI agents. He has authored over 200 research papers in top-tier conferences and journals, accumulating more than 27,000 citations (h-index 74) and holds over 20 patents. His contributions to the field have been recognized with several prestigious awards, including the Runner-Up Best Paper Award at NeurIPS 2024 and the Best Demo Award at CVPR 2022. He is an adjunct professor and Ph.D. supervisor at the University of Science and Technology of China, Xi’an Jiaotong University, and Tianjin University. Dr. Duan earned his B.S. and Ph.D. in Computer Science from Tianjin University in 2004 and 2011, respectively. In 2019, he was named the CCF-NLPCC Distinguished Young Scientist for his contributions to NLP, and in 2023, he was listed among the DeepTech Intelligent Computing Innovators in China for his work on AI foundation models. 段楠博士，现任京东探索研究院视觉与多模态实验室负责人，带领研究团队研发视觉和多模态基础模型。此前，他曾任阶跃星辰Technical Fellow(2024-2025)和微软亚洲研究院自然语言计算团队资深首席研究员和研究经理(2012-2024)。段博士是自然语言处理(NLP)、代码智能、多语言多模态基础模型和AI智能体领域的世界知名专家。他在顶级会议和期刊上发表了超过200篇研究论文，累积引用超过27,000次(h-index 74)，并拥有20多项专利。他在该领域的贡献得到了多个重要奖项的认可，包括2024年NeurIPS最佳论文亚军奖和2022年CVPR最佳演示奖。段博士是中国科学技术大学、西安交通大学和天津大学的兼职教授及博士生导师。段博士于2004年和2011年分别获得天津大学计算机科学学士和博士学位。2019年，他因在自然语言处理领域的贡献被评为CCF-NLPCC杰出青年科学家，2023年，他因在人工智能基础模型方面的贡献被列为中国DeepTech智能计算创新人物之一。

张伟楠博士现任上海交通大学计算机系教授、博士生导师、副系主任，科研领域包括强化学习和数据科学，相关研究成果在CCF-A类国际会议和期刊上发表100余篇学术论文，谷歌学术引用2万余次，爱思唯尔中国高被引学者，获得5个最佳论文奖项，出版教材《动手学强化学习》和《动手学机器学习》。张伟楠长期担任NeurIPS、ICML、ICLR、KDD等会议的领域主席和TPAMI、FCS等期刊的编委，作为负责人承担国家自然科学基金优秀青年项目和科技部2030新一代人工智能重大项目课题，入选中国科协青年人才托举工程和上海市科委英才扬帆计划，获得吴文俊人工智能优秀青年奖和达摩院青橙奖。张伟楠于2011年获得上海交通大学计算机系ACM班学士学位，于2016年获得伦敦大学学院计算机系博士学位。 Weinan Zhang is now a professor at the department of computer science and engineering, Shanghai Jiao Tong University. His research interests include reinforcement learning and data science with various real-world applications of robotic control, game AI, recommender systems, etc. He has published over 200 research papers at prestigious international conferences and journals, accumulating over 20k citations on Google Scholar, been selected as Elsevier China Highly Cited Researcher. He has been serving as an area chair at ICML, NeurIPS, ICLR, KDD, etc. and an associate editor at TPAMI and FCS. He was granted the ACM Rising Star Award 2017 and the Alibaba DAMO Young Scholar Award 2018. His research won five best paper awards at international conferences and workshops, including the Best Paper Honorable Mention Award at SIGIR 2017 and the Best System Paper Award at CoRL 2020. Weinan earned his Ph.D. from the Computer Science Department of University College London in 2016 and his B.E. from the ACM Honored Class of Shanghai Jiao Tong University in 2011.

梁小丹，中山大学教授，博导，逸仙学者，2016年博士毕业于中山大学计算机学院，2014-2016年于新加坡国立大学访问学者，2016年-2018.10年，在美国卡内基梅隆大学（CMU）机器学习系做博士后研究（合作导师：Eric P. Xing教授），2018年底加入中山大学智能工程学院，2024年担任中山大学通用具身智能中心主任，中山大学人机物智能融合实验室（https://www.sysu-hcp.net/ ）联合负责人。

伦敦大学学院（UCL）博士在读，导师是汪军教授。研究兴趣包括因果表示学习、强化学习和推荐系统，在机器学习领域的顶级会议和期刊上发表多篇一作研究成果。