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          預告:Victor S. Sheng :Cost-Sensitive Feature Value Acquisition in Testing

          2019年07月04日 15:36 來源:計算機與通信工程學院



          報告內容Cost-Sensitive Feature Value Acquisition in Testing

          報告人姓名Victor S. Sheng

          報告人所在單位the University of New Brunswick

          報告人職稱/職務及學術頭銜an Associate Professor of computer science



          報告人簡介Victor S. Sheng received the M.Sc. degree from the University of New Brunswick, Fredericton, NB, Canada, and the Ph.D. degree from the University of Western Ontario, London, ON, Canada, both in computer science, in 2003 and 2007, respectively.

          He is an Associate Professor of computer science and the Founding Director of Data Analytics Laboratory at University of Central Arkansas. After receiving the Ph.D. degree, he was an Associate Research Scientist and NSERC Postdoctoral Fellow in information systems with the Stern Business School at New York University. His research interests include data mining, machine learning, crowdsourcing, and related applications in business, industry, medical informatics, and software engineering. He has published more than 150 research papers in conferences and journals of machine learning and data mining. Most papers are published in top journals and conferences in data science, such as PAMI, TNNLS, TKDE, JMLR, AAAI, KDD, IJCAI, and ACMMM.

          Prof. Sheng is a senior member of IEEE. He is a conference organizer for several conferences, and an editorial board member for several journals. He also is a SPC and PC member for many international conferences (such as IJCAI, AAAI, and KDD) and a reviewer of more than twenty international journals (such as PAMI, TNNLS, TKDE, and JMLR). He was the recipient of the Best Paper Award Runner Up from KDD’08, the Best Paper Award from ICDM’11, the Best Student Paper Award Finalist from WISE’15, the Best Paper Award from ICCCS’18, the Google Student Award Winner of the 3rd annual Machine Learning Symposium 2008, and the Best Poster Award of the UW and IEEE Kitchener-Waterloo Section Joint Workshop on Knowledge and Data Mining (2006).


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