Campus Event Calendar
Event Entry
What and Who
Toward Data-Driven Education
Rakesh Agrawal
EPFL
SWS Distinguished Lecture Series
Rakesh Agrawal is the President and Founder of the Data Insights Laboratories, San Jose, USA and a Visiting Professor at
EPFL, Lausanne, Switzerland. He is a member of the National Academy of Engineering, both USA and India, a Fellow of ACM,
and a Fellow of IEEE. He has been both an IBM Fellow and a Microsoft Fellow. He has also been the Rukmini Visiting Chair
Professor at the Indian Institute of Science, Bangalore, India. ACM SIGKDD awarded him its inaugural Innovations Award and
ACM SIGMOD the Edgar F. Codd Award. He was named to the Scientific American’s First list of top 50 Scientists. Rakesh has
been granted 80+ patents and published 200+ papers, including the 1st and 2nd highest cited in databases and data mining.
Five of his papers have received “test-of-time” awards. His papers have received 100,000+ citations. His research formed
the nucleus of IBM Intelligent Miner that led the creation of data mining as a new software category. Besides Intelligent Miner,
several other commercial products incorporate his work, including IBM DB2 and WebSphere and Microsoft Bing.
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
AG Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Monday, 13 November 2017
10:00
90 Minutes
E1 5
002
Saarbrücken
Abstract
An educational program of study can be viewed as a knowledge graph consisting of learning units and relationships between them. Such a
knowledge graph provides the core data structure for organizing and navigating learning experiences. We address three issues in this talk.
First, how can we synthesize the knowledge graph, given a set of concepts to be covered in the study program. Next, how can we use data
mining to identify and correct deficiencies in a knowledge graph. Finally, how can we use data mining to form study groups with the goal of
maximizing overall learning. We conclude by pointing out some open research problems.
knowledge graph provides the core data structure for organizing and navigating learning experiences. We address three issues in this talk.
First, how can we synthesize the knowledge graph, given a set of concepts to be covered in the study program. Next, how can we use data
mining to identify and correct deficiencies in a knowledge graph. Finally, how can we use data mining to form study groups with the goal of
maximizing overall learning. We conclude by pointing out some open research problems.
Contact
Annika Meiser
0681 9303 9105
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Video Broadcast
Yes
Kaiserslautern
G26
111
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Annika Meiser, 10/25/2017 10:59 -- Created document.