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Topic1：开发智能物联网与云端医疗生态系统/Developing AIoT, Telemedicine and Healthcare Clouds with Sustainable Ecosystem for Digital Economy

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。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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讲者简介：

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年加入香港中大（深圳）担任校长讲座教授，兼任深圳人工智能与机器人研究院中心主任。他在计算机结构、并行处理、云计算、与物联网方面著作等身，桃李满天下。被评选入全球2%顶级科学家。

Topic 2: Developing Interpretable Temporal Point Process Models for Healthcare 

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.

讲者简介：

Shuang Li is currently a tenure-track Assistant Professor at the School of Data Science, 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 machine learning 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 Analytics Best Student Paper Competition.