Speakers / 报告嘉宾 · ICIT 2026

Biography / 个人简介

Dr. Hesheng Wang is Tang Jun Yuan Chair Professor at Shanghai Jiao Tong University and Dean of the Global College. As principal investigator, he has led multiple research projects, including projects funded by the National Key R&D Program of China, the National Science Fund for Distinguished Young Scholars, and the Excellent Young Scientists Fund, as well as key projects under the NSFC Joint Funds. His honors include the 8th Xplorer Prize and the Baosteel Excellent Teacher Award. He currently serves or has previously served as an Associate Editor of TRO, TASE, RAL, IJHR, and RIA. His other editorial roles include Senior Editor of TMECH, member of the Editorial Advisory Board of Advanced Intelligent Systems, and Editor-in-Chief of Robot Learning. He also served as General Chair of IROS 2025.

Robotic Embodied Navigation and Manipulation

Abstract / 报告摘要

This report focuses on the two core functions of service robots, namely mobility and manipulation. It begins by outlining the current status of the industry and technological development, as well as the challenges encountered. Subsequently, it presents the key achievements made by the team through long-term research into the core technical issues in mobility and manipulation: To address navigation in dynamic environments, a series of localization and navigation methods have been proposed, enabling robust perception and localization via visual fusion for mobile robots in large and complex scenarios. For robotic manipulation in complex environments, an adaptive visual servoing framework and learning-based mobile manipulation methods have been developed. Furthermore, a versatile vision-based methodology has been established for mobile manipulation, advancing the key generic technologies of service robots.

Biography / 个人简介

Daoqiang Zhang is a Professor, doctoral supervisor, and Dean of the School of Artificial Intelligence at Nanjing University of Aeronautics and Astronautics. He directs the Ministry of Education Key Laboratory of Brain-Machine Intelligence Technology and is an IAPR Fellow. He received his bachelor’s and doctoral degrees from Nanjing University of Aeronautics and Astronautics in 1999 and 2004. His research focuses on intelligent brain image analysis and its applications. In the past five years, he has published more than 200 papers in leading journals and conferences, including Nature Communications, IEEE TMI, TPAMI, TIP, NeurIPS, CVPR, KDD, and MICCAI. He has been listed among Elsevier’s Highly Cited Chinese Researchers for 12 consecutive years, with more than 20,000 citations. His research has received a second-class State Natural Science Award (2020) and Ministry of Education Natural Science Awards (2019 and 2016).

张道强教授，南京航空航天大学人工智能学院院长，教授，博士生导师。脑机智能技术教育部重点实验室主任、国际模式识别学会会士（IAPR Fellow）、“万人计划” 科技创新领军人才和青年拔尖人才、国家优青获得者、中国图像图形学学会监事、中国图学学会图学大数据专委会副主任、中国人工智能学会机器学习专委会常委、IEEE ComputerSociety Nanjing Chapter 副主席、IEEE Trans. Medical Imaging AE、Pattern Recognition AE、 Machine Intelligence Research AE、《数据采集与处理》副主编等。分别于1999和2004于南京航空航天大学计算机科学与工程系获学士和博士学位。2004年5月起留校任教，先后任讲师、副教授，并于2008年5月破格晋升为教授。2004年9月至2006年12月在南京大学计算机软件新技术国家重点实验室做在职博士后研究工作。2007年11月至2008年1月和2008年10-12月分别在香港科技大学和澳大利亚Curtin理工大学做短期学术访问。2010-2012年在美国北卡罗莱纳大学教堂山分校从事博士后研究工作。

在脑影像智能分析方面 与应用方面取得了一系列创新性成果。近 5 年主持承担了国家重点研发计划课题、国家自然科学基金重点项目、面上项目、牛顿高级学者基金等。在 Nature Communications、IEEE TMI/TPAMI/TIP、中国科学、NeurIPS、CVPR、KDD、MICCAI 等重要国内外期刊和会议发表论文 200 余篇。连续12年入选 Elsevier 中国高被引学者榜，论文被引用 20000 余次， 其中有 3 篇论文的单篇引用均超 1000 次。研究成果获得 2020 年国家自然科学二等奖（排 名 4）、2019 年教育部自然科学一等奖（排名 4）和 2016 年教育部自然科学二等奖（排名 1），以及 MICCAI 2019 和 MICCAI 2022 青年科学家奖等论文奖 10 余次。

Biography / 个人简介

Professor Dr. Angela Lee Siew Hoong is an award -winning academic leader, educator and researcher with extensive experience in artificial intelligence, data analytics, digital transformation and industry -engaged education. She is currently Professor and Deputy Dean (Employability & Alumni) at the Faculty of Engineering and Technology, Sunway University, where she leads initiatives to strengthen graduate employability, alumni engagement and strategic industry partnerships. She previously served as Head of the Computing Department, leading approximately 30 academic staff and 1,600 students . Her contributions to education have received national and international recognition, including the MCMC-Huawei Digital Leadership Programme Award and Deep Thinker Award in 2025, SAS Global Educator recognition in 2024, Malaysia National Outstanding Educator Award in 2023, and recognition among Malaysia’s Top 10 Most Prominent Data Science Leaders in 2022. Her research interests span AI, data analytics, blockchain, healthcare technology, digital education and emerging technologies. She has led and contributed to research and industry projects, including the RM1.26 million AI4PEP project. She is also an active keynote speaker, postgraduate supervisor and advocate for connecting academia with industry to create meaningful learning, career and innovation opportunities for students.

Human-Centred AI: Designing Intelligent Systems That People Can Trust, Understand and Use

Abstract / 报告摘要

Artificial Intelligence (AI) is increasingly embedded in decision-making, education, healthcare, business, and everyday life. While advances in AI have traditionally been driven by improvements in computational performance, prediction accuracy, and automation, technical capability alone does not guarantee meaningful adoption or positive impact. As intelligent systems become more autonomous and influential, there is a growing need to place human needs, values, understanding, and trust at the centre of AI design. This invited talk explores Human-Centred AI (HCAI) through three interconnected dimensions: trust, understandability, and usability. Drawing on research and practical applications of AI and data-driven systems, the talk examines how transparency, explainability, user-centred design, ethical considerations, and human feedback can contribute to more responsible and effective intelligent systems. It also discusses the persistent gap between developing technically sophisticated AI models and translating them into solutions that people can confidently understand and use in real-world contexts.