FEED20: Fragile Earth: Data Science for a Sustainable Planet San Diego Convention Center San Diego, CA, United States, August 24, 2020 |
Conference website | https://ai4good.org/fragile-earth |
Submission link | https://easychair.org/conferences/?conf=feed20 |
Submission deadline | May 10, 2020 |
Fragile Earth: Data Science for a Sustainable Planet, is a workshop taking place as part of the ACM's KDD 2020 Conference on research in Machine Learning and its applications. The workshop will be part of the "Earth Day" events. For more information on the KDD conference, see the conference website.
The United Nations Sustainable Development Goals (SDGs) identify “Zero Hunger” (FoodSecurity), “Clean Water and Sanitation” (Water Security) , “Affordable and Clean Energy” (Sustainable Energy), “Life on Land” (Restoring Land & Ecosystems), “Sustainable Cities & Communities” (Sustainable Cities) and “Climate Action” (Climate Change) as part of a shared blueprint for peace and prosperity for people and the planet for now into the future. The seventeen SDGs form the basis of this strategy. Altogether, ensuring security and sustainability of critical resources, namely food, energy and water, form the backbone for meeting the bulk of the global goals for 2030. The proposed workshop will bring together the research, industry, and policy communities to discuss, address and help develop the technological foundations for advancing and meeting these important development goals.The technological challenges posed by these development goals are two-fold: how to achieve accurate, robust and scalable modeling on physical, environmental, system and societal data, and how to ensure that the obtained models are socially acceptable for use in the associated policy and decision making support.
A key technological enabler for the former is theory-guided data science and scientific discovery, which by augmenting data driven modeling with domain physics and constraints, realizes both accuracy and flexibility in modeling. For the latter, leveraging the emerging techniques of trustworthy machine learning and artificial intelligence to attain the interpretability, accountability, fairness and privacy required for social adoption would be key, along with explicit consideration and inclusion of the viewpoints of policy makers.
This Workshop targets both a methodological and applied research agenda within these areas of investigation. The methodological agenda of interest includes but are not limited to the integration of physics into data driven environmental modeling, use of advanced machine learning techniques to enhance, replace or speed up physical simulations, addressing interpretability of theory guided and data driven models, incorporation of physics into the causal explanation of models, privacy aware schemes for data sharing in agriculture and food systems, addressing fairness in data sharing for sustainability.
The applications and agenda of interest include food security, sustainable agricultural practices, crop yield forecasting and improvement, restoring degraded landscapes to productive landscapes, clean water management, sustainable and clean energy production, energy efficient and low waste food supply chain, and the future of intelligent technologies in tackling these topics in an ever urbanizing world. We welcome all data scientific agendas, both methodological and application oriented, relevant to the goals of sustainable development.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. All submissions must follow the standard ACM Proceedings template, which can be found here: ACM Template. The following paper categories are welcome:
- Full papers (8-10 pages in length)
- Extended Abstracts (1-2 pages in length)
- Vision Papers / Position Papers (up to 5 pages in length)
- Posters
List of Topics
- Paradigms for enhancing scientific discovery through theory guided data science.
- Empirical investigations at the intersection of the earth sciences/sustainability and data.
- Data-informed Food/Energy/Water/Earth Sciences policy discussions.
- Frameworks for helping the scientific and KDD communities to work together.
- Any other related topics to the themes of the workshop are welcome!
Organizers
Organizing Committee
- Naoki Abe (IBM)
- Kathleen Buckingham (World Resources Institute)
- Auroop Ganguly (Northeastern University)
- James Hodson (AI for Good Foundation)
- Ramakrishnan Kannan (Oak Ridge National Laboratory)
Program Committee
- Chid Apte (IBM)
- Rayid Ghani (Carnegie Mellon University)
- Marko Grobelnik (Jozef Stefan Institute)
- Estevam Hruschka (Megagon Labs)
- Anuj Karpatne (Virginia Tech)
- Vipin Kumar (University of Minnesota)
- Thomas Potok (Oak Ridge National Laboratory)
- Shashi Shekhar (University of Minnesota)
- Mitchell Tuinstra (Purdue University)
- Raju Vatsavai (North Carolina State University)
Venue
San Diego Convention Center, San Diego, CA, USA
Contact
All questions about submissions should be emailed to hodson@ai4good.org