13 research proposals were awarded a total of $1.8M USD as part of Climate Change AI's inaugural Innovation Grants program.
PITTSBURGH, May 5, 2022 /PRNewswire-PRWeb/ -- Climate Change AI (CCAI) today formally announced the winners of its first Innovation Grants program. The thirteen winning projects were selected through a rigorous peer review process from almost 200 submissions from around the world. A total of $1.8M USD will be awarded for impactful, AI-driven research that addresses climate change mitigation and adaptation. The winning proposals span a broad range of application areas, from energy to conservation, with investigators in six continents.
The first edition of the funding program enjoyed significant interest and featured high-quality proposals, 50 of which were rated outstanding by a multidisciplinary team of expert reviewers. Building on the success of the inaugural program, CCAI is looking to grow the funding pool in the upcoming cycles and expand eligibility beyond OECD countries.
CCAI thanks Schmidt Futures and Quadrature Climate Foundation for their financial sponsorship and the Canada Hub of Future Earth for their administrative support.
The funded proposals are listed alphabetically below:
- Accelerating Material Discovery for High-Performance Chemical Separation using AI (University of Massachusetts, Amherst)
- Adaptive Learning Techniques for Improved Subseasonal Forecasting (University of Toronto; AER; Microsoft Research; MIT; IMPA)
- Detecting Flooding in Fiji's Croplands (University of Western Australia; Ministry of Agriculture, Fijian Government; University of the South Pacific; University of Sydney)
- Estimate the ice volume of all glaciers in High Mountain Asia with deep learning (ICENET) (Institute of Polar Sciences, National Research Council; University of California Irvine)
- Extracting and Discovering New Measurements from Climate Text Sources (University of California San Diego; Scripps Institution of Oceanography)
- ForestBench: Equitable Benchmarks for Monitoring, Verification and Reporting of Nature-Based Solutions with Machine Learning (Carnegie Institution for Science; ETH Zurich; MIT; Stanford University; Climate Reality Project; Restor)
- Improving Resiliency of Malian Farmers with Yield Estimation: IMPRESSYIELD (Istanbul Technical University; METU; Agcurate; OKO Finance)
- Learning Power System Dynamics in the Frequency Domain (University of Washington; Microsoft Research; Breakthrough Energy)
- Machine Learning-based Dynamic Climate Projections for Power System Planning Datasets (University of Colorado Boulder; University of California, San Diego; IIT Roorkee)
- Matching Structured Energy System Data for Policy Making and Advocacy using Weakly Supervised Machine Learning (Georgia Tech; Catalyst Cooperative)
- Mitigating Climate Change Impacts on Biodiversity via Machine Learning Powered Assessment (University of Edinburgh; iNaturalist; IUCN)
- Towards greener last-mile operations: Supporting cargo-bike logistics through optimized routing of multi-modal urban delivery fleets (IT University of Copenhagen; MIT–IBM Watson AI Lab; University of Edinburgh; Kale Collective)
- Using Machine Learning and Earth Observation Data to Identify Aquaculture Sites with High Potential for Production Intensification and Mangrove Restoration in Southeast Asia (Arizona State University; Conservation International; Thinking Machines Data Science)
About the organization:
Climate Change AI (CCAI) is a global non-profit that catalyzes impactful work at the intersection of climate change and artificial intelligence. Since it was founded in June 2019, CCAI has been instrumental in establishing climate change as a key area of impact for machine learning, and has inspired, informed, and connected thousands of researchers, engineers, entrepreneurs, investors, and policymakers around the world.
Media Contact
Jesse Dunietz, Climate Change AI, 1 7324700563, [email protected]
SOURCE Climate Change AI
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