AI与DSA联合工作坊：构建可泛化可信智能
基础模型时代的可泛化与可信智能学术工作坊（Building Generalizable and Trustworthy Intelligence Workshop）将于2026年7月2日（周四）在香港科技大学（广州）E1-147举办。
本次工作坊围绕基础模型时代的前沿研究方向展开，核心聚焦可泛化表征学习、可信人工智能与接地智能三大领域。上午场特邀英国布里斯托大学两位学者带来研究报告，分享通用表征、多模态生成、不完美信息场景下可信AI等方向的最新研究进展。下午场为邀约制圆桌研讨，将围绕AI科研智能体对科研范式、博士培养体系与学术职业路径带来的变革展开深度交流。
时间
2026年7月2日（周四）
上午场・特邀学术报告（10:00-12:00）
下午场・邀约制圆桌讨论（14:00-16:00）
地点
上午：E1-147；下午-报名链接：https://forms.gle/mHeqKKbgPuEp2zgF6
报告嘉宾
Wei-Hong Li
英国布里斯托大学计算机学院 讲师（助理教授）
Nan Lu
英国布里斯托大学计算机学院 人工智能方向讲师（助理教授）
工作坊组织者
陈颖聪，香港科技大学（广州）助理教授
郭志江，香港科技大学（广州）助理教授
陈浩，香港科技大学助理教授
活动流程
上午场・特邀学术报告（10:00-12:00）
本场主题为可泛化可信 AI：表征学习、鲁棒性与真实场景接地。单篇报告时长约 40 分钟，报告后设置 10 分钟问答交流环节。
10:00-10:05
开场致辞
10:05-10:55
Invited Talk 1: From Universal Representations to Grounded Intelligence
Speaker: Wei-Hong LI
10:55-11:05
中场休息
11:05-11:55
Invited Talk 2: When Data Lies: Building Trustworthy AI from Imperfect Information
Speaker: Nan LU
下午场・邀约制圆桌讨论（14:00-16:00）
下午场为邀约制内部交流，主题为Research Careers in the Age of AI Research Agents。圆桌将围绕以下方向展开:
当文献检索、代码实现、实验迭代、结果分析与论文写作越来越多由AI research agents辅助时，研究者的核心价值如何重新定义。
在AutoResearch时代，博士训练如何更重视问题提出、研究品味、批判性判断、实验设计与独立思考。
小型课题组和青年学者如何在基础模型时代选择问题、形成特色并保持竞争力。
中国内地、香港与英国之间的学术职业路径、基金体系、人才计划、学生培养、产业合作与评价机制。
嘉宾与报告简介
Wei-Hong Li
Lecturer (Assistant Professor), School of Computer Science, University of Bristol
Talk Title: From Universal Representations to Grounded Intelligence
Modern machine learning systems are often trained for individual tasks and domains, limiting their ability to generalize, adapt, and interact with the real world. In this talk, I will present a line of work aimed at building representations that are universal, structured, and grounded. I will begin with methods for universal representation learning across tasks, domains, time and disciplines, and show how shared structure enables efficient transfer and adaptation. I will then move to 3D-aware and geometry-guided multi-task learning, where explicit world structure provides strong inductive biases and supervision. Finally, I will present systems that connect representations to the physical and multimodal world, including language-guided 3D image compositing and action synthesis in 3D scenes. Overall, the talk argues that grounding representations in structure, geometry, and the physical world is key to building more general, robust, and controllable intelligent systems.
Biography
Wei-Hong Li is a Lecturer (Assistant Professor) within School of Computer Science at the University of Bristol and a member of ELLIS - the European Laboratory for Learning and Intelligent Systems. He obtained his PhD at the University of Edinburgh, supervised by Prof. Hakan Bilen and Prof. Timothy Hospedales. After PhD, he was a postdoc at the University of Edinburgh, working with Prof. Hakan Bilen and he was a SHIAE postdoctoral fellow within the MultiMedia Lab (MMLab) at the Chinese University of Hong Kong (CUHK), working with Prof. Xiangyu Yue. His research interests are in computer vision and machine learning, with a focus on universal representation learning, learning visual models from limited human supervision, 3D-aware modeling and multi-modal generative models. His PhD thesis was the only recipient of the BMVA Sullivan Doctoral Thesis Prize Runner-Up across the whole UK in 2022. His first-authored MTPSL paper is awarded the CVPR 2022 Best Paper Nominee. His another first-authored paper won the ICIG 2017 Best Paper Award. He serves as Area Chair at ICLR 2026 and NeurIPS 2026, and he received the Top Reviewer Award at NeurIPS 2023 and NeurIPS 2024.
Nan Lu
Lecturer (Assistant Professor) in Artificial Intelligence, School of Computer Science, University of Bristol
Talk Title: From Universal Representations to Grounded Intelligence
Modern AI thrives on large models and massive datasets. Yet in many real-world domains, from healthcare records to social systems, labels are often missing, biased, or unreliable, and data evolves in messy, unpredictable ways. This raises a fundamental question: how can we build AI we can trust when the data itself cannot be trusted? In this talk, I will present a unified perspective on building trustworthy AI under imperfect information. We will explore how models can learn without explicit labels, adapt to distribution shifts, leverage decentralized unlabeled data in federated settings, and train reinforcement learning agents when reward signals are sparse or unreliable. Along the way, I will share recent advances from our research, discuss the key challenges that connect these problems, and highlight open questions that offer exciting opportunities for future research and collaboration.
Biography
Dr. Nan Lu is a Lecturer in Artificial Intelligence in the School of Computer Science at the University of Bristol, UK. She was a postdoctoral researcher in the Foundations of Machine Learning Systems Group at the University of Tubingen, Germany, working with Prof. Robert Williamson, and obtained her Ph.D. in Machine Learning from the University of Tokyo under the supervision of Prof. Masashi Sugiyama. Her research focuses on trustworthy AI, developing principled algorithms that remain reliable under data corruption and across diverse modalities, with applications in core AI areas such as computer vision and reinforcement learning, as well as real-world problems in healthcare and social data.
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