One of the original inventors of Generative Flow Networks.

Lead 20+ person drug discovery team at Mila.

Ph.D. with Yoshua Bengio.

‍

CSO and Co-Founder

Chenghao works on the intersection of generative algorithms and molecular design. Prior to Dreamfold, Chenghao has designed and tested hundreds of molecules/polymers in the wet lab, including de novo designs generated from his own algorithms.

He did his Ph.D. at McGill University and Mila with Dima Perepichka and Yoshua Bengio. He has been awarded the prestigious Vanier scholarship.

DeepMind Professor of AI at Oxford, Former Head of Graph ML Research at Twitter, founder of multiple successful startups, author of major contributions to the field of geometric deep learning for protein design. Michael is an avid opera fan.

Joey is a Machine Learning scientist at DreamFold and a Post-Doctoral Fellow at the University of Oxford working with Michael Bronstein.

He completed his PhD at McGill/Mila under the supervision of Will Hamilton, Gauthier Gidel, and Prakash Panagaden. His research interests span Generative Modelling, and Differential Geometry for Machine Learning with a current emphasis on understanding symmetries, equivariances and invariances in data. Previously, he completed his Bachelors and Master’s degrees from the University of Toronto working on adversarial attacks against face detection and is the President and CEO of FaceShield Inc. an educational platform for digital privacy for facial data.

His work has been featured in Forbes, CBC, VentureBeat and other media outlets and is generously supported by the IVADO PhD Fellowship.

‍

Guillaume develops mathematical foundations for manifold learning (including generative models). His recent publications concern cutting-edge generative algorithms such as Flow Matching and Schrödinger Bridges.

Guillaume is a fourth-year PhD candidate at Mila, specializing in generative AI. He focuses his research on manifold learning, optimal transport, and dynamic models. Actively contributing to computational biology projects, particularly in the realms of protein design and single-cell analysis. Prior to his PhD, he worked on piecewise deterministic Markov processes for Monte Carlo methods.

Tara is a PhD student at Mila/McGill University. She is interested in Geometric Deep Learning (symmetries and equivariant models) and Generative Modelling, particularly with applications in physics and biology.

She holds a Bachelor’s degree in Engineering Physics from the University of British Columbia and also completed an M1 in Mathematics at Diderot University.

Before DreamFold, James worked with Microsoft for nearly 5 years, most recently as an Applied Scientist in the Turing team working on LLMs and web-scale chatbots. Prior to Microsoft, James was a founding Quantitative Analyst for a Toronto-based hedge fund.

James obtained a Bachelors of Engineering in Mathematics and Engineering from Queen's University

Eric is an applied research scientist with a decade of experience in implementing machine learning to solve real-world problems. He started his AI journey as one of Yoshua Bengio’s master's students at the very start of the deep learning era. Throughout his career, Eric implemented various NLP, recommender systems, forecasting and matchmaking solutions which gave him broad machine learning and product development experience. A scientist and artist at heart he is also an avid pianist.

Kilian is a machine learning research scientist. His research focuses on generative models for protein backbone generation. Before joining DreamFold, he was a postdoctoral fellow at Mila and McGill University where he worked on distribution shifts, generative modelling and optimal transport.

Kilian holds a PhD from IRISA-INRIA (France). His research focused on the interaction of optimal transport and deep learning, especially on the use of minibatch optimal transport in deep learning applications.