News Detail:

Talk by Dr. Hasan:

Dr. Mohammad Hasan, Associate Professor of Computer Science at Indiana University--Purdue University, Indianapolis (IUPUI) is going to deliver a talk titled "Representation Learning on Network Data and its Applications". Dr. Hasan is an alumnus of BUET CSE and a winner of NSF career award.

Date: 30 July (Tuesday)
Time: 1:30 pm
Place: Graduate Seminar Room, ECE Building, BUET

You are cordially invited.

Abstract of the talk:
As deep learning models are becoming highly successful in recent years, it is becoming increasingly evident that representation learning is one of the major contributing factors to the success of deep learning models. Representation learning methods embed the data instances to a high dimensional vector space in which the learning models can better discriminate the instances from different classes, or better cluster the instances from the same class. In the deep learning community, representation learning of image, sound, and text data received the major attention from the researchers, however in recent years, representation learning is also being applied on network data, as no natural vector representation is available for network elements, such as, nodes or edges.

In this talk, the speaker will discuss some of his recent works on representation learning on network data. He will discuss how we can design representation learning models for representing vertices or edges of a network. He will also discuss how representation learning can be used for many network related applications, including name disambiguation, link prediction, and recommendation.

About the speaker:
Mohammad Hasan is an Associate Professor of Computer Science at Indiana University--Purdue University, Indianapolis (IUPUI). Before that, he was a Senior Research Scientist at eBay Research Labs, San Jose, CA. He received his Ph.D. degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2009 and his BSc (Engg.) degree in Computer Science and Engineering from BUET in 1998. His research interest focuses on developing novel algorithms in data mining, data management, information retrieval, machine learning, social network analysis, and bioinformatics. He won PAKDD best paper award, ACM SIGKDD doctoral dissertation award and an NSF CAREER award. More information about his research is available from his group website .

Posted on: [2019-07-29]