Social Computing in the Era of AI and Big Data

Topic: Social Computing in the Era of AI and Big Data

Speaker: Professor Irwin King

Date: Feb. 7, Wednesday

Time: 16:30-17:30

Venue: Governing Board Meeting Room, Daoyuan Building

Language: English

Speaker's Profile:

Prof. Irwin King is Associate Dean (Education), Faculty of Engineering and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong.  He is also Director of the Shenzhen Key Laboratory of Rich Media and Big Data.  He has also worked at AT&T Labs Research and taught courses at UC Berkeley during his sabbatical. Recently, Prof. King has been an evangelist in the use of education technologies in eLearning for the betterment of teaching and learning.

He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles. 

Prof. Irwin King's research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing.  In these research areas, he has over 300 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes. He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD), Journal of Neural Networks, and a former Associate Editor of the IEEE Transactions on Neural Networks (TNN). 

Abstract:

The AI and Big Data Era has ushered in a new wave of research that investigates how we can better handle data with characteristics such as high volume, velocity, veracity, and variety using machine learning techniques.  Social Computing examines the collective intelligent behavior resulted from interactions among social entities.  In the first part of the talk, the speaker plans to draw some observations on the interplay between Social Computing and Big Data. The speaker will then focus on our recent work on social and location recommendations based on matrix factorization framework as a case study that demonstrates how filtered suggestions are highly desirable to cope with the information explosion problem.  The speaker will outline novel ways on how we can use social ensemble, trust relations, tags, click-through-rate, etc. to improve social and location recommender systems for a wide-range of applications and services in the era of Big Data.