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Invited Talk: Tommi Jaakkola
Tommi Jaakkola
Author Information
Tommi Jaakkola (MIT)
Tommi Jaakkola is a professor of Electrical Engineering and Computer Science at MIT. He received an M.Sc. degree in theoretical physics from Helsinki University of Technology, and Ph.D. from MIT in computational neuroscience. Following a Sloan postdoctoral fellowship in computational molecular biology, he joined the MIT faculty in 1998. His research interests include statistical inference, graphical models, and large scale modern estimation problems with predominantly incomplete data.
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2021 Spotlight: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles »
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2021 : Consistent Accelerated Inference via Confident Adaptive Transformers »
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2022 : Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations »
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2022 : Is Conditional Generative Modeling all you need for Decision-Making? »
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2023 : AlphaFold Meets Flow Matching for Generating Protein Ensembles »
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2023 : Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design »
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2023 : The Discovery of Binding Modes Requires Rethinking Docking Generalization »
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2023 : AlphaFold Meets Flow Matching for Generating Protein Ensembles »
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2023 : Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design »
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2023 : Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models »
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2023 : MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design »
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2023 : Learning Interatomic Potentials at Multiple Scales »
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2023 : MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design »
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2023 : Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms »
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2023 : AlphaFold Meets Flow Matching for Generating Protein Ensembles »
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2023 : Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms »
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2023 : AlphaFold Meets Flow Matching for Generating Protein Ensembles »
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2023 : The Discovery of Binding Modes Requires Rethinking Docking Generalization »
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2023 : Fast non-autoregressive inverse folding with discrete diffusion »
John Yang · Jason Yim · Tommi Jaakkola · Regina Barzilay -
2023 : Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design »
Hannes Stärk · Bowen Jing · Regina Barzilay · Tommi Jaakkola -
2023 : Coarse-graining via reparametrization avoids force-matching and back-mapping »
Nima Dehmamy · Csaba Both · Subhro Das · Tommi Jaakkola -
2023 : Fast protein backbone generation with SE(3) flow matching »
Jason Yim · Andrew Campbell · Yue Kwang Foong · Sarah Lewis · Victor Satorras · Michael Gastegger · Bas Veeling · Jose Jimenez-Luna · Regina Barzilay · Tommi Jaakkola · Frank Noe -
2023 : MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design »
Xiang Fu · Tian Xie · Andrew Rosen · Tommi Jaakkola · Jake Smith -
2023 : Removing Biases from Molecular Representations via Information Maximization »
Chenyu Wang · Sharut Gupta · Caroline Uhler · Tommi Jaakkola -
2023 : AlphaFold Meets Flow Matching for Generating Protein Ensembles »
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Bowen Jing · Tommi Jaakkola · Bonnie Berger -
2023 : Compositional Foundation Models for Hierarchical Planning »
Anurag Ajay · Seungwook Han · Yilun Du · Shuang Li · Abhi Gupta · Tommi Jaakkola · Josh Tenenbaum · Leslie Kaelbling · Akash Srivastava · Pulkit Agrawal -
2023 : Compositional Foundation Models for Hierarchical Planning »
Anurag Ajay · Seungwook Han · Yilun Du · Shuang Li · Abhi Gupta · Tommi Jaakkola · Josh Tenenbaum · Leslie Kaelbling · Akash Srivastava · Pulkit Agrawal -
2023 : Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models »
Gabriele Corso · Yilun Xu · Valentin De Bortoli · Regina Barzilay · Tommi Jaakkola -
2023 : Contributed talk | The Discovery of Binding Modes Requires Rethinking Docking Generalization »
Gabriele Corso · Arthur Deng · Nicholas Polizzi · Regina Barzilay · Tommi Jaakkola -
2023 : Learning Interatomic Potentials at Multiple Scales »
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2023 Poster: Restart Sampling for Improving Generative Processes »
Yilun Xu · Mingyang Deng · Xiang Cheng · Yonglong Tian · Ziming Liu · Tommi Jaakkola -
2023 Poster: Compositional Sculpting of Iterative Generative Processes »
Timur Garipov · Sebastiaan De Peuter · Ge Yang · Vikas Garg · Samuel Kaski · Tommi Jaakkola -
2023 Poster: Fundamental Limits and Tradeoffs in Invariant Representation Learning »
Han Zhao · Chen Dan · Bryon Aragam · Tommi Jaakkola · Geoffrey Gordon · Pradeep Ravikumar -
2023 Poster: Compositional Foundation Models for Hierarchical Planning »
Anurag Ajay · Seungwook Han · Yilun Du · Shuang Li · Abhi Gupta · Tommi Jaakkola · Josh Tenenbaum · Leslie Kaelbling · Akash Srivastava · Pulkit Agrawal -
2022 Spotlight: Poisson Flow Generative Models »
Yilun Xu · Ziming Liu · Max Tegmark · Tommi Jaakkola -
2022 Spotlight: Lightning Talks 6B-1 »
Yushun Zhang · Duc Nguyen · Jiancong Xiao · Wei Jiang · Yaohua Wang · Yilun Xu · Zhen LI · Anderson Ye Zhang · Ziming Liu · Fangyi Zhang · Gilles Stoltz · Congliang Chen · Gang Li · Yanbo Fan · Ruoyu Sun · Naichen Shi · Yibo Wang · Ming Lin · Max Tegmark · Lijun Zhang · Jue Wang · Ruoyu Sun · Tommi Jaakkola · Senzhang Wang · Zhi-Quan Luo · Xiuyu Sun · Zhi-Quan Luo · Tianbao Yang · Rong Jin -
2022 : DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking »
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2022 Poster: Torsional Diffusion for Molecular Conformer Generation »
Bowen Jing · Gabriele Corso · Jeffrey Chang · Regina Barzilay · Tommi Jaakkola -
2022 Poster: Poisson Flow Generative Models »
Yilun Xu · Ziming Liu · Max Tegmark · Tommi Jaakkola -
2021 : Consistent Accelerated Inference via Confident Adaptive Transformers »
Tal Schuster · Adam Fisch · Tommi Jaakkola · Regina Barzilay -
2021 Poster: Understanding Interlocking Dynamics of Cooperative Rationalization »
Mo Yu · Yang Zhang · Shiyu Chang · Tommi Jaakkola -
2021 Poster: GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles »
Octavian Ganea · Lagnajit Pattanaik · Connor Coley · Regina Barzilay · Klavs Jensen · William Green · Tommi Jaakkola -
2019 Poster: Solving graph compression via optimal transport »
Vikas Garg · Tommi Jaakkola -
2019 Poster: Generative Models for Graph-Based Protein Design »
John Ingraham · Vikas Garg · Regina Barzilay · Tommi Jaakkola -
2019 Poster: Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder »
Guy Lorberbom · Andreea Gane · Tommi Jaakkola · Tamir Hazan -
2019 Poster: Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers »
Guang-He Lee · Yang Yuan · Shiyu Chang · Tommi Jaakkola -
2019 Poster: A Game Theoretic Approach to Class-wise Selective Rationalization »
Shiyu Chang · Yang Zhang · Mo Yu · Tommi Jaakkola -
2018 : Invited Talk Session 3 »
Alexandre Tkatchenko · Tommi Jaakkola · Jennifer Wei -
2018 Poster: Towards Robust Interpretability with Self-Explaining Neural Networks »
David Alvarez-Melis · Tommi Jaakkola -
2017 Poster: Local Aggregative Games »
Vikas Garg · Tommi Jaakkola -
2017 Poster: Style Transfer from Non-Parallel Text by Cross-Alignment »
Tianxiao Shen · Tao Lei · Regina Barzilay · Tommi Jaakkola -
2017 Spotlight: Style Transfer from Non-parallel Text by Cross-Alignment »
Tianxiao Shen · Tao Lei · Regina Barzilay · Tommi Jaakkola -
2017 Poster: Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network »
Wengong Jin · Connor Coley · Regina Barzilay · Tommi Jaakkola -
2016 Poster: Learning Tree Structured Potential Games »
Vikas Garg · Tommi Jaakkola -
2015 Poster: From random walks to distances on unweighted graphs »
Tatsunori Hashimoto · Yi Sun · Tommi Jaakkola -
2015 Poster: Principal Differences Analysis: Interpretable Characterization of Differences between Distributions »
Jonas Mueller · Tommi Jaakkola -
2014 Poster: Controlling privacy in recommender systems »
Yu Xin · Tommi Jaakkola -
2013 Poster: Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions »
Tamir Hazan · Subhransu Maji · Joseph Keshet · Tommi Jaakkola -
2013 Poster: On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations »
Tamir Hazan · Subhransu Maji · Tommi Jaakkola -
2012 Workshop: Machine Learning Approaches to Mobile Context Awareness »
Katherine Ellis · Gert Lanckriet · Tommi Jaakkola · Lenny Grokop -
2012 Poster: Convergence Rate Analysis of MAP Coordinate Minimization Algorithms »
Ofer Meshi · Tommi Jaakkola · Amir Globerson -
2011 Tutorial: Linear Programming Relaxations for Graphical Models »
Amir Globerson · Tommi Jaakkola -
2010 Spotlight: More data means less inference: A pseudo-max approach to structured learning »
David Sontag · Ofer Meshi · Tommi Jaakkola · Amir Globerson -
2010 Poster: More data means less inference: A pseudo-max approach to structured learning »
David Sontag · Ofer Meshi · Tommi Jaakkola · Amir Globerson -
2008 Workshop: Approximate inference - how far have we come? »
Amir Globerson · David Sontag · Tommi Jaakkola -
2008 Poster: Clusters and Coarse Partitions in LP Relaxations »
David Sontag · Amir Globerson · Tommi Jaakkola -
2008 Spotlight: Clusters and Coarse Partitions in LP Relaxations »
David Sontag · Amir Globerson · Tommi Jaakkola -
2007 Oral: New Outer Bounds on the Marginal Polytope »
David Sontag · Tommi Jaakkola -
2007 Poster: New Outer Bounds on the Marginal Polytope »
David Sontag · Tommi Jaakkola -
2007 Poster: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations »
Amir Globerson · Tommi Jaakkola -
2006 Talk: Approximate inference using planar graph decomposition »
Amir Globerson · Tommi Jaakkola -
2006 Poster: Approximate inference using planar graph decomposition »
Amir Globerson · Tommi Jaakkola -
2006 Poster: Game Theoretic Algorithms for Protein-DNA binding »
Luis Perez-Breva · Luis E Ortiz · Chen-Hsiang Yeang · Tommi Jaakkola -
2006 Spotlight: Game Theoretic Algorithms for Protein-DNA binding »
Luis Perez-Breva · Luis E Ortiz · Chen-Hsiang Yeang · Tommi Jaakkola -
2006 Poster: Parameter Expanded Variational Bayesian Methods »
Yuan (Alan) Qi · Tommi Jaakkola