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 biomedicine. Research in my group has been funded by an R01 grant from the National Institutes of Health, by the National Science Foundation, by a grant from the University of California National Lab cooperation program, and by gifts from Intel and other companies.
My Ph.D. is in computer science from Cornell University, with a graduate minor in economics. While at Cornell, I was also a visiting Ph.D. student at Stanford University advised by John McCarthy, 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. While on leave from UCSD, I have been a visiting associate professor at Harvard University.
From 2014 to 2018 I worked in Seattle as the first Amazon Fellow, building and leading Amazon's central machine learning team in Seattle, Palo Alto, and New York. Since 2018, I have been a managing director and the global head of machine learning at Goldman Sachs in New York. For more information see this curriculum vitae.
Before joining Amazon, in winter 2014, I taught 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.
Efficient Elastic Net
Regularization for Sparse Linear Models
Z. Lipton and C. Elkan
http://arxiv.org/abs/1505.06449
pdf
We show how to train sparse
linear models efficiently with elastic net
regularization. The new algorithm applies stochastic
gradient updates to non-zero features only, bringing
weights current as needed with closed-form updates.
We provide dynamic programming algorithms that
perform each delayed update in constant time.
Experimental results show that on a bag-of-words
dataset with 260,941 features, but only 88 nonzero
features on average per training example, the
dynamic programming method trains a logistic
regression classifier with elastic net
regularization over 2000 times faster than
otherwise.
B. Duncan and C. Elkan
In
Proceedings of the 21st ACM Conference on Knowledge
Discovery and Data Mining (KDD), July 2015
This paper presents
probabilistic models that rank sales leads based on
their probability of conversion to a solid opportunity,
successful sale, and/or expected revenue. The trained
models replace traditional lead scoring systems, which
are error-prone and not probabilistic. Experimental
results are shown on real sales data from two companies.
For one company, a 307% increase in number of successful
sales is achieved, as well as a dramatic increase in
total revenue. Deployment shows that additional benefits
include decreased time needed to qualify leads, and
decreased number of calls placed to schedule a product
demo.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Z. Lipton and C. Elkan
http://arxiv.org/abs/1506.00019
pdf
Recurrent neural networks are
connectionist models that capture the dynamics of
sequences via cycles in the network of nodes, retaining
a state that can represent information from an
arbitrarily long context window. In recent years,
systems based on long short-term memory and
bidirectional architectures have demonstrated
ground-breaking performance on tasks as varied as image
captioning, language translation, and handwriting
recognition. We synthesize the research that has made
practical these powerful learning methods, providing a
self-contained explanation of the state of the art
together with a historical perspective and references to
primary research.
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 expected 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.
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
2020/1/24: Committee member for the Ph.D. defense of
Morteza Ashraphijuo
at Columbia University.
January 2020: Three lectures on deep learning at the 6th International Winter School on Big Data in Ancona, Italy.
2019/12/13: Panel speaker at the NeurIPS 2019 Workshop on Robust AI in Financial Services.
2019/10/24: Keynote talk at the AI World event in Boston.
August 2019: Area chair for NeurIPS 2019 to be held in Vancouver.
2015/12/12: Invited talk at the NIPS Workshop on Learning in Large Label Spaces.
2015/6/19: Keynote speech on differential privacy at Benelearn 2015.
2015/6/3: Keynote speech on data science at the Canadian AI conference.
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.
January 2020: Three lectures on deep learning at the 6th International Winter School on Big Data in Ancona, Italy.
2019/12/13: Panel speaker at the NeurIPS 2019 Workshop on Robust AI in Financial Services.
2019/10/24: Keynote talk at the AI World event in Boston.
August 2019: Area chair for NeurIPS 2019 to be held in Vancouver.
2015/12/12: Invited talk at the NIPS Workshop on Learning in Large Label Spaces.
2015/6/19: Keynote speech on differential privacy at Benelearn 2015.
2015/6/3: Keynote speech on data science at the Canadian AI conference.
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
elkan@ucsd.edu(619) 379-9852
