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Confirmed Speakers - AIGC 2024
<!doctype html> <html class="no-js" lang="en"> <head> <meta charset="utf-8"> <link rel="shortcut icon" href="favicon.ico"> <title>Confirmed Speakers - AIGC 2024</title> <meta name="keywords" content="Important dates, meeting times, paper submission deadlines, AIGC meeting schedule, important time nodes"> <link rel="stylesheet" href="css\icofonts.css"> <link rel="stylesheet" href="css\select2.min.css"> <link rel="stylesheet" href="css\magnific-popup.css"> <link rel="stylesheet" href="css\owl.carousel.min.css"> <link rel="stylesheet" href="css\bootstrap.min.css"> <link rel="stylesheet" href="css\style.css"> <link rel="stylesheet" href="css\responsive.css"> <style type="text/css"> .STYLE5 { font-size: 18px; font-weight: bold; } .STYLE7 { font-size: 14px } .STYLE8 { font-size: 18px } </style> </head> <body> <script src="js/header.js"></script> </header> </div> <div id="banner-area" class="banner-area" style="background-image: url(images/banner/banner-img1.jfif)"> <div class="container"> <div class="row"> <div class="col-md-12"> <h2 class="banner-title">Confirmed Speakers</h2> </div> </div> <div class="banner-img" id="scene" data-relative-input="true"> <img data-depth="0.5" src="images/banner-pattern.webp" alt="" class="img-fluid"> </div> </div> </div> <section id="tl-service" class="tl-service bg-solid"> <div class="container"> <div class="row justify-content-center"> <div class="col-lg-12"> <div align="center"> <p> <span class="STYLE5"> <strong>Keynote Speakers</strong> <span class="abln">(Alphabetize by Last Name)</span> </span> </p> <p><img src="images/2024ky/Han_Bo.webp" width="200" height="200"></p> <p><strong>Asst. Prof. Bo Han<br>Hong Kong Baptist University</strong></p> <p align="justify"><strong>Biography: </strong>Bo Han is currently an Assistant Professor in Machine Learning and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist of Imperfect Information Learning Team at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), where his research focuses on machine learning, deep learning, foundation models, and their applications. He was a Visiting Research Scholar at MBZUAI MLD (2024), a Visiting Faculty Researcher at Microsoft Research (2022) and Alibaba DAMO Academy (2021), and a Postdoc Fellow at RIKEN AIP (2019-2020). He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019). He has served as Senior Area Chair of NeurIPS, and Area Chairs of NeurIPS, ICML and ICLR. He has also served as Associate Editors of IEEE TPAMI, MLJ and JAIR, and Editorial Board Members of JMLR and MLJ. He received Outstanding Paper Award at NeurIPS, Most Influential Paper at NeurIPS, Outstanding Student Paper Award at NeurIPS Workshop, Notable Area Chair at NeurIPS, Outstanding Area Chair at ICLR, and Outstanding Associate Editor at IEEE TNNLS. <br> <strong>Speech Title: </strong>Exploring Trustworthy Foundation Models under Imperfect Data <br /> <strong>Abstract: </strong> In the current landscape of machine learning, it is crucial to build trustworthy foundation models that can operate under imperfect conditions, since most real-world data, such as unexpected inputs, image artifacts, and adversarial inputs, are easily noisy. These models need to possess human-like capabilities to learn and reason in uncertainty. In this talk, I will focus on three recent research advancements, each shedding light on the reliability, robustness, and safety in this field. Specifically, the reliability will be explored through the enhancement of vision-language models by introducing negative labels, which effectively detect out-of-distribution samples. Meanwhile, robustness will be explored through our investigation into image interpolation using diffusion models, addressing the challenge of information loss to ensure consistency and quality of generated content. Then, safety will be highlighted by our study on hypnotizing large language models, DeepInception, which leverages the creation of a novel nested scenario to induce adaptive jailbreak behaviors, revealing vulnerabilities during interactive model engagement. </p> <p><img src="images/2024ky/gaohuang200.webp" width="200" height="200"></p> <p><strong>Assoc. Prof. Gao Huang<br> Tsinghua University, China</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> </p> <p><img src="images/KN/good/chair/Noor Zaman Jhanjhi200.webp" width="200" height="200"></p> <p><strong>Prof. Noor Zaman Jhanjhi<br> Taylor's University, Malaysia</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> 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.<br> 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.<br> 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.<br> </p> <p><img src="images/2024ky/Hongyang_Li.webp" width="200" height="200"></p> <p><strong>Asst. Prof. Hongyang Li<br>The University of Hong Kong, China<br> The Research Scientist at OpenDriveLab, Shanghai AI Lab</strong></p> <p align="justify"><strong>Biography: </strong>Professor Li is an Assistant Professor in HKU Musketeers Foundation Institute of Data Science and Research Scientist at OpenDriveLab, Shanghai AI Lab. His research focus is on autonomous driving and embodied AI. He proposed the bird’s-eye-view perception work, BEVFormer, that won Top 100 AI Papers in 2022 and was explicitly recognized by Jensen Huang, CEO of NVIDIA and Prof. Shashua, CEO of Mobileye at public keynotes. He served as Area Chair for CVPR 2023, 2024, NeurIPS 2023 (Notable AC), 2024, ACM MM 2024, ICLR 2025, referee for Nature Communications. He will serve as Workshop Chair for CVPR 2026. He is the Working Group Chair for IEEE Standards under Vehicular Technology Society and Senior Member of IEEE. </p> <p><img src="images/2024ky/bingliu200.webp" width="200" height="200"></p> <p><strong>Prof. Bing Liu<br>University of Illinois Chicago (UIC)<br> ACM/AAAI/IEEE Fellow</strong></p> <p align="justify"><strong>Biography: </strong>Bing Liu is a Distinguished Professor and the Peter L. and Deborah K. Wexler Professor of Computing at the University of Illinois Chicago. He earned his Ph.D. in Artificial Intelligence from the University of Edinburgh. His research interests span continual/lifelong learning, lifelong learning dialogue systems, machine learning, and natural language processing. Professor Liu has published extensively in top conferences and journals and authored five books, including two focused on lifelong/continual learning. He has received three Test-of-Time paper awards, one Test-of-Time honorable mention, and some of his work has been widely featured in international media and tech press. He served as Chair of ACM SIGKDD from 2013 to 2017 and as a program chair for numerous leading data mining conferences. Currently, he serves as a program co-chair for the 2025 Conference on Lifelong Learning Agents (CoLLAs-2025). Among his many honors, Professor Liu is the 2018 recipient of the ACM SIGKDD Innovation Award and is a Fellow of ACM, AAAI, and IEEE.<br> <strong>Speech Title: </strong>Continual Learning Using Large Language Models <br /> <strong>Abstract: </strong> The ability to continually learn and accumulate knowledge over a lifetime is a hallmark of human intelligence. It is also essential for AI agents. However, the prevailing machine learning paradigm lacks this crucial capability. This talk introduces the concept of continual learning, outlining its different settings, and then delves into using large language models (LLMs) for continual learning, which notably boosts accuracy. Following this, it presents some recent work on using in-context learning as a strategy for continual learning, which further enhances accuracy and adaptability. </p> <p><img src="images/2024ky/Hoifung Poon200.webp" width="200" height="200"></p> <p><strong>Dr. Hoifung Poon<br> General Manager, Health Futures<br>Microsoft Research</strong></p> <p align="justify"><strong>Biography: </strong>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. <br> </p> <p><img src="images/2024ky/Jian_new200.webp" width="200" height="200"></p> <p><strong>Prof. Jian Sun<br> Xi’an Jiaotong University, China</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> <strong>Speech Title: </strong>生成式人工智能的数学与统计学基础 <br /> <strong>Abstract: </strong>生成式人工智能是当前通用人工智能发展的重要方向，主要通过设计人工智能算法实现对多模态、高维复杂样本分布的学习与新样本的生成，是当前人工智能应用于自动问答、跨模态生成、AI for science等问题的方法基础。生成式人工智能的底层基础是数学与统计学，本报告主要介绍生成式人工智能的背景、数学与统计学基本原理以及其面临的主要挑战问题；进一步介绍以最优传输理论与方法作为基础构建可控／条件生成的人工智能方法，及其在自然图像、医学影像等领域中的应用。最后总结并展望生成式人工智能的未来发展前景。 </p> <p><img src="images/2024ky/guoyin wang.webp" width="200" height="200"></p> <p> <strong> Prof. Guoyin Wang <br> President of Chongqing Normal University, China <br> IRSS/I2CICC/CAAI/CCF Fellow, IEEE SM <br> Vice-President of CAAI <br> </strong> </p> <p align="justify"> <strong>Biography: </strong>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. <br> <strong>Speech Title: </strong>Brain Cognition Inspired Artificial Intelligence <br /> <strong>Abstract: </strong>With the synergy of big data, big computing power and large model, artificial intelligence (AI) has made breakthrough progress in surpassing some key human intelligence abilities such as visual intelligence, auditory intelligence, decision intelligence, and language intelligence in recent years. However, AI systems surpass certain human intelligence abilities in a statistical sense as a whole only. They are not true realization of these human intelligence abilities and behaviors. This talk reviews the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on Marr’s three-layer framework, explores and analyses the limitations of the current development of AI. Future research directions and their scientific issues that need to be focused on in brain-inspired AI research are proposed. </p> <p><img src="images/2024ky/Xingwei Wang.webp" width="199" height="200"></p> <p> <strong>Prof. Xingwei Wang<br>Vice President of Northeastern University, China CCF Fellow<br></strong> </p> <p align="justify"><strong>Biography: </strong>Xingwei Wang is a distinguished professor and doctoral supervisor, holding the esteemed titles of Fellow of CCF, director of CCF, and vice president of Northeastern University. He has been awarded numerous national grants including the National Outstanding Youth Science Foundation of China, the Special Government Allowance from the State Council, and the Program for New Century Excellent Talents of the Ministry of Education. Additionally, Prof. Wang holds prominent positions in various important organizations such as being a member of the National Graduate Education Steering Committee for Professional Engineering Degree and serving as Deputy Director for both Network and Data Communications Committee & Technical Committee on Internet at China Computer Federation; he also serves as Director & Fellow at China Institute of Communications while being part of its fellow selection committee along with being an expert committee member at China Education and Research Network (CERNET) while also serving as Vice Chairman at Liaoning Internet Society. Furthermore, he is an editorial board member for prestigious journals like Chinese Journal Of Computers , Journal Of Software ,and Journal Of Computer Research And Development . Moreover，he is one among Elsevier Highly Cited Chinese Researchers Ranking .He leads Liaoning Provincial Innovation Team besides working as Director at Liaoning Provincial Key Laboratory Of Intelligent Internet Theory And Applications. <br> His primary research interests encompass the domains of Internet, cloud computing, and network space security. To date, he has been bestowed with 2 second prizes for national scientific and technological progress, 2 first prizes for scientific and technological progress from the Ministry of Education, 1 first prize for scientific and technological progress from the China Institute of Communications, 1 second prize for technical invention from the Ministry of Education, 1 second prize for technical invention from Liaoning Province, as well as 1 second prize for natural science from Hunan Province. Additionally, he has published over a hundred papers in esteemed academic journals such as IEEE Transactions while presenting his research at renowned academic conferences like IEEE ICDCS. Moreover, his contributions include over a hundred papers indexed in SCI along with the publication of nine monographs. Furthermore, he has been granted twenty-seven national invention patents and received twenty awards at both national and provincial levels for talent cultivation. </p> <p><img src="images/2024ky/xiaxin200.webp" width="199" height="200"></p> <p> <strong>Dr. Xin Xia<br>Chief Expert of the Software Engineering Application Technology at Huawei, China.<br></strong> </p> <p align="justify"><strong>Biography: </strong>Xin Xia is the Chief Expert of Software Engineering Application Technology at Huawei, China. Before joining Huawei, he was an ARC DECRA Fellow and a lecturer (equivalent to a U.S. assistant professor) at the Faculty of Information Technology, Monash University, Australia. He earned his Ph.D. in June 2014 from the College of Computer Science and Technology, Zhejiang University, China, under the supervision of Prof. Xiaohu Yang and Prof. Jianling Sun. From July 2012 to January 2014, he was a visiting student with Prof. David Lo at Singapore Management University. In 2022, he received the ACM SIGSOFT Early Career Researcher Award. <br /> Xin Xia's current research aims to assist developers and testers in improving their productivity by focusing on data science for software engineering. Specifically, he works on mining and analyzing data from software repositories to uncover valuable and actionable insights. His work employs and customizes a variety of structured and unstructured data analytics techniques, such as data mining, information retrieval, natural language processing, search-based algorithms, and program analysis, transforming passive software engineering data into automated tools and novel insights. <br> <strong>Speech Title: </strong>大模型下的软件工程：进展与挑战 <br /> <strong>Abstract: </strong>软件工程大模型得到了广泛应用，同时也迎来了新的挑战，例如如何让大模型可以更好地理解软件工程业务和知识、如何更好地使能大模型输出安全可信的代码、如何评价大模型在各项软件工程能力的表现等，这也亟需我们重新思考大模型下的软件工程的未来方向。本次报告从实践角度，梳理当前软件工程大模型的挑战，并探讨未来可能的发展方向。 </p> <p><img src="images/2024ky/yang-200-1.webp" width="200" height="200"></p> <p><strong>Prof. Shuanghua Yang<br> University of Reading, UK<br>IET Fellow, IEEE Senior Member</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> </p> <p><img src="images/2024ky/Quanming_Yao.webp" width="200" height="200"></p> <p><strong>Asst. Prof. Quanming Yao<br>Tsinghua University, China</strong></p> <p align="justify"><strong>Biography: </strong> Dr. Quanming Yao currently is a tenure-track assistant professor at Department of Electronic Engineering, Tsinghua University. He was a researcher to a senior scientist in 4Paradigm INC, where he set up and led the company's machine learning research team. He obtained his Ph.D. degree at the Department of Computer Science and Engineering of Hong Kong University of Science and Technology (HKUST). He has published 80+ top conference and journal papers, with more than 10000 citations. He regularly serves as area chairs for ICML, NeurIPS and ICLR. He is also a receipt of National Youth Talent Plan (China), inaugural winner of Ant Intech Prize, Forbes 30 Under 30 (China), Young Scientist Awards (Hong Kong Institution of Science), and Google Fellowship (in machine learning). <br> <strong>Speech Title:</strong> Parsimony Learning from Deep Networks <br> <strong>Abstract:</strong> The scaling law, which involves the brute-force expansion of training datasets and learnable parameters, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of the scaling law is a serious concern for the future of deep learning. In this paper, we address this issue by developing next-generation models in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of relying on the scaling law. This approach allows us to build a framework that uses this knowledge as “building blocks” to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate the application of our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. </p> <p><img src="images/2024ky/Xinwei_Yao.webp" width="200" height="200"></p> <p><strong>Prof. Xinwei Yao<br>Zhejiang University of Technology, China</strong></p> <p align="justify"><strong>Biography: </strong>Dr. Xin-Wei Yao is a Professor in the School of Computer Science and Technology (School of Software Engineering), and the Vice Dean of the Institute for Frontiers and Interdisciplinary Sciences, the director of the Smart Crowd Sensing and Collaboration (SCC) at Zhejiang University of Technology (ZJUT), Hangzhou, China. He is the Senior Member of IEEE, the Distinguished Member of CCF (China Computer Federation), the Senior Member of CAAI (Chinese Association for Artificial Intelligence). His research interests are in the Artificial Intelligence of Things (AIoT), Smart Crowd Sensing and Collaboration, Wireless Nano-Bio-communication Networks, Generalized Artificial Intelligence, Internet of Things (IoT) and so on. <br> </p> <p><img src="images/2024ky/yuezhang200.webp" width="200" height="200"></p> <p><strong>Prof. Yue Zhang<br> Westlake University, China</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> </p> <p><img src="images/2024ky/zhang-200.webp" width="200" height="200"></p> <p><strong>Prof. Zhongfei (Mark) Zhang<br> University of New York (SUNY) at Binghamton, USA<br>IEEE Fellow, IAPR Fellow, AAIA Fellow</strong></p> <p align="justify"><strong>Biography: </strong> Zhongfei (Mark) Zhang is a professor at the School of Computing, 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 the School of Computing at Binghamton University, SUNY. He is the author or co-author of the very first monographs on multimedia data mining and on relational data clustering, respectively. 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 and visiting professorship in Waseda University and 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. <br> <strong>Speech Title: </strong>Uncertainty Analysis for Out-of-distribution Detection <br> <strong>Abstract: </strong>One significant obstacle to deploying deep neural network (DNN) models in real-world applications is that deep learning systems often break down in novel situations which were never seen during the training of the system. This is related to the out-of-distribution detection problem in the literature. Specifically, DNNs tend to yield unreliable predictive estimates and make high-confident yet incorrect predictions when exposed to inputs drawn from unfamiliar distributions. Consequently, accurate predictive uncertainty analysis of DNNs is critical in many high-stake applications such as medical diagnosis, self-driving vehicles, and financial decision-making, where silent mistakes can lead to catastrophic consequences. In this talk, I will first introduce the uncertain analysis issue through a novel uncertainty factorization model as the theoretical foundation for this study. Based on this model, I will then introduce a general and flexible framework for predictive uncertainty estimation with promising evaluation results in several out-of-distribution detection tasks on both vision and language datasets. </p> <p><img src="images/2024ky/dongyanzhao200.webp" width="200" height="200"></p> <p><strong>Prof. Dongyan Zhao<br> Wangxuan Institute of Computer Technology <br>Peking University, China</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> </p> <p> <span class="STYLE5"> <strong>Invited Speakers</strong> <span class="abln">(Alphabetize by Last Name)</span> </span> </p> <p><img src="images/2024ky/lu-200.webp" width="200" height="200"></p> <p> <strong> Asst. Prof. Lei Lu<br> King’s College London & University of Oxford, UK<br> </strong> </p> <p align="justify"><strong>Biography: </strong>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. <br> </p> <p><img src="images/2024ky/qu-200.webp" width="200" height="200"></p> <p><strong>Asst. Prof. Liangqiong Qu<br> The University of Hong Kong, Hong Kong S.A.R., China</strong></p> <p align="justify"><strong>Biography: </strong>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/.<br> <strong>Speech Title:</strong> Advancing federated learning via Heterogeneity Evaluation, Optimization, and Privacy Preservation <br> <strong>Abstract:</strong> 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. <br>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. <br> </p> <p><img src="images/2024ky/yanansui200.webp" width="199" height="200"></p> <p> <strong>Assoc. Prof. Yanan Sui<br> Tsinghua University, China<br></strong> </p> <p align="justify"><strong>Biography: </strong> Yanan Sui (YananSui.com), Associate Professor at Tsinghua University, is dedicated to the research of human neuro-musculo-skeletal modeling and control, with applications in embodied intelligence and brain-machine interaction. He received his B.S. from Tsinghua University, his Ph.D. from Caltech, and did postdoctoral work at Caltech and Stanford University. His work on safe optimization has been included in textbooks at Stanford and other universities. He co-won the Best Conference Paper Award and the Best Paper Award on Human-Robot Interaction at the 2020 International Conference on Robotics and Automation. His work has been successfully applied to the clinical treatment of neural injuries in China and the United States. He has served as area chair of AI conferences, including AAAI, AISTATS, ICLR, ICML, NeurIPS. For his contribution to the interdisciplinary field of artificial intelligence and neural engineering, he was selected as one of MIT Technology Review's Innovators Under 35 in China. <br> <strong>Speech Title: </strong>Self Model for Embodied Intelligence: Modeling and Control of Full-Body Human Musculoskeletal System <br> <strong>Abstract: </strong>Modeling and control of the human musculoskeletal system is important for understanding human motor function, developing embodied intelligence, and optimizing human-robot interaction systems. However, current models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of whole-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art whole-body control. We validate the effectiveness of our model and algorithm in simulations using real human locomotion data. This work promotes a deeper understanding of human motion control and better design of interactive robots. </p> <p><img src="images/2024ky/yaodongyang200.webp" width="200" height="200"></p> <p><strong>Asst. Prof. Yaodong Yang<br> Peking University, China</strong></p> <p align="justify"><strong>Biography: </strong>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.<br> <strong>Speech Title:</strong> 人工智能对齐技术进展 <br> </p> </div> </div> </div> </div> </section> <footer id="tl-copyright" class="tl-copyright"> <div class="container"> <div class="row"> <div class="col-lg-3"> <p class="copyright-text">©2024 AIGC. 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