Natural language research announcements

["Roth, Dan" <danr AT uiuc.edu>]

-----Original Message-----
From: cinda heeren 
[mailto:c-heeren AT uiuc.edu]
Sent: Monday, June 30, 2008 9:32 PM
To: 
lvantang AT usc.edu;
 
ruiyangwu AT gmail.com;
 Sharon M. Paradesi; 
yliu49 AT emory.edu;
 
jxiao AT cs.utsa.edu;
 
cloverzj AT ksu.edu;
 Tim Weninger; 
xiajing AT ksu.edu;
 
welshamy AT ksu.edu;
 Ashish Panday; 
kjaeyeon AT iit.edu;
 
nathanielryckman AT yahoo.com;
 
zhourui AT ksu.edu;
 
caitlinl AT ksu.edu;
 
sahenke AT gmail.com;
 
shydslow AT aol.com;
 
jen_nsims AT yahoo.com;
 
belljessicaf_05 AT yahoo.com;
 
smartypantsizit AT yahoo.com;
 Donee' McAllister; William H. Hsu; Loeb, Alexander Thomas; Tim Vieira; 
Muriello, Daniel G; Robinson, Jacob C; Paul, Michael J; 
dssifac AT cs.uiuc.edu
Cc: Roth, Dan
Subject: Talk by Bill Hsu TOMORROW


Hi Everyone,  attached is the announcement for Bill's talk, TOMORROW
(sorry for the late notice).  Faculty, please pass on word of this
talk to your grad students.  DSSI students, your attendance is
expected. :)  Lunch will follow the talk in the usual place.

Who:  Bill Hsu (bio following)
When: 7/1, 10a
Where:  Siebel 3405


Constructive Induction in Link Mining with Applications to "Social"
Networks

This talk will focus on the problem of learning to predict and reason
about the structure of graphs whose links represent relations of various
types.  I will describe some framing problems in link mining, starting
with classification-based prediction of link existence in social
networks and extending this towards statistical relational learning
(especially using relational graphical models).  We will look at two
methodologies: first, computing graph features and using them in
classical feature construction; second, a more general constructive
induction approach that aims at synthesizing features in a pure
"discovery informatics" framework.  In both cases I will first discuss
classification, then mostly generative and some discriminative
techniques. I will present some early results from graph feature
construction in the social network link mining domain and discuss new
research using the more general approach.  I will conclude with a brief
survey of successful applications of this link mining approach,
including one in bioinformatics (protein-protein interaction
prediction).

---

William Hsu is an associate professor of computer science at Kansas
State University.  He received his Ph.D. in computer science in 1998,
was a research scientist in the Automated Learning Group at NCSA from
1998-1999, and has been a member of K-State's Computing and Information
Sciences faculty since 1999.  His research and teaching interests
include machine learning, probabilistic reasoning, time series analysis,
and data mining using graphical models.