David Rolnick studies machine learning innovations for tackling climate change, as well as the mathematical foundations of deep learning algorithms. His work bridges the gap between machine learning research and fields such as energy and ecology via high-impact applications in climate change mitigation and adaptation. This work has significantly influenced international policy and entrepreneurship. Rolnick’s work on the foundations of deep learning includes analysis of when and why different algorithms succeed or fail, as well as the development of new algorithms leveraging insights from mathematics and computational neuroscience. This work aims to build the groundwork for next-generation deep learning research by replacing trial and error progress with algorithms designed from first principles.
- Scientific Co-Director, Sustainability in the Digital Age, 2020
- Co-Founder and Chair, Climate Change AI, 2019
- US NSF Mathematical Sciences Postdoctoral Research Fellow, 2018
- US NSF Graduate Research Fellow, 2013
- Fulbright Scholar, 2012
- Rolnick, D., & Kording, K. (2020, November). Reverse-engineering deep ReLU networks. In International Conference on Machine Learning (pp. 8178-8187). PMLR.
- Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2019). Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433.
- Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., & Wayne, G. (2019). Experience replay for continual learning. In Advances in Neural Information Processing Systems (pp. 350-360).
- Hanin, B., & Rolnick, D. (2019). Complexity of linear regions in deep networks. arXiv preprint arXiv:1901.09021.
- Rolnick, D., & Tegmark, M. (2017). The power of deeper networks for expressing natural functions. arXiv preprint arXiv:1705.05502.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.