Many in the ML community wish to take action on climate change, yet feel their skills are inapplicable. This workshop aims to show that in fact the opposite is true: while no silver bullet, ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from designing smart electrical grids to tracking deforestation in satellite imagery. Many of these actions represent high-impact opportunities for real-world change, as well as being interesting problems for ML research.
Jeff Dean (Google AI)
Carla Gomes (Cornell)
Felix Creutzig (MCC Berlin, TU Berlin)
Lester Mackey (Microsoft Research, Stanford)
8:15 - 8:30 - Welcome and opening remarks
8:30 - 9:05 - Jeff Dean (Google AI) (Invited talk)
9:05 - 9:15 - Felipe Oviedo: Machine learning identifies the most valuable synthesis conditions for next-generation photovoltaics (Spotlight talk)
9:15 - 9:25 - Valentina Zantedeschi: Cumulo: A Dataset for Learning Cloud Classes (Spotlight talk)
9:25 - 9:35 - Qinghu Tang: Fine-Grained Distribution Grid Mapping Using Street View Imagery (Spotlight talk)
9:35 - 9:45 - Shamindra Shrotriya and Niccolo Dalmasso: Predictive Inference of a Wildfire Risk Pipeline in the United States (Spotlight talk)
9:45 - 10:30 - Coffee break + Poster Session
10:30 - 11:05 - Felix Creutzig (MCC Berlin, TU Berlin) (Invited talk)
11:05 - 11:15 - Ashish Kapoor: Helping Reduce Environmental Impact of Aviation with Machine Learning (Spotlight talk)
11:15 - 12:00 - Yoshua Bengio, Andrew Ng, Carla Gomes, Lester Mackey, Jeff Dean: Panel: Climate change and AI
12:00 - 2:00 - Networking lunch (provided) + Poster Session
2:00 - 2:40 - Carla Gomes (Cornell) (Invited talk)
2:40 - 2:50 - Kyle Story: A Global Census of Solar Facilities Using Deep Learning and Remote Sensing (Spotlight talk)
2:50 - 3:00 - Kiwan Maeng: Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon (Spotlight talk)
3:00 - 3:10 - Daisy Zhe Wang: Measuring Impact of Climate Change on Tree Species: analysis of JSDM on FIA data (Spotlight talk)
3:10 - 3:20 - Adrian Albert: Emulating Numeric Hydroclimate Models with Physics-Informed cGANs (Spotlight talk)
3:20 - 3:30 - Jan Drgona: Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning (Spotlight talk)
3:30 - 4:15 - Coffee break + Poster Session
4:15 - 4:40 - Lester Mackey (Microsoft Research) (Invited talk)
4:40 - 4:50 - Saumya Sinha: Detecting Avalanche Deposits using Variational Autoencoder on Sentinel-1 Satellite Imagery (Spotlight talk)
4:50 - 5:00 - Jacob Pettit: Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning (Spotlight talk)
5:00 - 6:00 - John Platt, Jennifer Chayes, Felix Creutzig, Craig Miller, Marta Gonzalez, Matt Horne: Panel: Working with stakeholders
David Rolnick (UPenn)
Priya Donti (CMU)
Lynn Kaack (ETH Zürich)
Alexandre Lacoste (Element AI)
Tegan Maharaj (Mila)
Andrew Ng (Stanford)
John Platt (Google AI)
Jennifer Chayes (Microsoft Research)
Yoshua Bengio (Mila)
NeurIPS (formerly written “NIPS”) is one of the premier conferences on machine learning, and includes a wide audience of researchers and practitioners in academia, industry, and related fields. It is possible to attend the workshop without either presenting at or attending the main NeurIPS conference.
We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics:
All machine learning techniques are welcome, from kernel methods to deep learning. Each submission should make clear why the application has (or could have) positive impacts regarding climate change. We highly encourage submissions which make their data publicly available. Accepted submissions will be invited to give poster presentations, of which some will be selected for spotlight talks.
The workshop does not record proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication. Previously published work may be submitted under certain circumstances (see the FAQ).
All submissions must be through the submission website. Submissions will be reviewed double-blind; do your best to anonymize your submission, and do not include identifying information for authors in the PDF. We encourage, but do not require, use of the NeurIPS style template (please do not use the “Accepted” format as it will deanonymize your submission).
We will be awarding $30K in cloud computing credits, sponsored by Microsoft AI for Earth, as prizes for top submissions. Winners will be announced at the workshop.
Please see the Tips for Submissions and FAQ, and contact firstname.lastname@example.org with questions.
There are two tracks for submissions. Submissions are limited to 3 pages for the Papers track, and 2 pages for the Proposals track, in PDF format (see examples here). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.
Work that is in progress, published, and/or deployed
Submissions for the Papers track should describe projects relevant to climate change that involve machine learning. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc.; and climate-relevant datasets.
Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Algorithms need not be novel from a machine learning perspective if they are applied in a novel setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.
Submissions creating novel datasets are welcomed. Datasets should be designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation). In this case, baseline experimental results on the dataset are preferred, but not required.
Detailed descriptions of ideas for future work
Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. No results need to be demonstrated, but ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, and explanation of the proposed method.
We are excited to announce limited travel grants, sponsored by Microsoft Research. Travel grant applications can be submitted at https://forms.gle/Aq8EcV2VLD13LUov5, and are due October 3.
We also encourage workshop participants to apply for NeurIPS 2019 travel grants and other grants (e.g. Google Conference and Travel Scholarships) for which they may be eligible. If you are aware of additional scholarships that may be relevant to workshop attendees, please contact the workshop organizers so we can make this information available.
Q: How can I keep up to date on this kind of stuff?
A: Sign up for our mailing list! https://www.climatechange.ai/mailing_list.html
Q: I’m not in machine learning. Can I still submit?
A: Yes, absolutely! We welcome submissions from many fields. Do bear in mind, however, that the majority of attendees of the workshop will have a machine learning background; therefore, other fields should be introduced sufficiently to provide context for the work.
Q: What if my submission is accepted but I can’t attend the workshop?
A: You may ask someone else to present your work in your stead.
Q: Do I need to use LaTeX or the NeurIPS style files?
A: No, although we encourage it.
Q: It’s hard for me to fit my submission on 2 or 3 pages. What should I do?
A: Feel free to include appendices with additional material (these should be part of the same PDF file as the main submission). Do not, however, put essential material in an appendix, as it will be read at the discretion of the reviewers.
Q: What do I do if I need an earlier decision for visa reasons?
A: Contact us at email@example.com and explain your situation and the date by which you require a decision and we will do our best to be accommodating.
Q: Can I send submissions directly by email?
A: No, please use the CMT website to make submissions.
Q: The submission website is asking for my name. Is this a problem for anonymization?
A: You should fill out your name and other info when asked on the submission website; CMT will keep your submission anonymous to reviewers.
Q: Do submissions for the Proposals track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.
Q: The submission website never sent me a confirmation email. Is this a problem?
A: No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted. If in any doubt regarding the submission process, please contact the organizers. Also please avoid making multiple submissions of the same article to CMT.
Q: Can I submit previously published work to this workshop?
A: If it was previously published in a non-ML venue, YES! If it was previously published in an ML venue, NO! If you are unsure, contact firstname.lastname@example.org. This policy is as per the NeurIPS workshop guidelines: “Workshops are not a venue for work that has been previously published in other conferences on machine learning or related fields. Work that is presented at the main NeurIPS conference should not appear in a workshop, including as part of an invited talk… (Presenting work that has been published in other fields is, however, encouraged!)”
Q: Can I submit work to this workshop if I am also submitting to another NeurIPS 2019 workshop?
A: Yes. We cannot, however, guarantee that you will not be expected to present the material at a time that conflicts with the other workshop.
Andrew Ross (Harvard)
Aneesh Rangnekar (RIT)
Ashesh Chattopadhyay (Rice)
Ashley Pilipiszyn (Stanford)
Bolong Cheng (SigOpt)
Christian Schroeder (Oxford)
Clement Duhart (MIT)
Dali Wang (Oak Ridge National Lab)
David Dao (ETH)
Di Wu (McGill)
Dimitrios Giannakis (Courant Institute, NYU)
Duncan Watson-Parris (Oxford)
Evan Sherwin (Stanford)
Femke van Geffen (FU Berlin)
Gege Wen (Stanford)
George Chen (CMU)
Greg Schivley (Carbon Impact Consulting)
Han Zou (UC Berkeley)
Hari Prasanna Das (UC Berkeley)
Hillary Scannell (University of Washington)
Joanna Slawinska (University of Wisconsin-Milwaukee)
Johan Mathe (Frog Labs)
Jonathan Binas (Mila, Montreal)
Jussi Gillberg (Aalto University)
Kalai Ramea (PARC)
Karthik Kashinath (Lawrence Berkeley National Lab)
Kate Duffy (Northeastern)
Kelly Kochanski (CU Boulder)
Kevin McCloskey (Google)
Kris Sankaran (Mila)
Lea Boche (EPRI)
Loubna Benabbou (Mohammadia School of Engineering, Mohammed V University)
Mahdi Jamei (Invenia Labs)
Max Callaghan (MCC Berlin)
Mayur Mudigonda (UC Berkeley)
Melrose Roderick (CMU)
Mohammad Mahdi Kamani (Penn State)
Natasha Jaques (MIT)
Neel Guha (CMU)
Niccolo Dalmasso (CMU)
Nikola Milojevic-Dupont (MCC Berlin)
Pedram Hassanzadeh (Rice)
Robin Dunn (CMU)
Sajad Haghanifar (University of Pittsburgh)
Sanam Mirzazad (EPRI)
Sandeep Manjanna (McGill)
Sasha Luccioni (Mila)
Sharon Zhou (Stanford)
Shubhankar Deshpande (CMU)
Sookyung Kim (Lawrence Livermore National Lab)
Soukayna Mouatadid (University of Toronto)
Surya Karthik Mukkavili (Mila)
Telmo Felgueira (IST)
Thomas Hornigold (Oxford)
Tianle Yuan (NASA)
Tom Beucler (Columbia & UCI)
Vikram Voleti (Mila, Montreal)
Volodymyr Kuleshov (Stanford)
Yang Song (Oak Ridge National Lab)
Ydo Wexler (Amperon)
Zhecheng Wang (Stanford)
Zhuangfang Yi (Development Seed)