This conference brings together students, experts, and leaders across areas with the goal of advancing how machine learning methods can address key scientific goals related to molecular modeling, molecular interactions, and therapeutic design. The conference provides an open and lively place to discuss, learn, and innovate, for students and experts alike.
Areas of Focus
ML for Quantum Chemistry
Molecular Dynamics Simulations
Modeling Molecular Interactions
Geometric ML for Molecules
Molecule/Protein 3D Structure Prediction and Processing
Scientific Director, Mila & IVADO, Full Professor, Samsung AI Professor, Université de Montréal, Canada CIFAR AI Chair
Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO. In 2018, he collected the largest number of new citations in the world for a computer scientist and in 2019 was awarded the prestigious Killam Prize. Since 2022 he has the largest h-index impact factor in computer science, worldwide. He is a Fellow of both the Royal Society of London and Canada and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.
School of Engineering Distinguished Professor for AI and Health; AI Faculty Lead, Jameel Clinic; MacArthur Fellow
Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. She is also a member of the National Academy of Engineering and American Academy of Artrs and Sciences. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. Her research interests are in machine learning models for molecular modeling with applications to drug discovery and clinical AI. She also works in natural language processing. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2020, she was awarded the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. She received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University. Prof. Barzilay received her undergraduate degree from Ben-Gurion University of the Negev, Israel.
Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology, 1992, and Ph.D. from MIT in computational neuroscience, 1997. Following a postdoctoral position in computational molecular biology (DOE/Sloan fellow, UCSC) he joined the MIT EECS faculty 1998. His research interests include many aspects of machine learning, statistical inference and estimation, and analysis and development of algorithms for various modern estimation problems such as those involving predominantly incomplete data sources. His applied research focuses on problems in natural language processing, computational chemistry, as well as computational functional genomics.
Associate Professor, HEC Montréal, Canada CIFAR AI Chair
Jian Tang is currently an associate professor at Mila-Quebec AI Institute and also at Computer Science Department and Business School of University of Montreal. He is a Canada CIFAR AI Research Chair. His main research interests are graph representation learning, graph neural networks, geometric deep learning, deep generative models, knowledge graphs and drug discovery. During his PhD, he was awarded with the best paper in ICML2014; in 2016, he was nominated for the best paper award in the top data mining conference World Wide Web (WWW); in 2020, he is awarded with Amazon and Tencent Faculty Research Award. He is one of the most representative researchers in the growing field of graph representation learning and has published a set of representative works in this field such as LINE and RotatE. His work LINE on node representation learning has been widely recognized and is the most cited paper at the WWW conference between 2015 and 2019. Recently, his group just released an open-source machine learning package, called TorchDrug, aiming at making AI drug discovery software and libraries freely available.
Adjunct Professor at Université de Montréal. Associate Industry Member at Mila
Dominique Beaini is currently an Adjunct Professor at the University of Montreal in the department of informatics and operational research. He is also an Associate Industry member at Mila and Research Unit Lead at Valence Discovery. He is focused on pushing the state of machine learning towards a better understanding of molecules and their interactions with human biology. He holds a Ph.D. from Polytechnique Montreal for my previous research in robotics and computer vision. His research interests include: graph neural networks, self-supervised learning, quantum mechanics, drug discovery, computer vision, and robotics.
Montréal, 6650 Rue Saint-Urbain, Montréal, QC H2S 3H1, Canada
Located in the heart of Quebec's Artificial Intelligence ecosystem, Mila is a community of more than 1,000 researchers specializing in machine learning and dedicated to scientific excellence and innovation. Mila boasts the largest concentration of deep learning academic researchers globally.
Mila is a growing hub for Biotech innovation and talent. With over 100 industry partners, Mila has created a dense ecosystem comprised of many biotechs ranging from small startups to billion-dollar pharmaceutical companies.