About me

I am a PhD student at Mila and the Université de Montréal supervised by Yoshua Bengio. I am currently visiting Frances Arnold’s lab at Caltech until spring 2026. My research interests center on the design of computationally efficient generative modeling, deep learning, solving the sampling problem, and the application of generative models for the de novo design of any molecular structure. I have worked extensively on protein generative models and previously co-founded the company Dreamfold where we worked on said problem. Before joining Mila I grew up in small town Michigan and graduated from the University of Michigan in 2017 with a BS in computer science.

Publications

Note that publications are likely out of date – please refer to Google Scholar for an updated list.

• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
  Tara Akhound-Sadegh*, Jarrid Rector-Brooks*, Avishek Joey Bose*, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong
  Arxiv (2024) link

• On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
  Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin
  Arxiv (2024) link

• 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) link

• 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)

• 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 SPIGM Workshop (2023) link

• Molecular fragment-based diffusion model for drug discovery
  Daniel Levy, Jarrid Rector-Brooks
  ICLR MLDD Workshop (2023) link

• Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport
  Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, Yoshua Bengio
  TMLR (2023) link

• Multi-Objective GFlowNets
  Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio
  ICML (2023) link

• 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 (2023) link

• Biological Sequence Design with GFlowNets
  Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, 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) link

• 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) link

• DEUP: Direct Epistemic Uncertainty Prediction
  Moksh Jain, Salem Lahlou, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio
  TMLR (2023) link

• Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
  Jarrid Rector-Brooks, Jun-Kun Wang, Barzan Mozafari
  AAAI (2019) link