David Sontag's Home Page
E-mail: dsontag {@ | at} cs.nyu.edu
Phone: 212-998-3498 (office)
I am an Assistant Professor in
NYU's Computer Science
department, part of the Courant Institute of Mathematical Sciences.
My research focuses on machine learning and probabilistic inference.
My group is particularly interested in machine learning problems motivated by clinical medicine. For example, we are developing algorithms to learn probabilistic models for medical diagnosis directly from unstructured clinical data, automatically discovering and predicting latent (hidden) variables. We collaborate with the
Emergency Medicine Informatics Research Lab at Beth Israel Deaconess Medical Center and with Independence Blue Cross.
Previously, I was a post-doc
at Microsoft
Research New England.
My Ph.D. is in Computer Science from MIT,
where I worked with Tommi Jaakkola on
approximate inference and learning in probabilistic models.
My bachelors degree is from UC
Berkeley, in Computer
Science, where I worked with Stuart Russell's First-Order Probabilistic Logic group.
News
-
New:
We have an opening for a postdoc in my group. Please e-mail me if interested.
- I am an area chair for ICML 2013 and NIPS 2013, the sponsorship chair for AIStats 2013, and the tutorials chair for UAI 2013.
- CILVR lab website, joint with my colleagues Rob Fergus, Yann LeCun, our students and postdocs.
Teaching
Fall 2013: Introduction to Machine Learning (CSCI-UA.0480-002)
Spring 2013: Probabilistic Graphical Models (CSCI-GA.3033-006)
Fall 2012: Introduction to Machine Learning (CSCI-UA.0480-002)
Publications
Theses:
- D. Sontag. Approximate
Inference in Graphical Models using LP
Relaxations. Ph.D. thesis, Massachusetts Institute of Technology, 2010.
George M. Sprowls Award for the best doctoral theses in Computer
Science at MIT (2010). BibTex
- D. Sontag. Cutting Plane Algorithms for Variational Inference in
Graphical Models. Master's thesis, Massachusetts Institute of Technology, 2007. BibTex
Machine learning:
Download code implementing
our UAI '12 paper (see readme file).
Download optimized code implementing
our UAI '08 paper (see readme file).
- E. Brenner, D. Sontag. SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. To appear in Uncertainty in Artificial Intelligence (UAI) 29, July 2013.
- Y. Halpern, D. Sontag. Unsupervised Learning of Noisy-OR Bayesian Networks. To appear in Uncertainty in Artificial Intelligence (UAI) 29, July 2013.
- S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, M. Zhu. A Practical Algorithm for Topic Modeling with Provable Guarantees. To appear in the 30th International Conference on Machine Learning (ICML), 2013. Supplementary
- D. Sontag, D. K. Choe, Y. Li. Efficiently Searching for Frustrated Cycles in MAP Inference. Uncertainty in Artificial Intelligence (UAI) 28, Aug. 2012. Supplementary BibTex
- Y. Halpern, S. Horng, L. A. Nathanson, N. I. Shapiro, D. Sontag. A Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text. ICML 2012 Workshop on Clinical Data Analysis, July 2012. BibTex
- D. Sontag, K. Collins-Thompson, P. N. Bennett, R. W. White, S. Dumais, B. Billerbeck. Probabilistic Models for Personalizing Web Search. Fifth ACM International Conference on Web Search and Data Mining (WSDM), Feb. 2012. [Slides]
BibTex
- D. Sontag, D. Roy. Complexity of Inference in Latent Dirichlet Allocation. Neural Information Processing Systems (NIPS)
25, Dec. 2011. [Slides]
BibTex
- K. Collins-Thompson, P. N. Bennett, R. W. White, S. de la Chica, D. Sontag. Personalizing Web Search Results by Reading Level. Twentieth ACM International Conference on Information and Knowledge Management (CIKM 2011), Oct. 2011.
BibTex
-
D. Sontag, A. Globerson, T. Jaakkola. Introduction to Dual Decomposition for Inference. Optimization for Machine Learning, editors S. Sra, S. Nowozin, and S. J. Wright: MIT Press, 2011.
BibTex
- D. Sontag, O. Meshi, T. Jaakkola,
A. Globerson. More data means less
inference: A pseudo-max approach to structured learning.
Neural Information Processing Systems (NIPS)
24, Dec. 2010. Supplementary
BibTex
- T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag. Dual Decomposition for Parsing with Non-Projective Head Automata. Empirical Methods in Natural Language Processing (EMNLP), 2010. Best paper award. BibTex
- A. Rush, D. Sontag, M. Collins, and T. Jaakkola.
On Dual Decomposition and
Linear Programming
Relaxations for
Natural Language
Processing. Empirical Methods in Natural Language Processing (EMNLP), 2010. BibTex
- O. Meshi, D. Sontag, T. Jaakkola, A. Globerson. Learning Efficiently with Approximate Inference via Dual Losses. 27th International Conference on Machine Learning (ICML), July 2010.
BibTex
- T. Jaakkola, D. Sontag, A. Globerson,
M. Meila. Learning
Bayesian Network Structure using LP Relaxations. 13th International Conference on Artificial Intelligence
and Statistics (AI-STATS),
2010. BibTex
- D. Sontag, T. Jaakkola. Tree Block Coordinate Descent for MAP in Graphical Models. 12th International Conference on Artificial Intelligence and Statistics (AI-STATS), April 2009. BibTex
- D. Sontag, A. Globerson, T. Jaakkola. Clusters and Coarse Partitions in LP Relaxations. Neural Information Processing Systems
(NIPS) 21, Dec. 2008. BibTex
- D. Sontag, T. Meltzer, A. Globerson, Y. Weiss, T. Jaakkola. Tightening
LP Relaxations for MAP using Message Passing. Uncertainty
in Artificial Intelligence (UAI) 24, July 2008. Best paper award. BibTex
- D. Sontag, T. Jaakkola. New
Outer Bounds on the Marginal Polytope. Neural Information Processing Systems
(NIPS) 20, Dec. 2007. Outstanding student paper award. BibTex
Computer networking:
- D. Sontag, Y. Zhang, A. Phanishayee, D. Andersen,
D. Karger. Scaling All-Pairs Overlay Routing.
Fifth ACM International Conference on emerging
Networking EXperiments and Technologies (CoNEXT), Dec. 2009. Code BibTex
Computational biology:
- D. Sontag, R. Singh, B. Berger. Probabilistic
Modeling of Systematic Errors in Two-Hybrid Experiments.
Pacific Symposium on Biocomputing (PSB), 2007. Supplementary information BibTex
First-Order Probabilistic Logic:
- B. Milch, B. Marthi, S. Russell, D. Sontag, D.
L. Ong, and A. Kolobov. BLOG:
Probabilistic Models with Unknown Objects. In Lise Getoor
and Ben Taskar, eds. Statistical Relational Learning. Cambridge, MA:
MIT Press, 2007.
- B. Milch, B. Marthi, S. Russell, D. Sontag,
D. L. Ong, and A. Kolobov. BLOG:
Probabilistic Models with Unknown Objects. Proc. 19th
International Joint Conference on Artificial Intelligence (IJCAI):
1352-1359, 2005. BibTex
- B. Milch, B. Marthi, D. Sontag, S. Russell,
D. L. Ong, and A. Kolobov. Approximate
Inference for Infinite Contingent Bayesian Networks. 10th
International Workshop on Artificial Intelligence and
Statistics, 2005. BibTex