Tommi S. Jaakkola, Ph.D. Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society MIT Computer Science and Artificial Intelligence Laboratory Stata Center, Bldg 32-G470 Cambridge, MA 02139 tommi at csail dot mit dot edu [home]   [papers]   [research]   [courses]   [people]

The list does not include all recent preprints from arXiv or bioRxiv. For a more complete list, see my Google scholar page

Computational biology papers

• Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
  AI-based methods for simulating, sampling, and predicting protein ensembles.
  Current Opinion in Structural Biology, 98. 2026.
  [link]
• Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Raut Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi S. Jaakkola, Rohith Krishna, and David Baker.
  Atom-level enzyme active site scaffolding using RFdiffusion2.
  Nature Methods, 23, pp. 96–105. 2025.
  [link]
• Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, and Tommi Jaakkola.
  Learning Diffusion Models with Flexible Representation Guidance.
  Neural Information Processing Systems (NeurIPS), 2025.
  [link]
• Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, and Tommi Jaakkola.
  Identifying biological perturbation targets through causal differential networks.
  International Conference on Machine Learning (ICML), 2025.
  [link]
• Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, and Tommi Jaakkola.
  Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations.
  International Conference on Machine Learning (ICML), 2025.
  [link]
• Gabriel Corso, Vignesh Ram Somnath, Noah Getz, Regina Barzilay, Tommi Jaakkola, and Andreas Krause.
  Composing Unbalanced Flows for Flexible Docking and Relaxation.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, and Karsten Kreis.
  ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi Jaakkola, and Caroline Uhler.
  An Information Criterion for Controlled Disentanglement of Multimodal Data.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Mostafa Karimi, Sharmi Banerjee, Tommi Jaakkola, Bella Dubrov, Shang Shang, and Ron Benson.
  Data Distillation for extrapolative protein design through exact preference optimization.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, and Aviv Regev.
  Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design.
  The 13th International Conference on Learning Representations (ICLR), 2025.
  [link]
• Bowen Jing, Hannes Stark, Tommi Jaakkola, and Bonnie Berger.
  Generative Modeling of Molecular Dynamics Trajectories.
  Neural Information Processing Systems (NeurIPS), 2024.
  [link]
• Bowen Jing, Bonnie Berger, and Tommi Jaakkola.
  AlphaFold Meets Flow Matching for Generating Protein Ensembles.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola.
  Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Hannes Stark, Bowen Jing, Chenyu Wang, Gabriel Corso, Bonnie Berger, Regina Barzilay, and Tommi Jaakkola.
  Dirichlet Flow Matching with Applications to DNA Sequence Design.
  International Conference on Machine Learning (ICML), 2024.
  [link]
• Jason Yim, Hannes Stark, Gabriel Corso, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
  Diffusion models in protein structure and docking.
  WIREs Computational Molecular Science, 14(2), pp. e1711. 2024.
  [link]
• Gabriele Corso, Hannes Stark, Stefanie Jegelka, Tommi Jaakkola, and Regina Barzilay.
  Graph Neural Networks.
  Nature Reviews Methods Primers, 4(17). 2024.
  [link]
• Gabriele Corso, Arthur Deng, Benjamin Fry, Nicholas Polizzi, Regina Barzilay, and Tommi Jaakkola.
  Deep Confident Steps to New Pockets: Strategies for Docking Generalization.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Chenyu Wang, Sharut Gupta, Caroline Uhler, and Tommi Jaakkola.
  Removing Biases from Molecular Representations via Information Maximization.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Bowen Jing, Tommi Jaakkola, and Bonnie Berger.
  Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Andrew Kirjner, Jason Yim, Raman Samusevich, Shahar Bracha, Tommi Jaakkola, Regina Barzilay, and Ila Fiete.
  Improving protein optimization with smoothed fitness landscapes.
  The 12th International Conference on Learning Representations (ICLR), 2024.
  [link]
• Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vazquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker.
  De novo design of protein structure and function with RFdiffusion.
  Nature, 620, pp. 1089–1100. 2023.
  [link]
• Gary Liu, Denise B. Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody C. Mohammed, Anush Chiappino-Pepe, Saad A. Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael G. Surette, Brian K. Coombes, Tommi Jaakkola, Regina Barzilay, James J. Collins, and Jonathan M. Stokes.
  Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.
  Nature Chemical Biology, 2023.
  [link] [pdf]
• Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stark, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, and James J Collins.
  Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery in Molecular Systems Biology.
  Molecular Systems Biology, 18(9). 2022.
  [link]
• Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey V. Zakharov, James J. Collins, Tommi S. Jaakkola, and Regina Barzilay.
  Deep learning identifies synergistic drug combinations for treating COVID-19.
  Proceedings of the National Academy of Sciences of the USA (PNAS), 118(39). 2021.
  [link]
• Karren Yang, Samuel Goldman, Wengong Jin, Alex Lu, Regina Barzilay, Tommi Jaakkola, and Caroline Uhler.
  Improved conditional flow models for molecule to image synthesis.
  Computer Vision and Pattern Recognition (CVPR), 2021.
  [link]
• Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
  Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models.
  NeurIPS Machine Learning for Molecules Workshop, 2020.
  [link]
• Jonas Mueller, Tommi Jaakkola, and David Gifford.
  Modeling Persistent Trends in Distributions.
  Journal of the American Statistical Association, 2017.
  [pdf]
• Richard I Sherwood, Tatsunori Hashimoto, Charles W O’Donnell, Sophia Lewis, Amira A Barkal, John Peter van Hoff, Vivek Karun, Tommi Jaakkola, and David K Gifford.
  Discovery of directional and nondirectional pioneer transcription factors by modeling Dnase profile magnitude and shape.
  Nature Biotechnology, 32(2), pp. 171–178. 2014.
  [pdf]
• Tatsunori Hashimoto, Tommi Jaakkola, Richard Sherwood, Esteban O Mazzoni, Hynek Wichterle, and David Gifford.
  Lineage based identification of cellular states and expression programs.
  Proceedings of the 20th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2012.
  [pdf]
• Yuchun Guo, Georgios Papachristoudis, Robert C. Altshuler, Georg K. Gerber, Tommi S. Jaakkola, David K. Gifford, and Shaun Mahony.
  Discovering homotypic binding events at high spatial resolution.
  Bioinformatics, 2010.
  [link]
• Georg K. Gerber, Robin D. Dowell, Tommi S. Jaakkola, and David K. Gifford.
  Automated discovery of functional generality of human gene expression programs.
  PloS Computational biology, 2007.
  [link]
• Luis Perez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, and Tommi Jaakkola.
  Game theoretic algorithms for protein-DNA binding.
  Advances in Neural Information Processing Systems (NIPS), 2006.
  [pdf]
• Yuan Qi, Alex Rolfe, Kenzie D MacIsaac, Georg K Gerber, Dmitry Pokholok, Julia Zeitlinger, Timothy Danford, Robin D Dowell, Ernest Fraenkel, Tommi S Jaakkola, Richard A Young, and David K Gifford.
  High-resolution computational models of genome binding events.
  Nature Biotechnology, 24, pp. 963-970. 2006.
  [pdf] [errata]
• Yuan Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford, and Hui Ge.
  Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo.
  Bioinformatics, 22(14), pp. 417-423. 2006.
  [pdf]
• Luis Perez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, and Tommi Jaakkola.
  DNA Binding and Games.
  2006.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Modeling the combinatorial functions of multiple transcription factors.
  The Ninth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 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), pp. R62. 2005.
  [pdf] [link]
• C-H. Yeang, T. Ideker, and T. Jaakkola.
  Physical network models.
  Journal of Computational Biology, 11(2-3), pp. 243-263. 2004.
  [pdf]
• Z. Bar-Joseph, G. Gerber, I. Simon, D. Gifford, and T. Jaakkola.
  Comparing continuous representations of time series expression profiles to identify differentially expressed genes.
  Proceedings of the National Academy of Sciences, 100(18), pp. 10146-10151. 2003.
  [pdf] [link]
• Z. Bar-Joseph, G. Gerber, T. Lee, N. Rinaldi, J. Yoo, F. Robert, B. Gordon, E. Fraenkel, T. Jaakkola, R. Young, and D. Gifford.
  Computational Discovery of Gene Modules and Regulatory Networks.
  Nature Biotechnology, 21(11), pp. 1337-1342. 2003.
  [pdf] [link]
• Z. Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon.
  Continuous Representations of Time Series Gene Expression Data.
  Journal of Computational Biology, 10(3-4), pp. 241-256. 2003.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Time series analysis of gene expression and location data.
  Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, pp. 305-312. 2003.
  [pdf]
• C-H. Yeang and T. Jaakkola.
  Physical network models and multi-source data integration.
  The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2003.
  [pdf]
• Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angele M. Hamel, Tommi S. Jaakkola, and Nathan Srebro.
  K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data.
  Bioinformatics, 2002.
  [pdf]
• Z. Bar-Joseph, G. Gerber, D. Gifford, and T. Jaakkola.
  A New Approach to Analyzing Gene Expression Time Series Data.
  The Sixth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2002.
  [pdf]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Combining location and expression data for principled Discovery of genetic regulatory network models.
  Pacific Symposium on Biocomputing, 7. 2002.
  [pdf]
• Z. Bar-Joseph, D. Gifford, and T. Jaakkola.
  Fast Optimal Leaf Ordering for Hierarchical Clustering.
  Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology (ISMB), 2001.
  [pdf]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Maximum likelihood estimation of optimal scaling factors for expression array normalization.
  Microarrays: Optical Technologies and Informatics, Proceedings of SPIE, 4266. 2001.
  [ps]
• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks.
  Pacific Symposium on Biocomputing, 6, pp. 422-433. 2001.
  [pdf]
• T. Jaakkola, M. Diekhans, and D. Haussler.
  A discriminative framework for detecting remote protein homologies.
  Journal of Computational Biology, 7(1,2), pp. 95–114. 2000.
  [ps]
• T. Jaakkola, M. Diekhans, and D. Haussler.
  Using the Fisher kernel method to detect remote protein homologies.
  Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB), 1999.
  [ps]
• T. Jaakkola and D. Haussler.
  Exploiting generative models in discriminative classifiers.
  Advances in Neural Information Processing Systems (NIPS), 1998.
  [ps]