AIRS in the AIR

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  黄铠

  本场活动执行主席，香港中文大学（深圳）校长讲座教授、AIRS 高性能智能计算中心主任

  开发智能物联网与云端医疗生态系统

  嘉宾介绍

  黄铠教授，加州大学计算机科学博士。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）担任校长讲座教授，兼任深圳市人工智能与机器人研究院（AIRS）中心主任。他在计算机结构、并行处理、云计算与物联网方面发表多本著作，入选全球2%顶级科学家榜单。黄教授获得诸多奖项，其中包括2012 IEEE 世界云计算大会终身成就奖，他2019年获得建国70周年70人科技创新成就奖，2020年获得吴文俊人工智能自然科学奖。他为中国计算机领域培养了几千名专业人才，包括6位院士、10 位IEEE/CCF 会士、30 多位计算机高科技领军骨干。li'shua

  个人简介

  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、云计算，与边缘感知设备等关键技术的融合lishua。目标是建设智能物联网医疗云的生态系统。面对大数据感知、机器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结合，为数字经济，远程医疗与全民健保，建立与时俱进的生态环境与工业体系。

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  李爽

  香港中文大学（深圳）助理教授、AIRS群体智能中心副研究员

  Developing Interpretable Temporal Point Process Models for Healthcare

  嘉宾介绍

  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.

  个人简介

  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、AIRS 群体智能中心研究员

  主持人

  嘉宾介绍

  个人简介

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