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Our research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. Our research in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. We design new methods, theory and algorithms so as to automate the use and generation of semi-structured data such as molecules, natural language text, images, or strategies. Our algorithms solve multi-faceted inferential tasks (e.g., in a biomedical context), generate or optimize molecules / reactions towards effective therapeutics, and help model strategic, game theoretic interactions.
Julia Balla(c), Abhi Gupta, Cathy Cai(c), MinGyu Choi(c), Cameron Diao(c), Felix Faltings(c), Peter Holderrieth, Maurice Weiler*, Cai Zhou(c)
(* = postdoc, c = co-advised, v = visiting)
Recent PhD graduates: Jeet Mohapatra, Chenyu Wang (Google), Shangyuan Tong (Tesla), Bowen Jing (Princeton), Hannes Stärk (Boltz PBC)
Recent MSc graduates: Ron Shprints (DE Shaw)
Recent highlight(s)
We introduce an all-atom generative model -- BoltzGen -- for designing proteins and peptides across all modalities to bind a wide range of biomolecular targets. BoltzGen builds strong structural reasoning capabilities about target-binder interactions into its generative design process and is controlled by a flexible design specification language. We experimentally validate these capabilities in a total of eight diverse wetlab design campaigns. Model weights, code for data, inference and training are released under the MIT license.
H. Stärk, F. Faltings, M. Choi, Y. Xie, E. Hur, T. O Donnell, A. Bushuiev, T. Ucar, S. Passaro, W. Mao, M. Reveiz, R. Bushuiev, T. Pluskal, Josef Sivic, Karsten Kreis, A. Vahdat, S. Ray, J. Goldstein, A. Savinov, J. Hambalek, A. Gupta, D. Taquiri-Diaz, Y. Zhang, A. K. Hatstat, A. Arada, N. H. Kim, E. Tackie-Yarboi, D. Boselli, L. Schnaider, C. C. Liu, G.-W. Li, D. Hnisz, D. M. Sabatini, W. F. DeGrado, J. Wohlwend, G. Corso, R. Barzilay and T. Jaakkola.
BoltzGen: Toward Universal Binder Design. Preprint.
[bioRxiv], [GitHub]
- Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang, Chubin Zhang, Muhan Zhang, Lester Mackey, Tommi Jaakkola, Stephen Bates, and Dinghuai Zhang.
Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner.
International Conference on Machine Learning (ICML), 2026.
[link]
- Abhi Gupta, Polina Barabanshchikova, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models.
International Conference on Machine Learning (ICML), 2026.
[link]
- Peter Holderrieth, Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong He, Zeynep Akata, Tommi Jaakkola, Nicholas Matthew Boffi, and Max Simchowitz.
Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps.
International Conference on Machine Learning (ICML), 2026.
[link]
- 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]
- Shangyuan Tong, Nanye Ma, Saining Xie, and Tommi Jaakkola.
Flow Map Distillation Without Data.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
[link]
- Peter Holderrieth, Uriel Singer, Tommi Jaakkola, Ricky T. Q. Chen, Yaron Lipman, and Brian Karrer.
GLASS Flows: Efficient Inference for Reward Alignment of Flow and Diffusion Models.
The 14th International Conference on Learning Representations (ICLR), 2026.
[link]
- Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, and Bo Liu.
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models.
The 14th International Conference on Learning Representations (ICLR), 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]
- Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, and Tommi Jaakkola.
Next Semantic Scale Prediction via Hierarchical Diffusion Language Models.
Neural Information Processing Systems (NeurIPS), 2025.
[link]
- Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, Manasi Mandal, Kiran Mak, Denisse Cordova Carrizales, Nguyen Tuan Hung, Xiang Fu, Bowen Han, Yao Wang, Weiwei Xie, Robert J. Cava, Tommi S. Jaakkola, Yongqiang Cheng, and Mingda Li.
Structural constraint integration in a generative model for the discovery of quantum materials.
Nature Materials, 2025.
[link]
- Menghua Wu, Cai Zhou, Stephen Bates, and Tommi Jaakkola.
Thought calibration: Efficient and confident test-time scaling.
Empirical Methods in Natural Language Processing (EMNLP), 2025.
[link]
- Peter Holderrieth, Michael S. Albergo, and Tommi Jaakkola.
LEAPS: A discrete neural sampler via locally equivariant networks.
International Conference on Machine Learning (ICML), 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]
- Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, and Saining Xie.
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.
In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2025.
[link]
- Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, and Yaron Lipman.
Generator Matching: Generative modeling with arbitrary Markov processes.
The 13th International Conference on Learning Representations (ICLR), 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]
- Yujian Liu, Shiyu Chang, Tommi Jaakkola, and Yang Zhang.
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning.
The 13th International Conference on Learning Representations (ICLR), 2025.
[link]
- Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stark, Yilun Xu, Tommi Jaakkola, and Rafael Gomez-Bombarelli.
Think while You Generate: Discrete Diffusion with Planned Denoising.
The 13th International Conference on Learning Representations (ICLR), 2025.
[link]
- Peter Holderrieth, Yilun Xu, and Tommi Jaakkola.
Hamiltonian Score Matching and Generative Flows.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, and Stefanie Jegelka.
Symmetries In-Context: Universal Self-Supervised Learning through Contextual World Models.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Xiang Fu, Andrew Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess Smidt, and Tommi Jaakkola.
A Recipe for Charge Density Prediction.
Neural Information Processing Systems (NeurIPS), 2024.
[link]
- Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, and Tommi Jaakkola.
Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations.
Neural Information Processing Systems (NeurIPS), 2024.
[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]
- Yilun Xu, Gabriel Corso, Tommi Jaakkola, Arash Vahdat, and Karsten Kreis.
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents.
International Conference on Machine Learning (ICML), 2024.
[link]
- Hannes Stark, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site 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]
- Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, and Mingda Li.
Virtual node graph neural network for full phonon prediction.
Nature Computational Science, 4(7). 2024.
[link]
- Yujian Liu, Yang Zhang, Tommi Jaakkola, and Shiyu Chang.
Correcting Diffusion Generation through Resampling.
Computer Vision and Pattern Recognition (CVPR), 2024.
[link]
- Gabriele Corso, Hannes Stark, Stefanie Jegelka, Tommi Jaakkola, and Regina Barzilay.
Graph Neural Networks.
Nature Reviews Methods Primers, 4(17). 2024.
[link]
- Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, and Jake Smith.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.
The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
- Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, and Tommi Jaakkola.
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.
The 12th International Conference on Learning Representations (ICLR), 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]
- Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, and Regina Barzilay.
Conformal Language Modeling.
The 12th International Conference on Learning Representations (ICLR), 2024.
[link]
- Brent A. Koscher, Richard B. Canty, Matthew A. McDonald, Kevin P. Greenman, Charles J. McGill, Camille L. Bilodeau, Wengong Jin, Haoyang Wu, Florence H. Vermeire, Brooke Jin, Travis Hart, Timothy Kulesza, Shih-Cheng Li, Tommi S. Jaakkola, Regina Barzilay, Rafael Gomez-Bombarelli, William H. Green, and and Klavs F. Jensen.
Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.
Science, 382. 2023.
[link]
- Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
Compositional Sculpting of Iterative Generative Processes.
Neural Information Processing Systems (NeurIPS), 2023.
[link]
- Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, and Tommi Jaakkola.
Restart Sampling for Improving Generative Processes.
Neural Information Processing Systems (NeurIPS), 2023.
[link]
- Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie Kaelbling, Akash Srivastava, and Pulkit Agrawal.
Hierarchical Planning with Foundation Models.
Neural Information Processing Systems (NeurIPS), 2023.
[link]
- Xiang Fu, Tian Xie, Nathan J. Rebello, Bradley D. Olsen, and Tommi Jaakkola.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.
Transactions on Machine Learning Research (TMLR), 2023.
[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]
- Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, and Tommi Jaakkola.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.
International Conference on Machine Learning (ICML), 2023.
[link]
- Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola.
SE(3) diffusion model with application to protein backbone generation.
International Conference on Machine Learning (ICML), 2023.
[link]
- Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi Jaakkola, and Shiyu Chang.
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models.
International Conference on Machine Learning (ICML), 2023.
[link]
- Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola.
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
Transactions on Machine Learning Research (TMLR), 2023.
[link]
- Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes Stark, Menghua Wu, Gabriele Corso, Celine Marquet, Regina Barzilay, and Tommi S. Jaakkola.
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models.
Machine Learning for Drug Discovery (ICLR workshop), 2023.
[link]
- Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi Jaakkola.
EigenFold: Generative Protein Structure Prediction with Diffusion Models.
Machine Learning for Drug Discovery Workshop (ICLR workshop), 2023.
[link]
- Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola.
Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem.
The 11th International Conference on Learning Representations (ICLR), 2023.
[link]
- Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.
The 11th International Conference on Learning Representations (ICLR), 2023.
[link]
- Yilun Xu, Shangyuan Tong, and Tommi Jaakkola.
Stable Target Field for Reduced Variance Score Estimation.
The 11th International Conference on Learning Representations (ICLR), 2023.
[link]
- Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal.
Is Conditional Generative Modeling all you need for Decision Making?.
The 11th International Conference on Learning Representations (ICLR), 2023.
[link]
- Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, and Tommi Jaakkola.
Efficiently Controlling Multiple Risks with Pareto Testing.
The 11th International Conference on Learning Representations (ICLR), 2023.
[link]
- Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, and Pradeep Ravikumar.
Fundamental Limits and Tradeoffs in Invariant Representation Learning.
Journal of Machine Learning Research, 23(340), pp. 1–49. 2022.
[link]
- Adam Fisch, Tommi Jaakkola, and Regina Barzilay.
Calibrated Selective Classification.
Transactions on Machine Learning Research, 2022.
[link]
- Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola.
Torsional Diffusion for Molecular Conformer Generation.
Neural Information Processing Systems (NeurIPS), 2022.
[link]
- Yilun Xu, Ziming Liu, Max Tegmark, and Tommi Jaakkola.
Poisson Flow Generative Models.
Neural Information Processing Systems (NeurIPS), 2022.
[link]
- 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]
- Bowen Jing, Gabriele Corso, Renato Berlinghieri, and Tommi Jaakkola.
Subspace Diffusion Generative Models.
European Conference on Computer Vision (ECCV), 2022.
[link]
- Hannes Stark, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola.
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction.
International Conference on Machine Learning (ICML), 2022.
[link]
- Wengong Jin, Regina Barzilay, and Tommi Jaakkola.
Antibody-Antigen Interface Design via Hierarchical Structure Refinement.
International Conference on Machine Learning (ICML), 2022.
[link]
- Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay.
Conformal Prediction Sets with Limited False Positives.
International Conference on Machine Learning (ICML), 2022.
[link]
- Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen.
Generative models for molecular discovery: Recent advances and challenges.
WIREs Computational Molecular Science, 2022.
[link]
- Tian Xie, Xiang Fu, Octavian Ganea, Regina Barzilay, and Tommi Jaakkola.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
- Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi Jaakkola.
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
- Yilun Xu, Hao He, Tianxiao Shen, and Tommi Jaakkola.
Controlling Directions Orthogonal to a Classifier.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
- Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, and Tommi Jaakkola.
Adversarial support alignment.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
- Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause.
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
