报 告 人：Prof. Liang Zhao

                George Mason University

报告题目：Knowledge Discovery and Predictive

 Modeling in Big Data Era

课程时间：12月29日 上午9:30-11:00

课程地点：综合楼 308

邀 请 人：智能系统研究所  黄敏教授

Abstract:

Nowadays, big-data mining has become an important research topic for more and more application domains such as public health, industrial process control, cyber security, and E-commerce. However, the exclusive characteristics in various big-data such as noisiness, heterogeneity, and dynamics motivate new techniques to overcome the new data challenges in big-data era. This talk will focus on the major challenges in big-data mining and the solutions to them in data-mining and machine-learning. To this end, the speaker will elaborate the motivations and designs of the state-of-the-art theoretical models and optimization methods for addressing big-data challenges. Relevant progress in social media-mining, sparse feature learning, multi-task learning, deep-learning, transfer-learning, and robust machine learning will be described.

Biography:

Dr. Liang Zhao is an assistant professor at Information Science and Technology Department at George Mason University. He got his PhD degree from Computer Science Department at Virginia Tech in the United States, and bachelor and master degree from Northeastern University in China. His research interests include big-data mining, artificial intelligence, and machine learning, with particular emphasis on sparse feature learning, event detection, text mining, and heterogeneous network modeling. He is named as the one of the “Top 20 Data-mining Rising Star in the world” by Microsoft Academic Search in 2016. He has published numerous papers in top venues in data-mining and artificial intelligence such as ACMKDD, IEEETKDE, IEEEICDM, AAAI, IJCAI, CIKM, and WWW. He has served as publication chair of SSTD 2017, and program committee of IEEE ICDM 2017 and ICTAI 2017. He has been serving as reviewer for top conferences and journals such as ACMKDD, ACMTKDD, IEEETKDE, and IEEEICDM.