| Drawdown: The most comprehensive plan ever proposed to reverse global warming P Hawken Penguin, 2017 | 397 | 2017 |
| Tackling Climate Change with Machine Learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433, 2019 | 115 | 2019 |
| Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, and Yoshua Bengio. Tackling Climate Change with Machine Learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433 [cs, stat], 2019 | 24* | 2019 |
| Assessment of atmospheric aerosols from two reanalysis products over Australia SK Mukkavilli, AA Prasad, RA Taylor, J Huang, RM Mitchell, A Troccoli, ... Atmospheric research 215, 149-164, 2019 | 17 | 2019 |
| Visualizing the consequences of climate change using cycle-consistent adversarial networks V Schmidt, A Luccioni, SK Mukkavilli, N Balasooriya, K Sankaran, ... International Conference on Learning Representations (ICLR), AI for Social …, 2019 | 15 | 2019 |
| Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event SK Mukkavilli, AA Prasad, RA Taylor, A Troccoli, MJ Kay Journal of Applied Meteorology and Climatology 57 (3), 493-515, 2018 | 13 | 2018 |
| EnviroNet: ImageNet for Environment SK Mukkavilli, P Tissot, A Ganguly, L Joppa, D Meger, G Dudek 18th Conference on Artificial and Computational Intelligence and its …, 2019 | 2 | 2019 |
| Strategic Foresight to Applications of Artificial Intelligence to Achieve Water-related Sustainable Development Goals H Mehmood, SK Mukkavilli, I Weber, A Koshio, C Meechaiya, T Piman, ... United Nations University, Institute for Water, Environment and Health, 2020 | 1 | 2020 |
| Predicting ice flow using machine learning Y Min, SK Mukkavilli, Y Bengio Neural Information Processing Systems (NeurIPS), Tackling Climate Change …, 2019 | 1 | 2019 |
| Climate Change & AI: Present and potential role of AI in assessment and response L Joppa, V Lakshmanan, V Kumar, G Dudek, SK Mukkavilli, P Tissot 99th American Meteorological Society Annual Meeting, 2019 | 1 | 2019 |
| EnviRoNet-Planetary Science Applications SK Mukkavilli, D Meger, G Dudek AGUFM 2018, P43J-3875, 2018 | 1 | 2018 |
| Parameterization of Autoconversion Rates in a Climate Model Using Machine Learning with Training from Large Eddy Simulations KG Pressel, PL Ma, J Fan, JD Fast, WI Gustafson, JC Hardin, C Kaul, ... AGU Fall Meeting 2020, 2020 | | 2020 |
| Towards robust operational neural network parameterizations of convection in climate models—advances in stability, credibility and software MS Pritchard, T Beucler, G Mooers, J Ott, P Gentine, L Peng, P Baldi, ... AGU Fall Meeting 2020, 2020 | | 2020 |
| Generative Large Eddy Simulations with conditional Variational Autoencoders SK Mukkavilli, MS Pritchard, KG Pressel, G Mooers, PL Ma, S Mandt AGU Fall Meeting 2020, 2020 | | 2020 |
| Integrated Climate Extremes: Modeling Future Impacts for Visualizing Climate Change SK Mukkavilli, Y Min, A Madanchi, VB Pacela, S Patel, Y Bengio 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| PMNet: Improving Aerosol Predictions Using Deep Neural Nets for Limited Ground Stations C Hoyne, SK Mukkavilli, D Meger 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| Deep Learning Applications for Environmental Science II SK Mukkavilli 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| AI Applications for Air Quality SK Mukkavilli 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| Environet: A Project Update SK Mukkavilli 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| Deep learning for Aerosol Forecasting C Hoyne, SK Mukkavilli, D Meger Neural Information Processing Systems (NeurIPS), Machine Learning and the …, 2019 | | 2019 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
