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徐鹰，南方科技大学医学院讲席教授、国家特聘专家、教育部特聘教授、计算系统生物学及生物信息学者。2023年1月加入南方科技大学医学院。之前为美国佐治亚大学生物化学系“校董事会教授”及“佐治亚科学协会著名学者讲席教授”(2003-2022)，佐治亚大学生物信息研究所首任所长(2003-2011)。

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Prof. Zhong's present research interests include Web Intelligence (WI), Brain Informatics (BI), Data Mining, Granular Computing, and Intelligent Information Systems. In 2000 and 2004, Zhong and colleagues introduced WI and BI as new research directions, respectively. Currently, he is focusing on "WI meets BI" research with three aspects: (1) systematic investigations for complex brain science problems; (2) BI studies based on WI research needs; and (3) new information technologies for supporting systematic brain science studies. The synergy between WI and BI advances our ways of analyzing and understanding of data, information, knowledge, wisdom, as well as their interrelationships, organizations, and creation processes, to achieve human-level Web intelligence reality. In 2010, Zhong and colleagues extended such a vision to develop Wisdom Web of Things (W2T) as a holistic framework for computing and intelligence in the big data era. Recently Zhong and colleagues have been working on brain big data based wisdom service project, in which the fundamental issues include how brain informatics based big data interacts in the social-cyber-physical space of the W2T and how to realize human-level collective intelligence as a big data sharing mind, a harmonized collectivity of consciousness on the W2T that uses brain-inspired intelligent technologies to provide wisdom services.

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Director of Major projects, National High-Tech Research and Development program (also known as the 863 project), Ministry of Science and Technology Second prize, National Natural Science Award, 2012。

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Dr. Ming Dong is an associate professor of Department of Mechanics, Tianjin university. He is also a Marie Curie research Fellow in Department of Mathematics, Imperial College London during 2016.2-2018.1. His main research interests include Fluid dynamics, Applied Mathematics and related engineering applications. He is currently looking for MSE/PhD students who are interested in fundamental science and mathematics.

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Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells for all their genes. While those measurements provide high-dimensional gene expression profiles for all cells, it requires fixing individual cells that lose many important spatiotemporal information. Is it possible to infer temporal relationships among cells from single or multiple snapshots? How to recover spatial interactions among cells, for example, cell-cell communication? In this talk I will present our newly developed computational tools that are mostly based on dynamical models and machine-learning methods, with a focus on inference and analysis of transitional properties of cells and cell-cell communication using both high-dimensional single-cell and spatial transcriptomics, as well as multi-omics data for some cases. Through their applications to various complex systems in development, regeneration, and diseases, we show the discovery power of such methods in addition to identifying areas for further method development for spatiotemporal analysis of single-cell data.

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Dr. Qing Nie is a University of California Presidential Chair and a Distinguished Professor of Mathematics and Developmental & Cell Biology at University of California, Irvine. Dr. Nie is also a University of California Presidential Chair, and the director of the NSF-Simons Center for Multiscale Cell Fate Research jointly funded by NSF and the Simons Foundation – one of the four national centers on mathematics of complex biological systems. In research, he uses systems biology and data-driven methods to study complex biological systems with focuses on single-cell analysis, multiscale modeling, cellular plasticity, stem cells, embryonic development, and their applications to diseases. Dr. Nie has published more than 200 research articles, including several papers in Nature, Nature Methods, and Nature Machine Intelligence. In training, Dr. Nie has supervised more than 60 postdoctoral fellows and PhD students, with many of them working in academic institutions. Dr. Nie is a fellow of the American Association for the Advancement of Science (AAAS), American Physical Society (APS), Society for Industrial and Applied Mathematics (SIAM), and American Mathematical Society (AMS).

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I will introduce our developed full-stack, full-spectrum Artificial Intelligence (AI, or deep learning) techniques for whole clinical workflow, from data acquisition to disease detection, follow-up, diagnosis, therapy, and outcome prediction. In particular, I will demonstrate some innovative technical development and implementation in scanners and clinical workflows, i.e., serving for fast MR, low-dose CT/PET acquisition, and clinical diagnosis/therapy.

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Dinggang Shen is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR, and MICCAI. He was also a recipient of the Distinguished Investigator Award from The Academy for Radiological & Biomedical Imaging Research, USA (2019). He was Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with The University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA, directing The Center of Image Analysis and Informatics, The Image Display, Enhancement, and Analysis (IDEA) Lab, and The Medical Image Analysis Core. Before that, he was also a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and an Instructor in the Johns Hopkins University. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1600 peer-reviewed papers in the international journals and conference proceedings, with H-index 140 and over 85K citations. He serves as an Editor-in-Chief for Frontiers in Radiology, as well as an editorial board member for eight international journals. Also, he has served in the Board of Directors for MICCAI Society in 2012-2015, and was General Chair for MICCAI 2019.

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I am an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and Electrical Engineering at Stanford University. I work on making machine learning more reliable, human-compatible and statistically rigorous, and am especially interested in applications in human disease and health. I received my Ph.D from Harvard in 2014, and was at one time a member of Microsoft Research, a Gates Scholar at Cambridge and a Simons fellow at U.C. Berkeley. I joined Stanford in 2016 and am excited to also be a Chan-Zuckerberg Investigator. We are also a part of the Stanford AI Lab. My research is supported by the Sloan Fellowship, the NSF CAREER Award, and the Google and Tencent AI awards.

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