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You can view all the papers in reverse
chronological order, sets of papers related to broad categories such as
machine learning,
natural language processing,
chemistry,
computational biology,
or physics, or papers in more specific areas including
inference, semi-supervised learning , information retrieval, or
reinforcement learning.
Computational biology papers
B. Jing, H. Stärk, T. Jaakkola, and B. Berger.
Generative modeling of molecular dynamics trajectories.
In Neural Information Processing Systems (NeurIPS), 2024.
[link]
B. Jing, B. Berger, and T. Jaakkola.
Alphafold meets flow matching for generating protein ensembles.
In International Conference on Machine Learning (ICML), 2024.
[link]
A. Campbell, J. Yim, R. Barzilay, T. Rainforth, and T. Jaakkola.
Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design.
In International Conference on Machine Learning (ICML), 2024.
[link]
H. Stärk, B. Jing, C. Wang, G. Corso, B. Berger, R. Barzilay, and T. Jaakkola.
Dirichlet flow matching with applications to dna sequence design.
In International Conference on Machine Learning (ICML), 2024.
[link]
J. Yim, H. Stärk, G. Corso, B. Jing, R. Barzilay, and T. Jaakkola.
Diffusion models in protein structure and docking.
WIREs Computational Molecular Science, 14(2):e1711, 2024.
[link]
G. Corso, A. Deng, N. Polizzi, R. Barzilay, and T. Jaakkola.
The discovery of binding modes requires rethinking docking generalization.
In The 12th International Conference on Learning Representations (ICLR), 2024.
C. Wang, S. Gupta, C. Uhler, and T. Jaakkola.
Removing biases from molecular representations via information maximization.
In The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
B. Jing, T. Jaakkola, and B. Berger.
Learning scalar fields for molecular docking with fast fourier transforms.
In The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
A. Kirjner, J. Yim, R. Samusevich, S. Bracha, T. Jaakkola, R. Barzilay, and I. R. Fiete.
Improving protein optimization with smoothed fitness landscapes.
In The 12th International Conference on Learning Representations (ICLR), 2024.
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J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, N. Hanikel, S. J. Pellock, A. Courbet, W. Sheffler, J. Wang, P. Venkatesh, I. Sappington, S. Vazquez Torres, A. Lauko, V. De Bortoli, E. Mathieu, S. Ovchinnikov, R. Barzilay, T. S. Jaakkola, F. DiMaio, M. Baek, and D. Baker.
De novo design of protein structure and function with rfdiffusion.
Nature, 620:1089–1100, 2023.
[link]
G. Liu, D. Catacutan, K. Rathod, K. Swanson, W. Jin, J. Mohammed, A. Chiappino-Pepe, S. Syed, M. Fragis, K. Rachwalski, J. Magolan, M. Surette, B. Coombes, T. Jaakkola, R. Barzilay, J. J. Collins, and J. M. Stokes.
Deep learning-guided discovery of an antibiotic targeting acinetobacter baumannii.
Nature Chemical Biology, 2023.
[link] [pdf]
F. Wong, A. Krishnan, E. Zheng, H. St\ärk, A. Manson, A. Earl, T. Jaakkola, and J. Collins.
Benchmarking alphafold-enabled molecular docking predictions for antibiotic discovery in molecular systems biology.
Molecular Systems Biology, 18(9), 2022.
[link]
W. Jin, J. Stokes, T. Eastman, Z. Itkin, A. V. Zakharov, J. J. Collins, T. Jaakkola, and R. 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]
K. Yang, S. Goldman, W. Jin, A. Lu, R. Barzilay, T. Jaakkola, and C. Uhler.
Improved conditional flow models for molecule to image synthesis.
In Computer Vision and Pattern Recognition (CVPR), 2021.
[link]
W. Jin, R. Barzilay, and T. Jaakkola.
Discovering synergistic drug combinations for covid with biological bottleneck models.
In NeurIPS Machine Learning for Molecules Workshop, 2020.
[link]
J. Mueller, T. Jaakkola, and D. Gifford.
Modeling persistent trends in distributions.
Journal of the American Statistical Association, 2017.
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R. Sherwood, T. Hashimoto, C. O'Donnell, S. Lewis, A. Barkal, J.P. van Hoff, V. Karun, T. Jaakkola, and D. Gifford.
Discovery of directional and nondirectional pioneer transcription factors by modeling dnase profile magnitude and shape.
Nature Biotechnology, 32(2):171--178, 2014.
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T. Hashimoto, T. Jaakkola, R. Sherwood, E. Mazzoni, H. Witchterle, and D. Gifford.
Lineage based identification of cellular states and expression programs.
In Proceedings of the 20th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2012.
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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.
[link]
G. Gerber, R. Dowell, T. Jaakkola, and D. Gifford.
Automated discovery of functional generality of human gene expression programs.
PloS Computational biology, 2007.
[link]
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]
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] [errata]
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.
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L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
Dna binding and games.
MIT CSAIL Technical Report TR-2006-018, 2006.
[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]
C-H. Yeang, T. Ideker, and T. Jaakkola.
Physical network models.
Journal of Computational Biology, 11(2-3):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):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):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):241--256, 2003.
[pdf]
C-H. Yeang and T. Jaakkola.
Time series analysis of gene expression and location data.
In Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, pages 305--312, 2003.
[pdf]
C-H. Yeang and T. Jaakkola.
Physical network models and multi-source data integration.
In The Seventh Annual International Conference on Research in Computational Molecular Biology, 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.
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Z. Bar-Joseph, G. Gerber, D. Gifford, and T. Jaakkola.
A new approach to analyzing gene expression time series data.
In The Sixth Annual International Conference on Research in Computational Molecular Biology, 2002.
[pdf]
A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
Combining location and expression data for principled discovery of genetic regulatory network models.
In Pacific Symposium on Biocomputing, volume 7, 2002.
[pdf]
Z. Bar-Joseph, D. Gifford, and T. Jaakkola.
Fast optimal leaf ordering for hierarchical clustering.
In Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001.
[pdf]
A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
Maximum likelihood estimation of optimal scaling factors for expression array normalization.
In Microarrays: Optical Technologies and Informatics, Proceedings of SPIE, volume 4266, 2001.
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A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.
In Pacific Symposium on Biocomputing, volume 6, pages 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):95--114, 2000.
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T. Jaakkola, M. Diekhans, and D. Haussler.
Using the fisher kernel method to detect remote protein homologies.
In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, 1999.
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T. Jaakkola and D. Haussler.
Exploiting generative models in discriminative classifiers.
In Advances in Neural Information Processing Systems 11, 1998.
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