Distinguished Lecture by Jiawei Han: One of the most influential data scientists of our time!

Title: Mining Structures from Massive Text Data: A Data-Driven Approach

On July 20th, Jiawei Han, one of the most cited data scientists in the world, will give a DSC/e Distinguished Lecture. The talk “Mining Structures from Massive Text Data: A Data-Driven Approach” will focus on the analysis of Big Unstructured Data (BUD), i.e., the type of data that is everywhere, but very hard to handle.

This is the second lecture in the newly established DSC/e Distinguished Lecture Series, where international thought leaders share their views on important data science topic. The first lecture was given by Ron Kenett on Manufacturing 4.0 Analytics. 

As well as delivering excellent talks, these lectures also provide a very good opportunity to meet other data science enthusiasts. (The lectures are followed by drinks to network and discuss.) 

Professor Jiawei Han is a distinguished professor working in Department of Computer Science of the University of Illinois at Urbana-Champaign. His achievements are remarkable and his work has had a huge impact on both academia and industry. He is currently ranked 3rd in the Top H-Index for Computer Science and Electronics compiled by Guide2Research. This is a global ranking based on citations covering all researchers in the broader computer science and electronics area. This illustrates the exceptional qualities of the next DSC/e Distinguished Lecture Series speaker.

Professor Han's primary research areas are data mining, information network analysis, database systems, and data warehousing, through which he has published over 600 journall papers and conference presentations. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab, and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing. He is a Fellow of ACM and Fellow of IEEE, and received the 2004 ACM SIGKDD Innovations Award, the 2005 IEEE Computer Society Technical Achievement Award, and the 2009 M. Wallace McDowell Award from IEEE Computer Society. His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.

The talk will combine the topics of text mining and Big data challenges. Real-world big data are largely unstructured, interconnected, and in the form of natural language text. One of the grand challenges is to turn such massive data into structured networks and actionable knowledge. He will propose a text mining approach that requires only distant supervision or minimal supervision but relies on massive data. He will also show that quality phrases can be mined from such massive text data, types can be extracted from massive text data with distant supervision, and relationships among entities can be discovered by meta-path guided network embedding. Finally, he proposes a D2N2K (i.e., data-to-network-to-knowledge) paradigm, that, first turns data into relatively structured information networks, and  subsequently mines such text-rich and structure-rich networks to generate useful knowledge. Clearly, this paradigm represents promising potential for turning massive text data into structured networks and useful knowledge.

PROGRAM  

Date and time:     Thursday, July 20, 16:00 – 17.00
Location:              TU/e, Filmzaal de Zwarte Doos

15:30-16:00         Welcome and coffee
16.00-17.00         Lecture by dr. Jiawei Han
17.00-18.00         Network drinks

This lecture is organized in cooperation with the Netherlands Research School SIKS. The DSC/e is organizing several interesting events. If you are interested in a certain lecture or workshop please send an email to dsce@tue.nl or subscribe for our newsletter here

 Please register here