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Charles Elkan


As a professor in the computer science and engineering department at the University of California, San Diego, my main research interests are in machine learning and data science. I work especially on foundational questions raised by applications in business and to biomedicine. Research in my group has been funded recently 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 Intel and other companies.

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 postdoctoral fellow 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.

In winter 2013, I am teaching CSE 250B, a graduate course on machine learning. In spring 2013 I taught CSE 255, a graduate course on data science and analytics, while in fall 2012 I taught CSE 250A, a different graduate course on machine learning.

For a complete list of publications, with links to full papers, see DBLP and this Google Scholar profile.

Selected publications


Differential Privacy Based on Importance Weighting
Z. Ji and C. Elkan
In Machine Learning, June 2013 pdf 
We propose and analyze a general method for publishing data while still protecting privacy, by computing weights that make an already public dataset analogous to the dataset that must be kept private. The weights are importance sampling coefficients that are regularized and have noise added to protect privacy. The weights allow arbitrary queries to be answered approximately while provably guaranteeing differential privacy. Experiments show that the new mechanism performs well even when the privacy budget is small, and when the public and private datasets are drawn from different populations.
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
In Algorithmic Finance 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%.
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). More information.
Some invited talks



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

Press


Some sources that have interviewed me, or that have mentioned research from my group. Click a logo to read an article.

Dr.
            Dobb's logo New Scientist
            logo Reuters logo Miller-McCune logo Wall Street Journal logo
              Photo of Charles Elkan

News
2014/3/3: Invited talk at the Machine Learning and Data Analytics Symposium in Qatar.

2013/6/11: Our paper on differential privacy has been accepted by Machine Learning.

2013/5/15: Keynote talk at the Qualcomm analytics summit.

2013/5/10: Postdoc position available funded by Intel (filled).

2013/4/16: Kickoff meeting for research with SRI funded by IARPA.

2013/3/29: Visiting Cornell tech and Google New York.

2013/2/20: Speaking at the IE Group Chief Data Scientist Summit, San Diego.

2013/1/29: Qualcomm distinguished lecture at ICNC 2013.

2012/12/14: Just finished teaching CSE 250A. 63 students in a graduate course!

2012/12/11: Keynote speaker at the International Symposium on Multimedia.

2012/9/24: In Bristol to present two papers at ECML.

2012/9/17: Appointed area chair for ICML 2013.

2012/6/26: In Edinburgh to present a paper at ICML.

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.

Contact
Room 4134, CSE Building
University of California, San Diego
La Jolla, CA 92093-0404

elkan@ucsd.edu

(619) 379-9852

Assistant: Esra Hembrough

Linkedin
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