报告题目：Only Time Will Tell: Time Series Anomaly Detection
报告专家：See-Kiong Ng（黄思强）新加坡国立大学计算学院教授
报告时间：2021年1月 13日16:00
报告地点：腾讯会议(ID:832465072 )
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Abstract:
Time series are a fundamental data type for understanding dynamics in real-world systems and their underlying processes. For example, in the medical domain, time series data are becoming common with the advent of medical devices for physiological monitoring. In many real-world physical systems such as smart buildings, factories, power plants and data centres, the prevalence of networked sensors and actuators has also generated substantial amounts of multivariate time series data for these systems. Detecting anomalous patterns in such time series is a challenging problem due to the complication of the temporal dynamics of these complex systems. We present our ongoing efforts in using deep learning for detecting irregular irregularities in univariate medical time series data, and anomalous events in multivariate IoT (Internet-of-Things) sensor data.
Speaker bio:
See-Kiong Ng (Ph.D. Carnegie Mellon University) is a Professor of Practice at the Department of Computer Science of the School of Computing at National University of Singapore (NUS), and the Deputy Director of the university’s Institute of Data Science. See-Kiong obtained his Bachelor (1989), Masters (1993), and Ph.D. (1998) degrees in Computer Science from Carnegie Mellon University and a Masters (1991) degree in Artificial Intelligence from University of Pennsylvania. Prior to joining NUS in 2016, See-Kiong was a Programme Director of the Urban Systems Initiative by the Science and Engineering Research Council of the Agency of Science, Technology and Research (A*STAR), and the founding head and principle scientist of its Data Mining Department. Currently, See-Kiong’s mission at NUS is to leverage his data science and AI expertise for transdisciplinary and translational research into important real-life problems and education of the next generation of data scientists. From using the computation of data to better understand the biology of the human body, See-Kiong is using machine learning and artificial intelligence to understand the “biology” of complex human cities and societies and creating real-world impact with the science of data.
讲题：只有时间才能证明：时间序列异常检测
讲座大纲：
时间序列是理解真实世界系统的动力学及其底层过程的基本数据类型。例如，在医学领域，随着用于生理监测的医疗设备的出现，时间序列数据变得越来越普遍。在智能建筑、工厂、发电厂和数据中心等许多实际物理系统中，网络传感器和执行器的普及也为这些系统产生了大量的多元时间序列数据。 由于这些复杂系统的时间动力学的复杂性，检测这种时间序列中的异常模式是一个具有挑战性的问题。 我们目前正在使用深度学习来检测单变量医学时间序列数据中的“不规则”和多变量物联网传感器数据中的异常事件。
主讲人简介：
黄思强博士为新加坡国立大学计算学院计算机科学系教授，新加坡国立大学数据科学研究所副所长。1986年，他获得新加坡国家电脑局海外奖学金赴美国学习，先后获得卡内基梅隆大学的计算机科学学士（1989年）、硕士（1993年）和博士（1998年）学位，以及宾夕法尼亚大学的人工智能硕士学位（1990年）。黄博士曾担任新加坡科技研究局（A*STAR）组织的城市体系计划主任，并担任了新科研信息通信研究所数据分析部门的创始负责人和首席科学家。目前，黄博士于国大新设立的数据科学研究所，着力为新加坡培养新一代杰出的数据科学家，并与多个行业与公共机构进行成功的研究合作，以数据科学开发实用的人工智能技术。
作为一名经验丰富的数据科学家，从使用数据挖掘和机器学习来揭示人体的生物学（生物信息学），到使用大数据和人工智能来理解复杂的人类城市（智能城市），黄博士以数据的科学与应用证明了数据中的巨大价值。
