David Rolnick

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Core Academic Member
David Rolnick
Assistant Professor, McGill University
David Rolnick
AI for Good
Deep Learning
Machine Learning (ML) Theory
Climate Change
http://www.davidrolnick.com/

I am an Assistant Professor in the School of Computer Science at McGill University 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.

Publications

2021-06

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
(2021-06-18)
www.sciencedirect.com
Techniques for Symbol Grounding with SATNet.
Sever Topan, David Rolnick and Xujie Si
arXiv preprint arXiv:2106.11072
(2021-06-16)
dblp.uni-trier.dePDF

2021-05

DC3: A learning method for optimization with hard constraints
Priya L. Donti, David Rolnick and J Zico Kolter
ICLR 2021
(2021-05-03)
dblp.uni-trier.dePDF[Also on arXiv preprint arXiv:2104.12225 (2021-04-25)]

2021-03

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
arXiv preprint arXiv:2103.11285
(2021-03-21)
dblp.uni-trier.dePDF

2021-02

Deep ReLU Networks Preserve Expected Length.
Boris Hanin, Ryan Jeong and David Rolnick
arXiv preprint arXiv:2102.10492
(2021-02-21)
ui.adsabs.harvard.eduPDF

2020-12

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
(2020-12-11)
www.climatechange.aiPDF

2019-06

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
(2019-06-10)
ui.adsabs.harvard.eduPDF

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