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CS Graduate Seminar: Mining Scholarly Data: from Search to Recommendation

Start Date: February 4, 2019
End Date: February 4, 2019

By Professor Xiangliang Zhang‚Äč (KAUST)

The scientific publication outputs have been exponentially growing in all domains, making document retrieval like finding a needle in a haystack. The main challenge roots in the fast-expanding attributed network with rich information of paper contents on nodes and citation relations on edges. This talk will introduce deep neural network models and deep generative models that are designed for addressing the problems of recommendation of top-k relevant papers to read/cite; identification of top-k possible authors of an anonymous paper; search of datasets and related papers, and identification top-k popular datasets in different domains.

Biography: Dr. Xiangliang Zhang is an Associate Professor of Computer Science and directs the Machine Intelligence and kNowledge Engineering ( group at KAUST. She earned her Ph.D. degree in computer science from INRIA-Universite Paris-Sud, France, in July 2010. Dr. Zhang's research mainly focuses on learning from complex and large-scale streaming data, and scholarly data.

More Information:

For more info contact: Prof. Xiangliang ZHANG: email:

Date: Monday 4th Feb 2019
Time:12:00 PM - 01:00 PM
Location: Location: Bldg. 9 Hall 1
Refreshments: Light Lunch will be served at 11:45 AM