Jarrid Rector-Brooks PhD Student · Mila · Université de Montréal · Caltech Jarrid Rector-Brooks generative modeling · artificial intelligence I am a PhD student at Mila and the Université de Montréal, supervised by Yoshua Bengio, winner of the 2018 A.M. Turing Award. I’m currently a visiting researcher in the lab of Frances Arnold, winner of the 2018 Nobel Prize in Chemistry, at Caltech. My research is on generative models — efficient algorithms to train and perform inference with them, methods to steer them, and the sampling problems underlying both — across discrete and continuous spaces, oftentimes aimed at designing proteins and other molecular structures. I co-founded Dreamfold during my PhD, and previously studied computer science at the University of Michigan during undergrad. If you’re interested in working together, feel free to reach out by email. Google Scholar GitHub LinkedIn Email Efficient generative models Flow matching, diffusion, and discrete diffusion models designed to be cheaper to train and faster at inference time. Steering & control Guiding pretrained models toward the objectives we care about — controllable, reward-driven generation framed as probabilistic inference (DDPP). Sampling & inference Drawing samples from unnormalized, Boltzmann-like densities with neural samplers and amortized inference (iDEM). Protein & molecular design Multimodal models that co-design protein sequence and structure for the de novo design of proteins, enzymes, and molecules (DISCO, FoldFlow). DISCO General Multimodal Protein Design Enables DNA-Encoding of Chemistry Jarrid Rector-Brooks*†, Théophile Lambert*, Marta Skreta*, Daniel Roth*, Yueming Long, Zi-Qi Li, Xi Zhang, Miruna Cretu, Francesca-Zhoufan Li, Tanvi Ganapathy, Emily Jin, Avishek Joey Bose, Jason Yang, Kirill Neklyudov, Yoshua Bengio, Alexander Tong, Frances H. Arnold†, Cheng-Hao Liu*† arXiv · 2026 *Equal contribution. †Corresponding author arXiv ↗ PAPL Planner-Aware Path Learning in Diffusion Language Model Training Fred Zhangzhi Peng*, Zachary Bezemek*, Jarrid Rector-Brooks, Shuibai Zhang, Anru R. Zhang, Michael Bronstein, Alexander Tong, Avishek Joey Bose ICLR (Oral) · 2026 *Equal contribution arXiv ↗ OXtal OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction Emily Jin*, Andrei Cristian Nica*, Mikhail Galkin, Jarrid Rector-Brooks, Kin Long Kelvin Lee, Santiago Miret, Frances H. Arnold, Michael Bronstein, Avishek Joey Bose, Alexander Tong ICLR · 2026 *Equal contribution arXiv ↗ Disc2Cont From Discrete-Time Policies to Continuous-Time Diffusion Samplers: Asymptotic Equivalences and Faster Training Julius Berner, Lorenz Richter, Marcin Sendera, Jarrid Rector-Brooks, Nikolay Malkin TMLR · 2026 arXiv ↗ INCO Inverse-Confidence Sampling for Continuous Diffusion Language Models Andrei Rekesh, Jarrid Rector-Brooks, Cheng-Hao Liu ICML Workshop · 2026 DDPP Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Cheng-Hao Liu, Sarthak Mittal, Nouha Dziri, Michael Bronstein, Yoshua Bengio, Pranam Chatterjee ICLR · 2025 arXiv ↗ Adversarial Exploration Adaptive Teachers for Amortized Samplers Minsu Kim, Sanghyeok Choi, Taeyoung Yun, Emmanuel Bengio, Leo Feng, Jarrid Rector-Brooks, Sungsoo Ahn, Jinkyoo Park, Nikolay Malkin, Yoshua Bengio ICLR · 2025 arXiv ↗ BIP-RL Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL Luca Scimeca, Siddarth Venkatraman, Moksh Jain, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yashar Hezaveh, Laurence Perreault-Levasseur, Yoshua Bengio, Glen Berseth, Nikolay Malkin ICLR Workshop · 2025 arXiv ↗ P2 Path Planning for Masked Diffusion Model Sampling Fred Zhangzhi Peng*, Zachary Bezemek*, Sawan Patel, Jarrid Rector-Brooks, Sherwood Yao, Avishek Joey Bose, Alexander Tong, Pranam Chatterjee arXiv · 2025 *Equal contribution arXiv ↗ iDEM Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Jarrid Rector-Brooks*, Tara Akhound-Sadegh*, Avishek Joey Bose*, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong ICML · 2024 *Equal contribution arXiv ↗ DiffSampler Improved Off-Policy Training of Diffusion Samplers Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin NeurIPS · 2024 arXiv ↗ FoldFlow-2 Sequence-Augmented SE(3)-Flow Matching for Conditional Protein Backbone Generation Guillaume Huguet*, James Vuckovic*, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, Alexander Tong, Avishek Joey Bose NeurIPS · 2024 *Equal contribution arXiv ↗ RTB Amortizing Intractable Inference in Diffusion Models for Vision, Language, and Control Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Nikolay Malkin NeurIPS · 2024 arXiv ↗ FoldFlow SE(3)-Stochastic Flow Matching for Protein Backbone Generation Avishek Joey Bose*, Tara Akhound-Sadegh*, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong ICLR · 2024 *Equal contribution arXiv ↗ RL Crystals Learning Conditional Policies for Crystal Design Using Offline Reinforcement Learning Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar Digital Discovery · 2024 OpenReview ↗ LambdaZero Generative Active Learning for the Search of Small-Molecule Protein Binders Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, SaiKrishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampášek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin Segler, Michael Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio ICLR Workshop · 2024 arXiv ↗ OT-CFM Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport Alexander Tong*, Kilian Fatras*, Nikolay Malkin*, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio TMLR · 2023 *Equal contribution arXiv ↗ SubTB Learning GFlowNets from Partial Episodes for Improved Convergence and Stability Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin ICML (Oral) · 2023 arXiv ↗ MOGFN Multi-Objective GFlowNets Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio ICML · 2023 arXiv ↗ TS-GFN Thompson Sampling for Improved Exploration in GFlowNets Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, Yoshua Bengio ICML Workshop · 2023 arXiv ↗ FragDiff Molecular Fragment-Based Diffusion Model for Drug Discovery Daniel Levy, Jarrid Rector-Brooks ICLR Workshop · 2023 OpenReview ↗ RECOVER RECOVER: Sequential Model Optimization Platform for Combination Drug Repurposing Identifies Novel Synergistic Compounds in vitro Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martínez-Peña, Eileen L. Tang, Suraj M S, Cristian Regep, Jeremy Hayter, Maksym Korablyov, Nicholas Valiante, Almer van der Sloot, Mike Tyers, Charles Roberts, Michael M. Bronstein, Luke L. Lairson, Jake P. Taylor-King, Yoshua Bengio Cell Reports Methods · 2023 arXiv ↗ DEUP DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Ion Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio TMLR · 2023 arXiv ↗ ProtGFN Biological Sequence Design with GFlowNets Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio ICML · 2022 arXiv ↗ Frank-Wolfe Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari AAAI · 2019 arXiv ↗ 03 — Beyond the lab Other stuff When I’m not building models, you’ll usually find me hiking or biking. I grew up in small-town Michigan and played snare drum in the Michigan Marching Band along the way. Most of all, I’m lucky to share my days with my brilliant partner Alana Valko, our dog Ella, and cat Clementine.