Since 2016, the Fragile Earth Workshop has brought together the research community to find and explore how data science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).
Fragile Earth 2022: AI for Climate Mitigation, Adaptation, and Environmental Justice is a workshop taking place as part of the ACM's KDD 2022 Conference on research in knowledge discovery and data mining and their applications. The dates for the Conference are August 14-18, 2022
The workshop will be a full day event on August 15, 2022.
ANNOUNCEMENT: BEST PAPER
GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
Fragile Earth 2022
Dates: August 15, 2022
Venue: Washington DC Convention Center
8:00-9:30 Session 1: Mitigation and Adaptation
8:10-9:30: Paper session 1 (20 minutes x 4 papers)
Paper 1: Urban Forests for Carbon Sequestration and Heat Island Mitigation
Authors: Levente Klein and Conrad Albrecht
Paper 2: AI-Enabled Decarbonization Framework
Authors: Ayush Jain, Manikandan Padmanaban, Jagabondhu Hazra, Ranjini Guruprasad and Heriansyah Syam
Paper 3: *BEST PAPER RUNNER UP* CIMF – Climate Impact Modelling Framework
Authors: Blair Edwards, Paolo Fraccaro, Nikola Stoyanov, Nelson Bore, Julian Kuehnert, Kommy Weldemariam and Anne Jones
Paper 4: A Multi-Perspective Content Analysis Platform for Sustainability Assessment
Authors: Lipika Dey, Tirthankar Dasgupta, Abir Naskar, Tushar Goel, Ishan Verma, Vipul Chauhan, Uma M N and Rajkumar Pallikuth
9:30-10:00: Coffee break
10:00-12:00 Session 2: Disaster Prediction, Impact Modeling
10:00-10:45: Keynote 1 – Dr. Zhe Jiang: Embracing the New Opportunities and Challenges of AI in Geo-domains: An Illustrative Example in Observation-based Flood Inundation Mapping
Prof. Zhe Jiang is an assistant professor in the Department of Computer & Information Science & Engineering at the University of Florida, where he is also affiliated with the Center for Coastal Solutions. He received his Ph.D. in Computer Science from the University of Minnesota and a B.E. in Electrical Engineering from the University of Science and Technology of China. His research interests include data mining, machine learning, and artificial intelligence, with a particular focus on spatiotemporal data mining for interdisciplinary applications in hydrology, disaster management, monitoring coastal hazards, etc. His research has been sponsored by multiple federal agencies (e.g., NSF, USGS, NOAA, UCAR) and industry companies. He is a senior member of IEEE.
10:50-11:50: Paper session 2 (20 minutes x 3 papers)
Paper 1: Robustness of Urban Coastal Rail Network Under Projected Future Floods
Authors: Ashis Kumar Pal, Puja Das, Nishant Yadav and Auroop R. Ganguly
Paper 2: *BEST PAPER* GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
Authors: Rohit Saha, Azin Asgarian, Mengyi Fang, Angeline Yasodhara, Kyryl Truskovskyi, Daniel Homola, Raahil Shah, Frederik Dieleman, Jack Weatheritt and Thomas Rogers
Paper 3: Evaluation of Surface Runoff Projections from Earth System Models in Major River Basins of the World
Authors: Puja Das and Auroop R. Ganguly1
1:00-3:00 Session 3: Topics in Climate Modeling
1:00-1:45: Keynote 2 – Keynote from Forrest Hoffman, Oak Ridge National Laboratory.
- Forrest M. Hoffman is a Distinguished Computational Earth System Scientist and the Group Leader for the Computational Earth Sciences Group at Oak Ridge National Laboratory (ORNL), where he develops and applies Earth system models (ESMs) to investigate the global carbon cycle and feedbacks between biogeochemical cycles and the climate system. Forrest also applies data mining and machine learning methods using high performance computing to problems in Earth system modeling, landscape ecology, ecohydrology, remote sensing, and large-scale climate data analytics. Forrest is a Fellow of the American Association for the Advancement of Science (AAAS).
1:50-2:50: Paper session 3 (20 minutes x 3 papers)
Paper 1: Multi-Fidelity Hierarchical Neural Processes for Climate Modeling
Authors: Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma and Rose Yu
Paper 2: Estimation of Nearshore Water Depth Using Physics-Informed Deep Learning
Authors: Nan Wang and Qin Chen
Paper 3: Feature Scaling and Attention Convolutions for Extreme Precipitation Prediction
Authors: Weichen Huang
3:00-3:30: Coffee break
3:30-5:00 Session 4
3:30-4:10: Keynote 3 – Dr. Rose Yu: Accelerating Climate Model Simulation with Physics-Guided Deep Learning
- Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the ’MIT Rising Stars in EECS’.
4:10-5:00: Panel- Climate Adaptation, Mitigation & Justice Discussion
- A lively discussion regarding academic and industry research to support solutions toward climate adaptation, mitigation and justice.
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