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演讲简介
The high development of emerging computing paradigms, such as Ubiquitous Computing, Mobile Computing, and Social Computing, has brought us a big change from all walks of our work, life, learning and entertainment, along with increasing attention from both academia and industry. In this talk, we concentrate on Deep Neural Networks for Big Data Analytics in CPSS, specifically, discuss models and methods on big data aggregation, organization and mining using machine learning/deep learning techniques. As for implementations, mechanisms and algorithms are introduced based on the design of several deep neural network models for smart applications, including personalized recommendation, anomaly detection, object detection, data augmentation, developed in modern cyber-physical-social systems.
关于讲者
周晓康，现任日本滋贺大学数据科学学院副教授。2014年博士毕业于日本早稻田大学。2012至2015年，于早稻田大学人间科学学术院任研究助手(Research Associate)。2017年起，于日本理化研究所革新知能综合研究中心（AIP）兼职任客员研究员。
研究领域涵盖计算机科学、数据科学和社会人类信息学，主要关注大数据、机器学习、行为认知、普适计算智能和安全等方面。发表学术期刊/会议论文170余篇，其中SCI期刊论文110余篇 (中科院1区，IEEE/ACM Trans 70余篇)，包括THMS, TLT, TCSS, TETC, IoTJ, TSC, TBD, TII, TCBB, TNSE, TVT, TOMM, T-ITS, TIA, TOSN, JSAC, WCM, TOIT, TASE, TCE, TIV, TNNLS等。入选2023斯坦福大学发布全球前2%顶尖科学家。荣获多项国际性奖励与荣誉，包括2023和2020 IEEE SMC Society Andrew P. Sage Best Transactions Paper Award、2023 IEEE Industrial Electronics Society TC-II Best Paper等。近年来，在多个国际知名期刊，如Computer Communications, TCE, Applied Energy, JSA, IoTJ, TOSN, TCBB, BAE, IEM, INF, BDR, CAEE, WWW, Ad Hoc Networks, MTAP, JPDC, FGCS等担任客座编委，副编委等职，并于多个IEEE重要国际学术会议担任程序委员会主席。
