Mila > Team > David Rolnick

David Rolnick

Core Academic Member
Assistant Professor, McGill University, Canada CIFAR AI Chair

I am an Assistant Professor in the School of Computer Science at McGill University, Adjunct Professor, Department of Computer Science and Operations Research (DIRO) at UdeM and a Core Academic Member at Mila. I also serve as co-founder and chair of Climate Change AI. My research foci are:

  • Deep learning theory: Mathematical understanding of the properties of neural networks.
  • Machine learning and climate change: Applications of machine learning to mitigate and adapt to the climate crisis.

Previously, I was an NSF Mathematical Sciences Postdoctoral Research Fellow at the University of Pennsylvania, working with Konrad Körding. I received my PhD in Applied Math from MIT in 2018, co-advised by Nir Shavit, Max Tegmark, and Ed Boyden. Before that, I was a Fulbright Scholar at the Freie Universität Berlin and an undergraduate at MIT.



Techniques for Symbol Grounding with SATNet
Sever Topan, David Rolnick and Xujie Si


Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks.
Christopher Hillar, Tenzin Chan, Rachel Taubman and David Rolnick


ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker and David Rolnick


Digitizing a sustainable future
Lucia A. Reisch, Lucas Joppa, Peter Howson, Artur Gil, Panayiota Alevizou, Nina Michaelidou, Ruby Appiah-Campbell, Tilman Santarius, Susanne Köhler, Massimo Pizzol, Pia Johanna Schweizer, Dipti Srinivasan, Lynn H. Kaack, Priya L. Donti and David Rolnick
One Earth


DC3: A learning method for optimization with hard constraints
Priya L. Donti, David Rolnick and J Zico Kolter


Deep ReLU Networks Preserve Expected Length.
Boris Hanin, Ryan Jeong and David Rolnick
arXiv preprint arXiv:2102.10492


Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification
Charles A. Kantor, Marta Skreta, Brice Rauby, Léonard Boussioux, Emmanuel Jehanno, Alexandra Luccioni, David Rolnick and Hugues Talbot


Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification
Marta Skreta, Sasha Luccioni and David Rolnick
NeurIPS 2020 Workshop on Tackling Climate Change with Machine Learning


Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Körding, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig... (2 more)
arXiv preprint arXiv:1906.05433

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