Distinguished Lecture

Graph Identification

Lise Getoor

Professor
University of Maryland
 
Monday, November 19, 2007
4:30pm - 5:30pm
1670 Beyster Bldg.

 
A reception will precede the seminar from 4:00 to 4:30 in 3725 CSE.

About the Event

Within the machine learning community, there has been a growing interest in learning structured models from input data that is itself structured. Graph identification refers to methods that transform an observed input graph into an inferred output graph. Examples include inferring organizational hierarchies from social network data and identifying gene regulatory networks from protein-protein interactions. The key processes in graph identification are: entity resolution, link prediction, and collective classification. I will overview algorithms for these tasks, discuss the need for integrating the results to solve the overall problem collectively, and show how these methods are relevant to foundational problems in AI such as knowledge representation, reformulation, and reasoning.

Biography

Lise Getoor is an assistant professor in the Computer Science Department at the University of Maryland, College Park. She received her PhD from Stanford University in 2001. Her current work includes research on link mining, statistical relational learning and representing uncertainty in structured and semi-structured data. She has published numerous articles in machine learning, data mining, database, and AI forums. She is an action editor for the Machine Learning Journal, is a JAIR associate editor, has been a member of AAAI Executive council, and has served on a variety of program committees including AAAI, ICML, IJCAI, KDD, SIGMOD, UAI, VLDB, and WWW.

Additional Information

Contact: Rita Rendell

Phone: 764-4343

Email: lorichon@umich.edu

Event Sponsor: CSE

Open to: Public

Presentation: http://inst-tech.engin.umich.edu/media/?sk=cse-dls-07