Keynote Speakers

Prof. Guoyin Wang
President of Chongqing Normal University, China
IRSS/I2CICC/CAAI/CCF Fellow, IEEE SM
Vice-President of CAAI

Biography: Guoyin Wang received the B.S., M.S., and Ph.D. degrees from Xi'an Jiaotong University, Xian, China, in 1992, 1994, and 1996, respectively. He worked at the University of North Texas, and the University of Regina, Canada, as a visiting scholar during 1998-1999. He had worked at the Chongqing University of Posts and Telecommunications during 1996-2024, where he was a professor, the Vice-President of the University, the director of the Chongqing Key Laboratory of Computational Intelligence, the director of the Key Laboratory of Cyberspace Big Data Intelligent Security of the Ministry of Education, the director of Tourism Multi-source Data Perception and Decision Technology of the Ministry of Culture and Tourism, and the director of the Sichuan-Chongqing Joint Key Laboratory of Digital Economy Intelligence and Security. He was the director of the Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, China, 2011-2017. He has been serving as the President of Chongqing Normal University since June 2024. He is the author of over 10 books, the editor of dozens of proceedings of international and national conferences and has more than 300 reviewed research publications. His research interests include rough sets, granular computing, machine learning, knowledge technology, data mining, neural network, cognitive computing, etc. Dr. Wang was the President of International Rough Set Society (IRSS) 2014-2017, and a council member of the China Computer Federation (CCF) 2008-2023. He is currently a Vice-President of the Chinese Association for Artificial Intelligence (CAAI), and the President of Chongqing Association for Artificial Intelligence (CQAAI). He is a Fellow of IRSS, I2CICC, CAAI and CCF.

Prof. Noor Zaman Jhanjhi
Taylor's University, Malaysia

Biography: Professor Dr. Noor Zaman Jhanjhi, often referred to as N.Z. Jhanjhi, holds the esteemed position of Professor in Computer Science with specializations in Cybersecurity and Artificial Intelligence. He currently serves as the Program Director for Postgraduate Research Degree Programmes in Computer Science and Director of the Center for Smart Society (CSS5) at Taylor’s University, Malaysia. Recognized as one of the world’s top 2% research scientists for consecutive years in 2022 and 2023, he is esteemed as one of Malaysia's top three computer science researchers. Notably, he was honoured as an Outstanding Faculty Member by MDEC Malaysia in 2022.
Prof. Jhanjhi boasts a prolific publication record with numerous highly indexed works in WoS/ISI/SCI/SCIE/Scopus, accumulating a collective research impact factor exceeding 1000 points. His Google Scholar H-index stands at an impressive 65, with an I-10 Index approaching 291, and a Scopus H-index of 47. With over 600 publications to his credit, including several international patents in Australia, Germany, the UK, and Japan, Prof. Jhanjhi has significantly contributed to the academic discourse.
An accomplished editor and author, he has curated over 50 research books published by esteemed publishers such as Springer, IGI Global USA, Taylor & Francis, IET, Elsevier, Wiley, Bentham, and Intech Open. Prof. Jhanjhi excels in mentoring postgraduate scholars, with over 38 scholars graduating under his tutelage. He also serves as Associate Editor and Editorial Assistant Board member for reputable journals and has received accolades such as the Outstanding Associate Editor award for IEEE ACCESS.
Renowned as a top-tier reviewer by Publons (Web of Science), Prof. Jhanjhi has evaluated over 60 theses as an external Ph.D./Master thesis examiner for universities worldwide. His extensive academic qualifications span 10 years and encompass accreditation bodies such as ABET, NCAAA, and NCEAC. Prof. Jhanjhi's diverse research interests encompass Cybersecurity, AI, IoT Security, Wireless Security, Data Science, Software Engineering, and Unmanned Aerial Vehicles (UAVs). Additionally, he has been invited as a keynote speaker for over 60 international conferences and has chaired numerous international conference sessions.

Prof. Shuanghua Yang
University of Reading, UK
IET Fellow, IEEE Senior Member

Biography: Shuang-Hua Yang received his BSc degree in instrument and automation and the MSc degree in process control from the China University of Petroleum (Huadong), Beijing, China, in 1983 and 1986, respectively, and the PhD degree in intelligent systems from Zhejiang University, Hangzhou, China, in 1991. He is currently professor and the Head of Department of Computer Science at University of Reading, UK, and the Director of the Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, based at the Southern University of Science and Technology, China. His research interests include cyber-physical systems, the Internet of Things, wireless network-based monitoring and control, and safety-critical systems. He is a fellow of IET and InstMC, UK, and a senior member of IEEE. He was awarded a Doctor of Science, degree, a higher doctorate degree, in 2014 from Loughborough University to recognize his scientific achievement in his academic career. He was awarded the 2010 Honeywell Prize by the Institute of Measurement and Control in the UK in recognition of his contribution to home automation research. He is also an Associate Editor of IET Cyber-Physical Systems: Theory and Applications.

Prof. Zhongfei (Mark) Zhang
University of New York (SUNY) at Binghamton, USA
IEEE Fellow, IAPR Fellow, AAIA Fellow

Biography: Zhongfei (Mark) Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at the University at Buffalo, SUNY, before he joined the faculty of Computer Science at Binghamton University, SUNY. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than thirty inventions, has served as members of the organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships at many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor’s Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.

Dr. Qun Liu
Chief Scientist of Speech and Language Computing
Huawei Noah’s Ark Lab

Biography: Dr. Qun Liu is the Chief Scientist of Speech and Language Computing in Huawei Noah's Ark Lab. He was a Full Professor in Dublin City University and the Theme Leader of the ADAPT Centre, Ireland during July 2012 and June 2018. Before that, he was as a Professor in the Institute of Computing Technology (ICT), Chinese Academy of Sciences for 20 years, where he founded and led the ICT NLP Research Group. He obtained his B.Sc., M.Sc. and Ph.D. in computer science in the University of Science and Technology of China, Chinese Academy of Sciences, and Peking University respectively. His research interests lie in the area of Natural Language Processing and Machine Translation.

Dr. Hoifung Poon
General Manager, Health Futures
Microsoft Research

Biography: Hoifung Poon,Ph.D., is the General Manager at Microsoft Health Futures. His research interest is in developing next-generation AI technology to accelerate progress in access, safety, and preventative care for precision health. At Microsoft, He leads biomedical AI research and incubation, with a particular focus on scaling real-world evidence generation by structuring all medical data. He obtained a B.S. with Distinction in Computer Science from Sun Yat-Sen University, and a Ph.D. in Computer Science and Engineering (my dissertation) from University of Washington. He is an affiliated professor at University of Washington Medical School, and serves as co-PI for various academic projects such as DARPA Big Mechanisms. His past work spans diverse topics in machine learning and NLP, and has been recognized with Best Paper Awards in top conferences such as NAACL, EMNLP, and UAI.

Prof. Dongyan Zhao
Wangxuan Institute of Computer Technology
Peking University, China

Biography: Dongyan Zhao is a professor with the Wangxuan Institute of Computer Technology (WICT), Peking University (PKU), China. He received the BS, MS, and PhD degrees in computer science from the Department of Computer Science and Technology, PKU. He His major research interests include natural language processing, semantic data management and knowledge-based intelligent system.

Prof. Jian Sun
Xi’an Jiaotong University, China

Biography: Jian Sun is a Professor at Xi'an Jiaotong University, where he completed his Ph.D. in Applied Mathematics. His career includes roles as a visiting student at Microsoft Research Asia (Nov. 2005 - March 2008), a postdoctoral researcher at the University of Central Florida (Aug. 2009 - April 2010), and with the Willow team at École Normale Supérieure de Paris / INRIA (Sept. 2012 - Aug. 2014). He serves on the editorial board of the International Journal of Computer Vision (IJCV) and has been an area chair for major conferences such as ICCV, ECCV, and MICCAI. Dr. Sun is a recipient of the National Science Fund for Distinguished Young Scholars in China. His current research focuses on machine learning methods, including generalizable and explainable machine learning, optimal transport, AI applications in mathematics, as well as computer vision and medical image analysis.

Prof. Yue Zhang
Westlake University, China

Biography: Yue Zhang is a tenured Professor at Westlake University. His research interests include NLP and its underlying machine learning algorithms. His major contributions to the field include psycholinguistically motivated machine learning algorithm, learning-guided beam search for structured prediction, pioneering neural NLP models including graph LSTM, and OOD generalization for NLP. He authored the Cambridge University Press book ``Natural Language Processing -- a Machine Learning Perspective''. He is the PC co-chair for CCL 2020 and EMNLP 2022, and action editor for Transactios for ACL. He also served as associate editor for IEEE/ACM Transactions of Audio Speech and Language Processing (TASLP), ACM Transactions on Asian and Low-Resource Languages (TALLIP), IEEE Transactions on Big Data (TBD) and Computer, Speech and Language (CSL). He won the best paper awards of IALP 2017 and COLING 2018, best paper honorable mention of SemEval 2020, and best paper nomination for ACL 2018 and ACL 2023.

Assoc. Prof. Gao Huang
Tsinghua University, China

Biography: Gao Huang is an Associate Professor affiliated with the Department of Automation at Tsinghua University. He obtained the PhD degree in machine learning from Tsinghua in 2015, and spent three years at Cornell University as a postdoc. His research interests lie in machine learning and computer vision. In particular, he is actively working on efficient deep learning, dynamic neural networks, learning with limited data and reinforcement learning. His work on DenseNet won the Best Paper Award of CVPR (2017). He has collected more than 70,000 citations according to Google Scholar.

Invited Speakers

Assoc. Prof. Yanan Sui
Tsinghua University, China

Biography: Yanan Sui is an associate professor at Tsinghua University working on Machine Learning for Control and Dynamical Systems, Neural Engineering, and Robotics. He received undergraduate degree from Tsinghua and Ph.D. degree from Caltech, working with Profs. Mu-ming Poo, Joel Burdick, and Yisong Yue. He was a postdoc with Prof. Fei-Fei Li at Stanford before joining Tsinghua. His research interests include machine learning, neural engineering, robotics, AI-assisted healthcare.

Asst. Prof. Yaodong Yang
Peking University, China

Biography: Dr. Yaodong Yang is the deputy director of Centre for AI Safety and Governance at the Institute for AI, Peking University. Before joining Peking University, he was an assistant professor at King's College London. He studies game theory, reinforcement learning and multi-agent systems, aiming to achieve intelligent decision making, strategic interaction and human value alignment for the coming AGI era. He has maintained a track record of more than 100 publications at top conferences (NeurIPS, ICML, ICLR) and top journals (AIJ, JMLR, PAMI, National Science Review, etc), along with the ICCV'23 best paper award initial list, the CoRL'20 best system paper award, and the AAMAS'21 best blue-sky paper award. He has also been awarded ACM SIGAI China Rising Star and World AI Conference (WAIC'22) Rising Star. He holds a Ph.D. degree from University College London (nominated by UCL for ACM SIGAI Doctoral Dissertation Award), an M.Sc. degree from Imperial College London and a Bachelor degree from University of Science and Technology of China.
演讲题目: 人工智能对齐技术进展

Asst. Prof. Lei Lu
King’s College London & University of Oxford, UK

Biography: Dr. Lei Lu is an Assistant Professor at King’s College London, and a Visiting Research Fellow at University of Oxford. Prior to this, he was a Senior Research Associate at the Institute of Biomedical Engineering, University of Oxford. Dr. Lu’s work focuses on clinical machine learning and computational informatics for healthcare applications. This involves developing multimodal AI and generative model for medical diagnosis, patient phenotyping, health prediction, and biomarker identification. He contributes to the academic community by serving as conference session chair and workshop committee for IJCAI, CIKM, and ICRA. His papers were published in IEEE TPAMI, TCYB, JBHI, TBME, and EHJ-DH. He received the IET J.A. Lodge award in 2021, which presents to one early-career researcher annually with distinction in the UK and abroad.

Asst. Prof. Liangqiong Qu
The University of Hong Kong, Hong Kong S.A.R., China

Biography: Dr. Liangqiong Qu is an Assistant Professor in the Department of Statistics and Actuarial Science and the Institute of Data Science, The University of Hong Kong. Previously, she was a postdoctoral research fellow at Stanford University, working with Prof. Daniel Rubin. Before joining Stanford, she was a postdoctoral research fellow at The University of North Carolina at Chapel Hill, working with Prof. Dinggang Shen. She obtained her joint Ph.D. degree in University of Chinese Academy of Sciences and City University of Hong Kong under the supervision of Prof. Yandong Tang, Prof. Qingxiong Yang, and Prof. Rynson W.H. Lau. Her research interests span the area of artificial intelligence, computer vision and medical imaging processing. More information about Dr. Qu can be found at her personal website: https://liangqiong.github.io/.
Speech Title: Advancing federated learning via Heterogeneity Evaluation, Optimization, and Privacy Preservation
Abstract: Federated Learning (FL) offers a promising solution for training robust deep learning models on large and representative data without sharing it across institutions. Nonetheless, the widespread adoption of FL in healthcare is hindered by two key challenges: (1) The lack of federated learning methods robust to data, device, and state variabilities across sites. Existing approaches for addressing device and state heterogeneities are often evaluated in simulated FL environments, raising concerns about their real-world performance. Additionally, assessing a new FL device/state optimization method’s ability to adapt to varying degrees of such heterogeneity is challenging due to the lack of diverse real-world datasets and quantification metrics. (2) Potential privacy leakage risks through shared model weights and the absence of intuitive tools for securely executing FL algorithms. While advanced privacy preservation FL techniques exist, they usually involve considerable trade-offs between accuracy and utility.
In this talk, we will illustrate how we address the foregoing challenges by establishing a practical and versatile FL platform that integrates real-world evaluation benchmarks, heterogeneous optimization methods, and privacy protection strategies.