Confirmed Speakers
AIGC 2025 Keynote Speakers
(Alphabetize by Last Name)
Prof. Yi Cai
South China University of Technology, China
Dean of Software Engineering
Biography: Prof. Cai Yi, is the Dean of Software Engineering in SCUT, the Director of The China Ministry of Education Key Laboratory of Big Data and Robotic Intelligence. He had received his PhD degree in the Chinese University of Hong Kong, and work as postdoctoral fellow in City University of Hong Kong. Moreover, he is CCF Distinguish member, member of Technical Committee on Databases, member of the Technical Committee on NLP Technology, member of the Technical Committee on Social Media Processing of CIPS. Prof. Cai has published more than 170 high quality papers in top journals and conferences such as IEEE TKDE, TMM, TAFFAC, IEEE/ACM TASLP, AAAI, ACL, ACM MM, including ESI highly cited papers. Prof. Cai also had published 2 academic monographs, hosted 3national projects and 7 provincial projects, and acts as the Chairman and Program Committee members of more than 20 prestigious international academic conferences.
Prof. Ge Li
Peking University, China
Biography: Ge Li is a Full Professor with Tenure at Peking University, a doctoral supervisor, and a Yangtze River Scholar Program Member. His research interests include Code Generation, Code Comprehension, and Deep Learning Technology. He is known as a pioneer researcher in "Code Generation and Comprehension based on Deep Learning." Many of his works have been widely cited as "pioneering achievements" around the world. His research team has been keeping the achievements of SOTA on many research issues, and is honored as the world's outstanding research team. aiXcoder, an Intelligent programming robot developed by his team, provides services for hundreds of thousands of developers and many world-famous enterprises.
Dr. Li is the secretary general of CCF Software Engineering Society and the founder of the Software Program Generation Study Group, which includes over 100 advanced researchers in China. Dr. Li has published more than 50 research papers, and many of them are published in top-tier conferences and journals, such as AAAI, ACL, ASE, ICSE, EMNLP, CIKM, COLING and JSS. He has served as in the Technical Program Committee of many international conferences including KSEM, ICSR, UIC, ICeBE etc. He was awarded the first prize of Science and Technology Progress Award from the Department of the Ministry of Education. His undergraduate course, Introduction to Computing, was the first computer MOOC course from mainland China on Coursera, and was awarded National Excellent Course. He was also awarded Peking University Teaching Excellence Award, Beijing Educational Innovation Model, First prize of the Association of Computer Education in Beijing Colleges and Universities, First prize of Teaching Skills Competition of Beijing Universities. His current research mainly concerns applications of probabilistic methods for machine learning, including Program Language Processing, Natural Language Processing, and Software Engineering.
Prof. An-An Liu
Tianjin University, China
IET Fellow
Biography: Prof. An-An Liu is the Director of the Institute of Television and Image Information, Ministry of Education of the People's Republic of China. He has reached impressive achievements for multimedia content analysis, retreival, generation and communication with continuous contributions to cross-media representation learning and understanding, human action recognition, 3D model retrieval and classification, social network analysis, bio-medical image analysis, marine big data analysis by developing algorithms, building new datasets, organizing contests, and build benchmarks. He has published 100+ papers in the first-tier venues. He holds 40+ Chinese and U.S. patents. Many novel techniques have been widely utilized for multimodal big data analysis for social network, smart city, and maritime space.
Prof. Rustam Shadiev
Zhejiang University, China
Biography: Dr. Rustam Shadiev is a tenured professor at the College of Education, Zhejiang University. He has an accomplished academic career distinguished by significant contributions to the field of educational technology. He earned his PhD from Taiwan Central University in 2012, followed by a postdoctoral fellowship at Taiwan Cheng Kung University. Before joining Zhejiang University, he served as a professor in the School of Education Science at Nanjing Normal University for six years, until 2022. In recognition of his academic excellence, he was awarded the title of Distinguished Professor of Jiangsu Province in 2019. Dr. Shadiev is a Fellow of the British Computer Society (BCS) and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). His outstanding research achievements have earned him recognition as one of the Most Cited Chinese Researchers by Elsevier, SCOPUS, and Shanghai Ranking for four consecutive years (2020–2023). Additionally, he was ranked among Stanford/Elsevier’s Top 2% Scientists in both 2023 and 2024. A prolific scholar, Dr. Shadiev has authored over 200 publications in prestigious academic journals and conferences. His research focuses on advanced learning technologies, particularly their applications in language learning and cross-cultural education. His work continues to influence global educational practices and research. Dr. Shadiev is an editorial board member of several esteemed journals, including the Journal of Educational Technology & Society, the Journal of Computer Assisted Learning, and Smart Learning Environments. He also serves as a guest editor for special issues in journals such as Educational Technology & Society, and Discover Education.
Asst. Prof. Yi Wu
Tsinghua University, China
Biography: Yi Wu, Assistant Professor at Institute of Cross-Information Studies, Tsinghua University, was a full-time researcher at OpenAI before returning to China, with research interests in deep reinforcement learning, multi-intelligent body learning, inference models, human-computer interaction, etc. He received his Ph.D. degree from the University of California, Berkeley, USA, under the supervision of Prof. Stuart Russell in 2019; and he received his undergraduate degree from the Institute of Cross-Information Studies, Tsinghua University, China, in 2014. Experimental Class of Computer Science (Yao class), representative works include reinforcement learning generalizability early work Value Iteration Network, multi-intelligence body learning highest citation algorithm MAPPO/MADDPG, OpenAI multi-intelligence body hide-and-seek project, etc., and has also won the top conference NIPS2016 best paper award and the ICRA2024 best demo award finalist.
Prof. Wei Xu
Tsinghua University, China
Associate Dean of the Institute for Interdisciplinary Information Sciences
Biography: Wei Xu is a professor at the Institute for Interdisciplinary Information Sciences at Tsinghua University in Beijing. His research interests broadly cover distributed system design, big data, and financial technology. His current projects focus on privacy-preserving computation, data center networking, large-scale systems for machine learning and data mining, as well as various big data applications. He received his Ph.D. from the University of California, Berkeley in 2010, where he was a member of the RAD Lab in the EECS Department. His advisors were Prof. David Patterson and Prof. Armando Fox, and his dissertation focused on analyzing free-text console logs for problem detection. Before joining Tsinghua, he worked as a software engineer at Google for two and a half years.
Prof. Zhiwen Yu
South China University of Technology, China
Biography: Zhiwen Yu is a Professor in School of Computer Science and Engineering, South China University of Technology, China. He received the Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2008. Dr. Yu has authored or coauthored more than 200 refereed journal articles and international conference papers, including more than 80 articles in the journals of IEEE Transactions, h-index 49，Google citation 12000. He is an Associate Editor of the IEEE Transactions on systems, man, and cybernetics: systems, and the informatics journal. Dr. Yu is in charge of or take part in more than 30 research projects, such as the National Natural Science Foundation of China (the Key Program、the General Program and the Youth Program), National Natural Science Foundation of China for Excellent Young Scientists, the Key R&D Program of Guang Dong Province, and so on. He is a senior member of IEEE and ACM, a Member of the Council of China Computer Federation from 2016 to 2023 (CCF).
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.
Speech Title: 生成式人工智能的数学与统计学基础
Abstract: 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。
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.
Speech Title: Brain Cognition Inspired Artificial Intelligence
Abstract: 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.
Prof. Xingwei Wang
Vice President of Northeastern University, China
CCF Fellow
Biography: 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.
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.
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.
Speech Title: LLM reasoning and generalization
Abstract: In this talk, I will discuss linguistic reasoning, and the
capabilities of formal logic reasoning for large langauge models (LLMs). I will discuss
the difficulty of learning formal reasoning from empirical risk minimization, and
discuss a perspective to this problem from causal learning theory. I will discuss causal
features and confounders, and show how learning confounders can lead to low
out-of-distribution generalization performance. Then I will discuss two general methods
to address the issue, including a data-centric method and a model-centric method,
introducing several recent works using both methods.
Asst. Prof. Bo Han
Hong Kong Baptist University
Biography: 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.
Speech Title: Exploring Trustworthy Foundation Models under Imperfect
Data
Abstract: 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.
Prof. Songlin Hu
Institute of Information Engineering (IIE), the Chinese
Academy of Sciences, China
Biography: Songlin Hu is a full professor at the
Institute of Information Engineering (IIE), the Chinese Academy of Sciences. He is also
a joint professor at the University of Chinese Academy of Sciences. His research areas
include big data, natural langurage processing, knowledge graph, etc. He has published
more than 100 publications in many reputed conferences and journals, like
ACL,AAAI,IJCAI,EMNLP, SIGMOD,VLDB,ICDE, ACM/IEEE Trans, etc.
Speech Title: 大模型安全治理
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.
Speech Title: 面向长序列的Transformer基础架构
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.
Speech Title: Cybersecurity Issues and Challanges in the Era of
Generative AI
Asst. Prof. Hongyang Li
The University of Hong Kong, China
Biography: 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.
Speech Title: Achilles' Heel in Manipulation: Key Recipe and Missing
Pieces towards Intelligent Embodied AI
Abstract: The increasing demand for versatile robotic systems to
operate in diverse and dynamic environments has emphasized the importance of a
generalist policy, which leverages a large cross-embodiment data corpus to facilitate
broad adaptability and high-level reasoning. However, the generalist would struggle with
inefficient inference and cost-expensive training. The specialist policy, instead, is
curated for specific domain data and excels at task-level precision with efficiency.
Yet, it lacks the generalization capacity for a wide range of applications. Inspired by
these observations, we introduce RoboDual, a synergistic dual-system that supplements
the merits of both generalist and specialist policy. A diffusion transformer-based
specialist is devised for multi-step action rollouts, exquisitely conditioned on the
high-level task understanding and discretized action output of a vision-language-action
(VLA) based generalist. Compared to OpenVLA, RoboDual achieves a 12% improvement on
CALVIN and 26.7% in real-world by adapting the specialist policy with 20M trainable
parameters only. It maintains strong performance with merely 5% of demonstration data,
and enables a 3.8 higher control frequency in real-world deployment. Code and models
would be made publicly available.
Prof. Bing Liu
University of Illinois Chicago (UIC)
ACM/AAAI/IEEE Fellow
Biography: 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.
Speech Title: Continual Learning Using Large Language Models
Abstract: 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.
Assoc. Prof. Liang Pang
CAS Key Laboratory of AI Safety, Institute of
Computing Technology, Chinese Academy of Sciences, China
Biography: Liang Pang, an associate researcher at the
CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of
Sciences, and a visiting scholar at the National University of Singapore, specializes in
research areas of natural language generation and information retrieval. He has
published over 60 papers at international conferences and has accumulated more than 3000
citations on Google Scholar. Pang serves as a program committee member for international
conferences, a reviewer for academic journals, a standing committee member of the
Information Retrieval Special Committee of the Chinese Information Processing Society,
the deputy director of the Youth Working Committee of the Chinese Information Processing
Society, and a member of the Youth Innovation Promotion Association of the Chinese
Academy of Sciences. He has been honored with the Outstanding Doctoral Dissertation
Award from the Chinese Information Processing Society, Best Paper Runner-up Award at
CIKM, and received the Best Paper Hornerable Mentioned Award at SIGIR. His proposed deep
text matching model achieved a global ranking of fourth in the Kaggle QQP Text Matching
competition. He was the global champion in reinforcement learning at the NeurIPS 2018
Multi-Agent Challenge. His team topped the global leaderboard in the multi-hop
open-domain question answering challenge HotpotQA.
Speech Title: 检索增强大模型前沿技术与社会影响
Abstract: 近年来，检索增强大模型的范式有效地提升了大语言模型生成内容的准确性和可信性，基于检索增强大模型的流程我们可以从四个视角来讨论。在信息检索模块的视角，如何构建适用于大模型的检索模块，有助于大模型更高效的筛选出对生成有效的信息；在大语言模型模块的视角，如何教会大模型使用外部信息，有助于避免检索噪声信息对生成影响；在模块间交互的视角，如何设计信息检索模块与大语言模型模块交互配合的机制，有助于将内部参数知识与外部语料库知识充分融合；最后，在信息回路的视角，讨论智能生成内容将对信息检索内容生态造成的潜在影响。
Dr. Hoifung Poon
General Manager, Health Futures
Microsoft
Research
Biography: Hoifung Poon is the General Manager at Health
Futures in Microsoft Research and an affiliated faculty at the University of Washington
Medical School. He leads biomedical AI research and incubation, with the overarching
goal of structuring medical data to optimize delivery and accelerate discovery for
precision health. His team and collaborators are among the first to explore large
language models (LLMs) and multimodal generative AI in health applications, producing
popular open-source foundation models such as PubMedBERT, BioGPT, BiomedCLIP, LLaVA-Med,
BiomedParse. His latest publication in Nature features GigaPath, the first whole-slide
digital pathology foundation model pretrained on over 1 billion pathology image tiles.
He has led successful research partnerships with large health providers and life science
companies, creating AI systems in daily use for applications such as molecular tumor
board and clinical trial matching. He has given tutorials on these topics at top AI
conferences such as ACL, AAAI, and KDD, and his prior work has been recognized with Best
Paper Awards from premier AI venues such as NAACL, EMNLP, and UAI. He received his PhD
in Computer Science and Engineering from the University of Washington, specializing in
machine learning and NLP.
Speech Title: Advancing Health at the Speed of AI
Abstract: The dream of precision health is to develop a data-driven,
continuous learning system where new health information is instantly incorporated to
optimize care delivery and accelerate biomedical discovery. The confluence of
technological advances and social policies has led to rapid digitization of multimodal,
longitudinal patient journeys, such as electronic medical records (EMRs), imaging, and
multiomics. Our overarching research agenda lies in advancing multimodal generative AI
for precision health, where we harness real-world data to pretrain powerful multimodal
patient embedding, which can serve as digital twins for patients. This enables us to
synthesize multimodal, longitudinal information for millions of cancer patients, and
apply the population-scale real-world evidence to advancing precision oncology in deep
partnerships with real-world stakeholders such as large health systems and
pharmaceutical companies.
Dr. Xian Wu
Director of Tencent Youtu Lab Jarvis Research Center
Biography: Xian Wu received the PhD degree from Shanghai
Jiao Tong University. He is now a principal researcher with Tencent. Before joining
Tencent, he worked as a senior scientist manager and a staff researcher with Microsoft
and IBM Research. His research interests include medical AI, natural language processing
and multi-modal modeling. He has published papers in CVPR, NeurIPS, ACL, WWW, AAAI,
IJCAI etc. He also served as PC member of IEEE Transactions on Knowledge and Data
Engineering, ACM Transactions on Knowledge Discovery from Data, ACM Transactions on
Information Systems, ACM Transactions on Intelligent Systems and Technology, CVPR, ICCV,
AAAI etc.
Speech Title: 从深度学习到大模型，医学AI上的一些尝试
Dr. Xin Xia
Chief Expert of the Software Engineering Application Technology
at Huawei, China.
Biography: 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.
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.
Speech Title: 大模型下的软件工程：进展与挑战
Abstract: 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。
Prof. Jungang Xu
University of Chinese Academy of Sciences, China
Director of Cloud Computing and Intelligent Information Processing Laboratory
Biography: Jungang Xu, Professor and doctoral supervisor
of University of Chinese Academy of Sciences, Director of Cloud Computing and
Intelligent Information Processing Laboratory, and chief Professor of Deep Learning
Course of University of Chinese Academy of Sciences. His research interests include
multimodal intelligence, intelligent decision and optimization, embodied intelligence,
etc. He is the Expert in the National Science and Technology expert Database, the expert
of the Ministry of Industry and Information Technology of China, the expert of the
Beijing Municipal Science and Technology Commission and Administrative Commision of
Zhongguancun Science Park. He is the executive member of the Special Committee on
Artificial Intelligence and Pattern Recognition, executive member of the Special
Committee on Natural Language Processing, executive member of the Special Committee on
Database in China Computer Federation, and standing member of the Special Committee on
Intelligent Service of the Chinese Association for Artificial Intelligence. He has
presided over a number of scientific research projects, such as National Key Technology
Research and Development Program, National Natural Science Foundation, Beijing Science
and Technology Plan, and Beijing Natural Science Foundation, and published more than 100
articles. He won the second prize of China Geographic Information Technology Progress
Award in 2022.
Speech Title: 大模型的发展趋势与应用
Assoc. Prof. Cheng Yang
Beijing University of Posts and Telecommunications, China
Biography: Cheng Yang received the BE and PhD degrees
from Tsinghua University, in 2014 and 2019, respectively. He is currently an associate
professor with the Beijing University of Posts and Telecommunications. His research
interests include natural language processing and network representation learning.
Speech Title: 大语言模型智能体高效协作框架
Abstract:大语言模型(LLMs)目前已展现出推理、规划、工具使用等诸多类人智能，可作为智能体(Agent)的大脑自动化地处理各种复杂任务。然而这些大语言模型智能体是否能够像人类一样学会沟通与分工，更快更好地进行任务协作，仍然是一个亟待探索的问题。本报告将介绍大语言模型智能体协作研究的最新进展，并分析实验中发现的各类智能体合作涌现行为。
Prof. Shuanghua Yang
University of Reading, UK
IET Fellow, IEEE Senior
Member
Biography: Shuang-Hua Yang is currently a professor and
the Head of Department of Computer Science at the University of Reading, the UK and the
Director of Shenzhen Key Laboratory of Safety and Security for Next Generation of
Industrial Internet, China. He was selected as a member of European Academy of Sciences
and Arts in 2024, and awarded DSc from Loughborough University in 2014 to recognize his
academic contribution to wireless monitoring research. He is a Fellow of IET and a
Fellow of InstMC, U.K. His current research interests include cyber-physical system
safety and security, and industrial Internet of Things.
Speech Title: Comprehensive Knowledge Integration for Multivariate Time
Series Anomaly Detection with Multi-view learning
Abstract: Anomaly detection in the Industrial Internet of Things (IIoT)
is a challenging task that hinges on the effective learning of multivariate time series
representations. Despite the intricate spatial and temporal relationships inherent in
IIoT systems, existing methods primarily extract features from a single domain—either
temporal or spatial (sensor-wise)—or simply combine the two sequentially, limiting their
anomaly detection capabilities.
To address these limitations, this talk introduces the Spatial-Temporal Association
Discrepancy (STAD) component, which leverages the discrepancies between spatial and
temporal features to enhance latent representation learning. Specifically, we propose
the Skip-Patching Spatial-Temporal Anomaly Detection (SSAD) framework, which integrates
spatial and temporal features in a diverse and comprehensive manner, significantly
improving learning processes. Furthermore, we present a novel framework called Two-Views
Pre-train Anomaly Detection (2ViewsAD), designed to enhance both the generalization and
robustness of learned representations.
The SSAD framework demonstrates superior performance, validating the effectiveness of
combining skip-patching techniques with spatial-temporal features to improve anomaly
detection in IIoT systems. Meanwhile, 2ViewsAD utilizes self-supervised learning during
pre-training, effectively capturing both temporal and spatial (sensor-wise) features.
This dual-view strategy enables the model to seamlessly integrate insights from both
perspectives, further boosting detection capabilities. Experimental results confirm that
2ViewsAD achieves state-of-the-art anomaly detection performance.
Asst. Prof. Quanming Yao
Tsinghua University, China
Biography: 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).
Speech Title: Parsimony Learning from Deep Networks
Abstract: 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.
Prof. Xinwei Yao
Zhejiang University of Technology, China
Biography: 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.
Speech Title: 群体智能感算一体化关键技术及应用
Prof. Xindong You
Beijing Information Science & Technology University,
China
Biography: Xindong You is a Professor at Beijing Information Science
and Technology University, she is a member of both the Natural Language Processing
Professional Professional Committee and the Information Storage Committee of China
Computer Federation (CCF). She has presided near 20 research projects, including the
National Natural Science Foundation of China, the National Defense Basic Strengthening
Research Program, the Beijing Natural Science Foundation General Program, the Equipment
Pre-research Key Laboratory Fund Project, industry-commissioned horizontal projects, the
Zhejiang Provincial Natural Science Foundation, and the China Postdoctoral Science
Foundation General Program. She has also participated as a key member in over 10
research projects, such as the 973 National Key Research and Development Program, the
Ministry of Science and Technology's Support Program, the National Natural Science
Foundation of China, Zhejiang Provincial Major Special Projects, Zhejiang Provincial
Natural Science Foundation Projects, and the Humanities and Social Sciences Research
Program funded by the Ministry of Education. She has published more than 30 papers in
domestic and international journals as the first author or corresponding author, with
three papers included in the TOP journals of the first quartile in Chinese Academy of
Sciences' journal ranking. Additionally, She has authored one academic monograph
independently, which was published with support from the China Postdoctoral Excellent
Academic Monograph Publication Fund by Science Press.
Speech Title: Exploration of Key Technologies and Field Applications of
Knowledge Graphs
Abstract: The technical system of symbolic knowledge graphs serves as
an effective complement to large models, providing support for accurate domain knowledge
and complex reasoning capabilities for the industrial implementation of large models.
The combination of domain-specific large models and domain-specific knowledge graphs can
become an important means for the application of artificial intelligence in various
fields. This report mainly discusses the past, present, and future development trends of
knowledge graphs, the key technologies for constructing knowledge graphs and their main
application scenarios, as well as the research group's application in the fields of
weaponry and equipment, coal mine electromechanical equipment, interpretability of image
classification, and the entire Mini/Micro LED industry chain, and vision future
prospects for the application of the knowledge graph.
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 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.
Speech Title: Uncertainty Analysis for Out-of-distribution Detection
Abstract: 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.
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.
Speech Title: 基于大规模语言模型的智能问答
AIGC 2024 Invited Speakers (Alphabetize by Last Name)
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.
Speech Title: Deep Learning for Advancing Cardiovascular Healthcare
Abstract: Electrocardiogram (ECG) is widely considered the primary test
for evaluating cardiovascular diseases. However, the use of AI models to advance these
medical practices and learn new clinical insights from ECGs remains largely unexplored.
Utilising a data set of 2.3 million ECGs collected from patients with 7 years follow-up,
we developed a DNN model with state-of-the-art granularity for the interpretable
diagnosis of cardiac abnormalities, gender identification, and hypertension screening
solely from ECGs, which are then used to stratify the risk of mortality. Our model
demonstrated cardiologist-level accuracy in interpretable cardiac diagnosis, and the
potential to facilitate clinical knowledge discovery for gender and hypertension
detection which are not readily available. In addition, we explored the design of
optimal DNN models through of a novel Neural Architecture Search (NAS) approach, which
was able to find networks outperformed the state-of-the-art models with fewer than 5%
parameters.
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.
Assoc. Prof. Yanan Sui
Tsinghua University, China
Biography:
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 for leading AI
conferences. 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.
Speech Title: Self Model for Embodied Intelligence
Abstract: 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.