Prof. Jian Pei

Jian Pei is the Arthur S. Pearse Distinguished Professor at Duke University. His research focuses on data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Royal Society of Canada (RSC), the Canadian Academy of Engineering, ACM, and IEEE. He is a productive and influential author. Since 2000, he has published one textbook, two monographs and over 300 research papers in refereed journals and conferences, which have been cited over 128,000 times. His research has generated remarkable impact substantially beyond academia. His algorithms have been adopted by industry in production and popular open source software suites. He is responsible for several commercial systems of unprecedentedly large scale. Jian Pei received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award and the 2015 ACM SIGKDD Service Award.

To enable practical federated learning, we have to not only improve the efficiency but also address the incentive and fairness concerns. In this talk, I will delve into the challenges in valuation and personalization in federated learning and share our recent endeavours. Particularly, valuation in federated learning seeks to allocate credits to participants in a just and equitable manner. Personalization in federated learning addresses the individual needs of participants. In the context of federated learning, those problems have some unique challenges. For example, the federated learning process is often run only once and many participants may not be consulted in every round. I will discuss the intuitions and ideas of our latest methods and also discuss some challenges and opportunities for future work.

Dr. James Zou

James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He is also the faculty director of Stanford AI4Health. He works on both improving the foundations of ML–-by making models more trustworthy and reliable–-as well as in-depth scientific and clinical applications. Many of his innovations are widely used in tech and biotech industries. He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe. His research has also been profiled in popular press including the NY Times, WSJ, and WIRED.

The talk will explore how we can develop and use generative AI to help researchers, clinicians, and patients. It will first discuss how we employ AI to generate recipes for the creation and validation of new drugs. Then, the talk will present how we developed visual-language AI to help clinicians interpret histology images. Finally, the talk will also discuss how we use large language models (LLMs) to simplify medical forms and improve the informed consent process for patients. To conclude, it will discuss perspectives on what’s on the new horizon for generative AI.

Prof. Xilin Chen

Xilin Chen is a professor with the Institute of Computing Technology, Chinese Academy of Sciences (CAS). He has authored one book and more than 400 papers in refereed journals and proceedings in the areas of computer vision, pattern recognition, image processing, and multimodal interfaces. He is currently an information sciences editorial board member of Fundamental Research, an editorial board member of Research, a senior editor of the Journal of Visual Communication and Image Representation, and an associate editor-in-chief of the Chinese Journal of Computers, and Chinese Journal of Pattern Recognition and Artificial Intelligence. He served as an organising committee member for multiple conferences, including general co-chair of FG 2013 / FG 2018, VCIP 2022, program co-chair of ICMI 2010 / FG 2024, and an Area Chair / Senior PC of ICCV / CVPR / ECCV / NeurIPS / ICMI for more than 10 times. He is a fellow of the ACM, IEEE, IAPR, and CCF.

As robots become more prevalent in our daily lives, their ability to understand and respond to human communication is becoming increasingly important. While natural language processing (NLP) has enabled robots to capture human intentions from verbal communication, understanding non-verbal cues such as facial expressions and gestures remains a challenge. However, these non-verbal channels are crucial for conveying user intention to robots, especially for human-centered robotics. In this talk, I will explore the roles of non-verbal channels in human communication, which provide additional information to emphasise key points and express attitudes. I will also discuss recent efforts in understanding facial expressions and gestures, both conscious and unconscious, to improve the interaction efficiency between humans and robots.

Prof. Chengqi Zhang

Professor Chengqi Zhang has been the Pro Vice-Chancellor and Distinguished Professor of UTS. He has been appointed as the General Chair of IJCAI-2024 and a member of the Board of IJCAI Trustees (2022-2026). Between 2021 and 2023, he was invited to be a judge for China and Asia Pacific for MIT Technology Review Innovators Under 35 (TR35). Professor Zhang received a PhD degree from the University of Queensland in October 1991, and a DSc (Higher Doctorate) degree from Deakin University in October 2002. All degrees are in the field of computer science (the field of artificial intelligence).

So far, Professor Zhang has published more than 350 scientific research papers; According to Google Scholar, his total papers have attracted a total of more than 26,000 citations and an H-index of 69. He has been invited to give 28 keynote speeches at international conferences. He has supervised more than 30 doctoral graduates, 10 of whom have been promoted to full professorships. He was the recipient of the NSW Science and Engineering Award in the Engineering and ICT category in 2011 and the Vice-Chancellor's Award for Outstanding Research in Leadership at UTS in 2011.

Professor Zhang was a college of experts in the Australian Research Council (ARC) from 2012 to 2014. He has also served as the chair of the world's three top conferences in the field of artificial intelligence (including ICDM-2010, KDD-2015, IJCAI-2024).

ChatGPT is a large language model based on the GPT architecture that generates human language with logic and semantics by processing massive amounts of language data. Its emergence has made a qualitative breakthrough in the cognitive field of AI research and has shown the prospect of artificial general intelligence. It will profoundly affect various directions of AI research. The stunning effect and convenience of ChatGPT will influence various aspects of people's work and life. However, as a generative model, ChatGPT cannot guarantee the accuracy of the generated results. Therefore, in domains where inaccurate results can have serious consequences, it is necessary for human experts to supervise its use. In summary, embracing the development of AI can bring a better future, but we should use it with caution and supervision in critical domains.