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  黄铠

  本场运动执行主席，，，，，香港中文大学（深圳）校长讲座教授、优德官网 高性能智能盘算中心主任

  开发智能物联网与云端医疗生态系统

  嘉宾先容

  黄铠教授，，，，，加州大学盘算机科学博士。。。。。。。。在美国南加大与普渡大学任教多年。。。。。。。。2018年加入香港中文大学（深圳）担当校长讲座教授，，，，，兼任优德官网（优德官网）中心主任。。。。。。。。他在盘算机结构、并行处置惩罚、云盘算与物联网方面揭晓多本著作，，，，，入选全球2%顶级科学家榜单。。。。。。。；；；；；；平淌诨竦弥疃嘟毕睿，，，，其中包括2012 IEEE 天下云盘算大会终身成绩奖，，，，，他2019年获得开国70周年70人科技立异成绩奖，，，，，2020年获得吴文俊人工智能自然科学奖。。。。。。。。他为中国盘算机领域作育了几千名专业人才，，，，，包括6位院士、10 位IEEE/CCF 会士、30 多位盘算机高科技领军主干。。。。。。。。li'shua

  演讲摘要

  在这个报告中，，，，，黄铠教授将探讨大数据、AI 芯片、5G/6G 通讯网络、云盘算，，，，，与边沿感知装备等要害手艺的融合lishua。。。。。。。。目的是建设智能物联网医疗云的生态系统。。。。。。。。面临大数据感知、机械学习认知与群体人工智能应用，，，，，他将强调智能云认知与物联网感知的无缝团结，，，，，为数字经济，，，，，远程医疗与全民健保，，，，，建设与时俱进的生态情形与工业系统。。。。。。。。

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  李爽

  香港中文大学（深圳）助理教授、优德官网群体智能中心副研究员

  Developing Interpretable Temporal Point Process Models for Healthcare

  嘉宾先容

  Shuang Li is currently a tenure-track Assistant Professor at the School of DataScience, The Chinese University of Hong Kong, Shenzhen. She received her Ph.D.in Industrial Engineering (specification in Statistics, minor in Operations Research) from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2019. After that, she was a postdoctoral fellow working with Dr. Susan Murphy in the Department of Statistics at Harvard University. She has published in top-tier machinelearning conferences and journals, including ICML, NeurIPs, and JMLR. Her works have been selected as an oral presentation and a spotlight presentation at NeurIPS. She was also a finalist in the INFORMS Quality, Statistics, and Reliability (QSR) Best Student Paper Competition and Social Media AnalyticsBest Student Paper Competition.

  演讲摘要

  Complex systems like healthcare continually produce large amounts of irregularly spaced discrete events. Understanding the generating process of these event data has long been an interesting problem. Temporal point process models provide an elegant tool for modeling these event data in continuous time. The learned model can be used to predict the time-to-event and event types. Recent advances in neural-based temporal point process models have exhibited superior ability in event prediction. However, the lack of interpretability of these black-box models hinders their applications in high-stakes systems like healthcare. Recently, we proposed an interpretable temporal point process modeling and learning framework, where the intensity functions (i.e., occurrence rate) of events are informed by a collection of human-readable temporal logic rules. Our framework enables the extraction of medical knowledge or clinical experiences from noisy raw event data as a compact set of temporal logic rules. The discovered rules can contribute to the sharing of clinical experiences and aid in improving treatment strategies.

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  吴均峰

  香港中文大学（深圳）副教授、优德官网 群体智能中心研究员

  主持人

  嘉宾先容

  演讲摘要

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