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报告题目：DAG: A General Model for Privacy-Preserving Data Mining
报 告 人：Cheng Siong Lee 教授
报告时间：2019年1月4日 9:00
报告地点：21#426
主办单位：科学技术研究院
承办单位：天游线路测速登录中心
报告人简介：Vincent CS Lee (PhD) is currently Associate Professor at Clayton School of IT, Monash University, Australia. Lee has 35 (13 years with MNC and public-sector establishments in Singapore, and 22 years with four universities in Australia and Singapore) years of experience in applied and fundamental research, business enterprise system development, engineering and ICT system integration, and university teaching. Lee is also a registered professional electrical engineer in Singapore; Licensed Electrical Engineer (High voltage switching authorization) in Singapore; Chartered Engineer and member of IEE in UK and Association of Computing Machines (ACM) in USA. Lee published 61 peer-reviewed international (high impact factor SCI and SSCI) journals articles and 136 papers in peer-reviewed international conference proceedings. His publications are in IEEE Transactions on Knowledge and Data Engineering; IEEE Proceeding on Generation, Transmission and Utilizations; Signal Processing; IEEE Selected Areas in Communications; IEEE Security and Privacy. He has chaired/co-chaired more than a dozen of IEEE international conference technical program committees: conference chair KICSS2012; general chair ICDIM2011. Lee's current research interests are multidisciplinary spreading across signal processing; adaptive knowledge representation and information engineering; data, text, and graph mining for knowledge discovery; decision theory; information system research based on design science paradigm; and Neuro-financial engineering.
报告内容：Brief introduction of FIT Monash University’s specific research organization and direction- AI and Data Science span across all faculties in Monash University; Overview of my current research focus: (1) Agile Enterprise Architecture framework for alignment of business strategy, process and ICT technologies. (2) Data mining with deep machine learning for (educational Learning Management system and university log files; healthcare vital signs in ICU via EMR, EHR and EPR linkages; real time anomaly detection for structural system health). (3) Spatial-temporal clustering algorithm development (signal direction of arrival, Hybrid MIMO for 5G mobile network’s QoS). (4) IoT analytics and privacy preserving technique via secure multiparty computation public key crypto-system, and Blockchain technology. (5) Development of optimization model for distributed energy resources; and for financial investment portfolio with visualization tool/technique. Research funding scheme available for HDR (PhD and Research Masters scholarship) research students.
