Computer Science
Tony Jebara works on machine learning and statistical inference. Learning involves fitting models to large-scale data by optimally estimating parameters and structure. Inference involves using these models to predict labels of interest or finding the most likely and marginal configurations of any hidden variables of interest. The models Jebara focuses on are probabilistic graphical models: large networks which describe the conditional independencies between variables in a system.
- Associate professor of computer science, Columbia University, 2008–
- Assistant professor of computer science, Columbia University, 2002–2007
- International Machine Learning Society (IMLS)
- Institute of Electrical and Electronics Engineers (IEEE)
- Association for the Advancement of Artificial Intelligence (AAAI)
- Association for Computing Machinery (ACM)
- Neural and Cognitive Computation Chair Professor at Tsinghua University, 2013-2015
- IBM Faculty Award, 2013
- Yahoo Faculty Award, 2011
- Google Faculty Award, 2009
- Best Paper Award at the 26th International Conference on Machine Learning, 2009
- IEEE ICTAI Award for Contributions to Artificial Intelligence, 2009
- National Science Foundation Career Award, 2004
- Best Student Paper Award at the 20th International Conference on Machine Learning, 2003
- Honorable Mention Winner of the 27th Annual Pattern Recognition Society Award, 2001
- Deciphering the cortex: circuit inference from large-scale brain activity data, DARPA BAA-14-59-SIMPLEX-FP-024 (Paninski, Jebara, Blei, Yuste). Total grant $2,103,864, 2015-2018.
- Approximate Learning and Inference in Graphical Models, NSF III-1526914 (PI: Jebara), $164,089, 2015-2018
- EAGER: New Optimization Methods for Machine Learning (PI: Jebara) $100,000, 2014-2018.
- D. Tang and T. Jebara. Initialization and coordinate optimization for multi-way matching. Artificial Intelligence and Statistics (AISTATs), April 2017.
- G. Gidel, S. Lacoste-Julien and T. Jebara. Frank-Wolfe algorithms for saddle point problems. Artificial Intelligence and Statistics (AISTATs), April 2017.
- A. Choromanska, K. Choromanski, M. Bojarski, T. Jebara, S. Kumar, Y. LeCun. Binary embeddings with structured hashed projections. International Conference on Machine Learning (ICML), June 2016.
- K. Tang, N. Ruozzi, D. Belanger, and T. Jebara. Bethe learning of graphical models via MAP decoding. International Conference on Artificial Intelligence and Statistics (AISTATs), May 2016.
- S.M. Bellovin, R.M. Hutchins, T. Jebara and S. Zimmeck, When Enough is Enough: Location Tracking, Mosaic Theory and Machine Learning, 8 New York University Journal of Law & Liberty 556 (2014).
- J. Wang, T. Jebara and S.-F. Chang. Semi-Supervised Learning Using Greedy Max-Cut. Journal of Machine Learning Research, Volume 14, pages 771-800, 2013.
- T. Jebara. Multitask Sparsity via Maximum Entropy Discrimination. Journal of Machine Learning Research, Volume 12, pages 75-110, 2011.
- P. Shivaswamy and T. Jebara. Maximum Relative Margin and Data-Dependent Regularization. Journal of Machine Learning Research, Volume 11, pages 665-706, 2010.
- D. Lazer, A. Pentland, L. Adamic, S. Aral, A.-L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, M. Van Alstyne. Computational Social
- Science. Science, Volume 323, Pages 721-723, February 6, 2009.
- C. Lima, U. Lall, T. Jebara, and A.G. Barnston. Statistical Prediction of ENSO from Subsurface Sea Temperature Using a Nonlinear Dimensionality Reduction, Journal of Climate, Volume 22, Number 17, Pages 4501-4519, September 1, 2009.