Charles Elkan
I am a professor in the computer science and engineering department at the University of California, San Diego. My doctorate is in computer science from Cornell University, with a graduate minor in economics. As a graduate student I also spent time at Stanford University, and before joining UCSD I was a postdoc at the University of Toronto. My undergraduate degree is in mathematics from Cambridge University, with a focus on statistics and optimization. In 1998/99 I was a visiting associate professor at Harvard University. For more information see this curriculum vitae.
My main research interests are in machine learning, data mining, and analytics. I am interested especially in applications to business and to biomedicine. Research in my group is funded by an R01 grant from the National Institutes of Health, by a grant from the University of California National Lab cooperation program, and by gifts from multiple companies.
In the winter quarter of 2012, I am teaching CSE 250B, a graduate course on machine learning, and organizing the UCSD AI seminar. In the spring quarter I will teach CSE 255, a graduate course on analytics and data mining.
For a complete list of publications, with links to full papers, see DBLP and my Google Scholar profile.
Link Prediction via Matrix Factorization
A. K. Menon and C. Elkan
In
Proceedings of the European Conference on Machine Learning
(ECML), September 2011 pdf
We show how to learn to predict
which edges exist in a social network (or other graph)
using a matrix factorization approach. The new method
learns latent features that capture the structure of a
network, and combines these with explicit
side-information. The algorithm directly optimizes a
ranking loss, and scales to very large networks. Results
on many social and other networks show the
superior accuracy of the new method.
Nonlinear Support Vector Machines Can Systematically Identify Stocks with High and Low Future Returns
R. Huerta, C. Elkan, and F. Corbacho
Published
as SSRN paper number 1930709, September 19, 2011 pdf
This paper rigorously develops a
reliable model to identify stocks with high and low
future returns. Technical and fundamental features are
computed using CRSP and Compustat data. From 1981 to
2010, taking into account realistic trading costs and
constraints, the model leads to annual Jensen alpha over
10% with standard deviation 8%.
Preserving Privacy in Data Mining via Importance Weighting
C. Elkan
In Proceedings of the Workshop on
Privacy and Security Issues in Data Mining and Machine
Learning (PSDML), September 2010 pdf
This paper presents a
fundamentally new approach to protecting privacy. Let D be a confidential
database, and let E be
a public database with a similar schema. We compute a
weight w(x) for each record x in E that measures how
representative it is of the records in D. Learning on E using these
weights is then essentially equivalent to learning
directly on D,
but D is kept
private.
Accounting for Word Burstiness in Topic
Models
G. Doyle and C. Elkan
In
Proceedings of the 26th International Conference on
Machine Learning (ICML), July 2009 pdf
A fundamental property of
language is that if a word is used once, then it is more
likely to be used again. Previous topic models fail to
capture this burstiness phenomenon. This paper presents
a topic model that uses Dirichlet compound multinomial
distributions to model burstiness. The new model
achieves better goodness of fit in text mining with far
fewer topics than standard latent Dirichlet allocation
(LDA).
Learning to make predictions in networks. Department of Computer Science and Automation, Indian Institute of Science, Bangalore, December 21, 2011.
The analytics landscape: A personal view. Indo-US Workshop on Large Scale Data Analytics and Intelligent Services, Bangalore, December 20, 2011. pdf
A vision for reinforcement learning and predictive maintenance. Keynote talk, Workshop on Data Mining for Service and Maintenance, ACM International Conference on Knowledge Discovery in Databases (KDD), August 21, 2011. pdf
Some sources that have interviewed me, or that have mentioned research from my group. Click a logo to read an article.
News
2012/1/26: Appointed area chair for the
AAAI
conference.
2012/1/8: Looking for a bioinformatics/ML postdoc.
2012/1/4: 57 graduate students enrolled for CSE 250B.
2011/11/16: Zhanglong Ji wins best poster award at the San Diego Data Summit meeting.
2011/10/24: Invited to be a plenary keynote speaker at PAKDD'12.
2011/10/8: Our paper Nonlinear SVMs can systematically identify stocks with high and low future returns is among the most downloaded in two SSRN categories, Capital Markets and Econometrics.
2010/5/1: Our paper Predicting labels for dyadic data selected as one of the 7 best from 658 submissions to ECML/PKDD.
2012/1/8: Looking for a bioinformatics/ML postdoc.
2012/1/4: 57 graduate students enrolled for CSE 250B.
2011/11/16: Zhanglong Ji wins best poster award at the San Diego Data Summit meeting.
2011/10/24: Invited to be a plenary keynote speaker at PAKDD'12.
2011/10/8: Our paper Nonlinear SVMs can systematically identify stocks with high and low future returns is among the most downloaded in two SSRN categories, Capital Markets and Econometrics.
2010/5/1: Our paper Predicting labels for dyadic data selected as one of the 7 best from 658 submissions to ECML/PKDD.
Contact
Room 4134, CSE BuildingUniversity of California, San Diego
La Jolla, CA 92093-0404
elkan@ucsd.edu
(619) 379-9852
Assistant: Sheila Manalo, (858) 534-8873.
