2023年第九届IEEE云计算与智能系统国际会议（以下简称“CCIS 2023”）由中国人工智能学会和IEEE北京分会联合主办，将于8月12-13日在大理举办。
IEEE云计算与智能系统国际会议由中国人工智能学会联合国际电气与电子工程师协会（IEEE）北京分会发起，已陆续在北京、杭州、深圳、香港、南京、新加坡、西安和成都成功举办八届。该会议旨在对云计算、人工智能的前沿技术和热点问题进行深入研究和探讨，以促进相关技术和产业的发展。
南达科他大学计算机系主任KC Santosh教授；CAAI生物信息学与人工生命专委会主任、清华大学自动化系张学工教授；南京大学Kai Ming Ting教授；中国平安保险股份有限公司首席科学家肖京先生将受邀出席CCIS 2023作主旨报告。
嘉宾介绍
KC Santosh
Bio: KC Santosh – the chair of the Department of Computer Science, University of South Dakota –is a highly accomplished AI expert with an impressive background. He earned his PhD in Computer Science - Artificial Intelligence from INRIA Nancy GrandEst Research Centre (France) and worked as a research fellow at the National Institutes of Health. With funding of over $3 million, including a $1 million grant from DEPSCOR - 2023 for AI/ML capacity building at USD, he has authored 9 books and published more than 240 peer-reviewed research articles. Prof. Santosh's areas of expertise include artificial intelligence, machine learning, computer vision, data mining, and big data. He is also an editor of multiple prestigious journals, including IEEE Transactions on AI and Int. J of Machine Learning & Cybernetics. As the founder of AI programs at USD, he has taken significant strides to increase enrollment in the graduate program, resulting in over 2,000% growth in just two years. His leadership has helped to build multiple inter-disciplinary AI/Data Science related academic programs, including collaborations with Biology, Physics, Biomedical Engineering, Sustainability and Business Analytics departments. Prof. Santosh is highly motivated in academic leadership, and his contributions have established USD as a pioneer in AI programs within the state of SD. More info. https://kc-santosh.org/
Keynote Title: Big Data Issues, No Worries – Active Learning is the Need
Xuegong Zhang
Bio: Xuegong Zhang received his BS degree in Industry Automation in 1989 and his Ph.D. degree in Pattern recognition and Machine Intelligence in 1994, both from Tsinghua University, after which he joined the faculty of Tsinghua University. He had visited Harvard School of Public Health in 2001-2002, and is now a Professor of Pattern Recognition and Bioinformatics in the Department of Automation, Tsinghua University, and Adjunct Professor of the School of Life Sciences and School of Medicine. He is ISCB Fellow and CAAI Fellow. He is also the chairman of the Committee of Bioinformatics and Artificial Life, Chinese Association of Artificial Intelligence, and the chairman of the Committee of Intelligent Health and Bioinformatics, Chinese Association of Automation. His major research interests include machine learning, bioinformatics, human cell atlas, and intelligent precision medicine.
Keynote Title: From Human Ensemble Cell Atlas to Digital Life Systems and AI Precision Medicine
Abstract：
The rapid development of high-throughput single-cell biology is driving toward the comprehensive profiling of characteristic molecular properties of all major cell types of the human body. The seemingly unlimited ability of foundation language models has made people to expect the emerging of artificial general intelligence (AGI). These two trends lead to an unprecedented anticipation on the development of AI medicine and on solving major health issues using AI medicine. But there lacks a clear route toward this ambition. Based on our practices in building the first human ensemble cell atlas (hECA) and developing AI foundation models for single-cell transcriptomics, we proposed the concept of Digital Life Systems or dLife as a framework for integrating and representing all information of a living human body from molecular, cellular levels to tissues, organs, systems, and the whole body. As a proof of concept, we developed the method of “in data” cell experiments for drug effects prediction and for virtual trials. We propose that the route to future AI precision medicine is to build foundation models of patients or AI patients as digital twins of real patients based on the dLife framework.
Kai Ming Ting
Bio: After receiving his PhD from the University of Sydney, Australia, Kai Ming Ting worked at the University of Waikato (NZ), Deakin University, Monash University and Federation University in Australia. He joined Nanjing University in 2020.Research grants received include those from National Science Foundation of China, US Air Force of Scientific Research (AFOSR/AOARD), Australian Research Council, Toyota InfoTechnology Center and Australian Institute of Sport.
He is the principal driver of isolation-based methods, and a key originator of Isolation Forest, Isolation Kernel and Isolation Distributional Kernel. Isolation Forest is widely used in industries and academia. Isolation Kernel is a unique similarity measure which is derived from a dataset based on the same/similar isolation mechanism as Isolation Forest, and has no closed-form expression. Isolation Kernel and Isolation Distributonal Kernel are the X-factor that enables many problems to be solved more effectively and efficiently than existing algorithms which rely on Gaussian kernel or Euclidean distance.
Keynote Title: Distributional Kernels: an under-utilized resource
Abstract：
This talk presents recent works on distributional kernels based on kernel mean embedding (KME). KME has a strong theoretical underpinning, and guarantees that the resultant kernel mean map is injective, i.e., the kernel mean maps of two distributions have their difference equals to zero if and only if the distributions are the same. Yet, KME's applications have been less successfully so far. One key breakthrough is the identification of the root cause of KME's (seemingly) failures, i.e., the use of Gaussian kernel. The talk presents works, following this identification, that release the power of this under-utilized resource. The works demonstrate that the distributional kernels can solve long-standing problems, some of which have evaded decades of effort, in terms of efficiency and task-specific accuracy issues. These include point and group anomaly detections, clustering, and anomaly detections in trajectories, periodic time series and graphs/networks.
Dr. Xiao Jing
Bio: Dr. Xiao Jing is the Group Chief Scientist of Ping An Insurance Company of China, Ltd. He leads research and development in AI-related technologies and their applications in finance, healthcare, and smart cities. He received his PhD degree from the School of Computer Science at Carnegie Mellon University. He has published over 215 academic papers and was granted 101 US patents. Before joining Ping An, he worked as Principal Applied Scientist Lead at Microsoft and as Manager of the Algorithm Group at Epson Research and Development, Inc. Dr. Xiao Jing began his work in R&D in artificial intelligence and related fields in 1995, covering a broad range of application areas such as healthcare, autonomous driving, 3D printing and display, biometrics, web search, and finance.
Keynote Title: AI Empowered Financial Services
随文附件
附件2. Registration-cn - nopaper.doc
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