Tommi S. Jaakkola, Ph.D.
Professor of Electrical Engineering and Computer Science

MIT Computer Science and Artificial Intelligence Laboratory
Stata Center, Bldg 32-G498
Cambridge, MA 02139

E-mail: tommi at csail.mit.edu

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Research synopsis (more...)

On the theoretical side, our research focuses on statistical inference and estimation, development of principled approximation methods for problems with limited computational resources, analysis and development of algorithms for various modern estimation problems such as those involving predominantly incomplete data. The applied side of our work involves primarily functional genomics (transcriptional regulation), large scale inference problems, and information retrieval.

Students/postdocs (more...)

Tatsu Hashimoto, Paresh Malalur, Yu Xin

Recent tutorials

NIPS*2011 Tutorial with Amir Globerson.

  • Part 1 (Jaakkola) [pdf]
  • Part 2 (Globerson) [pdf]

Recent papers (more...)

  • D. Sontag, A. Globerson, and T. Jaakkola.
    Introduction to dual decomposition for inference.
    In S. Sra, S. Nowozin, and S. Wright, Eds., Optimization for Machine Learning. MIT Press, 2010. To appear.
    [pdf]

  • Y. Guo, G. Papachristoudis, R. Altshuler, G. Gerber, T. Jaakkola, D. Gifford, and S. Mahony.
    Discovering homotypic binding events at high spatial resolution.
    Bioinformatics, 2010. To appear.
    [link to paper]

  • D. Sontag, O. Meshi, T. Jaakkola, and A. Globerson.
    More data means less inference: A pseudo-max approach to structured learning.
    In Advances in Neural Information Processing Systems 24, 2010. To appear.
    [pdf]

  • A. Rush, D. Sontag, M. Collins, and T. Jaakkola.
    On dual decomposition and linear programming relaxations for natural language processing.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
    [pdf]

  • T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag.
    Dual decomposition for parsing with non-projective head automata.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010. Best paper award.
    [pdf]

  • E. Minkov, B. Charrow, J. Ledlie, S. Teller, and T. Jaakkola.
    Collaborative future event recommendation.
    In International Conference on Information and Knowledge Management, 2010. To appear.
    [pdf]

  • O. Meshi, D. Sontag, T. Jaakkola, and A. Globerson.
    Learning efficiently with approximate inference via dual losses.
    In Proceedings of the 27th International Conference on Machine Learning, 2010.
    [pdf]

  • T. Jaakkola, D. Sontag, A. Globerson, and M. Meila.
    Learning bayesian network structure using lp relaxations.
    In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010.
    [pdf], [related pdf slides]

  • D. Sontag and T. Jaakkola.
    Tree block coordinate descent for map in graphical models.
    In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, 2009.
    [pdf]

  • D. Sontag, A. Globerson, and T. Jaakkola.
    Clusters and coarse partitions in lp relaxations.
    In Advances in Neural Information Processing Systems 21, 2008.
    [pdf]

  • D. Sontag, T. Meltzer, A. Globerson, T. Jaakkola, and Y. Weiss.
    Tightening lp relaxations for map using message passing.
    In Proceedings of the 24rd Conference on Uncertainty in Artificial Intelligence, 2008.
    [pdf]

  • D. Sontag and T. Jaakkola.
    New outer bounds on the marginal polytope.
    In Advances in Neural Information Processing Systems 20, 2007.
    [pdf]

  • A. Globerson and T. Jaakkola.
    Fixing max-product: Convergent message passing algorithms for map lp-relaxations.
    In Advances in Neural Information Processing Systems 20, 2007.
    [pdf]

  • G. Gerber, R. Dowell, T. Jaakkola, and D. Gifford.
    Automated discovery of functional generality of human gene expression programs.
    PloS Computational biology, 2007.
    [link to paper]

  • A. Globerson and T. Jaakkola.
    Convergent propagation algorithms via oriented trees.
    In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
    [pdf]

  • D. Sontag and T. Jaakkola.
    On iteratively constraining the marginal polytope for approximate inference and map.
    Technical report, 2007.
    [pdf]

  • A. Globerson and T. Jaakkola.
    Approximate inference using conditional entropy decompositions.
    In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, 2007.
    [pdf]

  • H. Steck and T. Jaakkola.
    Predictive discretization during model selection.
    In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, 2007.
    [pdf]

  • A. Globerson and T. Jaakkola.
    Approximate inference using planar graph decomposition.
    In Advances in Neural Information Processing Systems 19, 2006.
    [pdf]

  • L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
    Game theoretic algorithms for protein-dna binding.
    In Advances in Neural Information Processing Systems 19, 2006.
    [pdf]

  • A. Qi and T. Jaakkola.
    Parameter expanded variational bayesian methods.
    In Advances in Neural Information Processing Systems 19, 2006.
    [pdf]

  • Y. Qi, A. Rolfe, K. MacIsaac, G. Gerber, D. Pokholok, J. Zeitlinger, T. Danford, R. Dowell, E. Fraenkel, T. Jaakkola, R. Young, and D. Gifford.
    High-resolution computational models of genome binding events.
    Nature Biotechnology, 24:963--970, 2006.
    [pdf], [pdf]

  • Y. Qi, P. Missiuro, A. Kapoor, C. Hunter, T. Jaakkola, D. Gifford, and H. Ge.
    Semi-supervised analysis of gene expression profiles for lineage-specific development in the caenorhabditis elegans embryo.
    Bioinformatics, 22(14):417--423, 2006.
    [pdf]

  • L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
    Dna binding and games.
    MIT CSAIL Technical Report TR-2006-018, 2006.
    [pdf]

  • A. Corduneanu and T. Jaakkola.
    Data dependent regularization.
    In Semi-supervised learning. MIT Press, 2006.
    [pdf]

  • M. Wainwright, T. Jaakkola, and A. Willsky.
    Map estimation via agreement on (hyper)trees: Message-passing and linear-programming approaches.
    IEEE Transactions on Information Theory, 51(11):3697--3717, 2005.
    [pdf]

  • J. Rennie and T. Jaakkola.
    Using term informativeness for named entity detection.
    In Proceedings of the 28th Annual Conference on Research and Development in Information Retrieval (SIGIR), 2005.
    [pdf]

  • C-H. Yeang and T. Jaakkola.
    Modeling the combinatorial functions of multiple transcription factors.
    In The Ninth Annual International Conference on Research in Computational Molecular Biology, 2005.
    [pdf]

  • C-H. Yeang, H. Mak, S. McCuine, C. Workman, T. Jaakkola, and T. Ideker.
    Validation and refinement of gene-regulatory pathways on a network of physical interactions.
    Genome Biology, 6(7):R62, 2005.
    [pdf], [link to paper]

  • M. Wainwright, T. Jaakkola, and A. Willsky.
    A new class of upper bounds on the log partition function.
    IEEE Transactions on Information Theory, 51:2313--2335, 2005.
    [pdf]

  • R. Rosales and T. Jaakkola.
    Focused inference.
    In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.
    [pdf]

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