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Fragile Earth 2023

Fragile Earth: AI for Climate Sustainability - from Wildfire Disaster Management to Public Health and Beyond

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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).

The Fragile Earth Workshop was one of three workshops associated with the planned Earth Day event at KDD 2019 (organized by our OC members, Shashi Shekhar and James Hodson), provided keynotes and panels for Earth Day in 2020, and has been a recurring workshop at the annual KDD conference for the past seven years. We hope to continue this tradition in 2023.

The Fragile Earth workshop 2023 is complete. Thank you for participating and see you next year!

Fragile Earth 2023: August 7, 2023

KDD Conference August 6-10th
Long Beach, CA


Paper Notification Date: June 23rd, 2023

Fragile Earth 2023 Wildfires and related disasters are increasing globally, making highly destructive megafires a part of our lives more frequently. This increasing prevalence of devastating megafires has necessitated innovation in wildland fire science and management. A common observation across these large events is that fire behavior is changing, making applied data-driven fire research more important and time critical.

Significant improvements towards modeling wildland fires and the dynamics of fire related environmental hazards and socio-economic impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and artificial intelligence. However, there are many challenges and opportunities in integration of the scientific discoveries and data-driven methods for hazards with the advances in technology and computing in a way that provides and enables different modalities of sensing and computing. The WIFIRE cyberinfrastructure took the first steps to tackle this problem with a goal to create an integrated infrastructure, data and visualization services, and workflows for wildfire mitigation, monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sens- ing and collection to artificial intelligence and modeling efforts using a continuum of computing methods that integrate edge, cloud, and high-performance computing. This talk reviews our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise. This talk will also describe opportunities to address complex societal-scale wildland fire challenges through science and data-driven innovations and cross-sector partnerships. We will discuss how collaboration among researchers, practitioners, policymakers, educators, and citizens can foster knowledge exchange, accelerate the development of new tools, and facilitate the implementation of sustainable practices in fire management. Through real-world examples of collaborative initiatives, we will highlight common obstacles faced in establishing and maintaining partnerships and propose strategies to overcome these challenges to foster long-lasting and effective collaborations. We will conclude by exploring emerging technologies, standards and approaches in wildland fire science, highlighting the importance of continued collaboration and partnership in driving future advancements in fire management.

Fragile Earth 2023Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models. In this talk, I will present ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets. Towards the end of the talk, I will present ClimateLearn, our open-sourced library to standardize machine learning for climate science. Bio: Aditya Grover is an assistant professor of computer science at UCLA. His goal is to develop efficient machine learning approaches that can interact and reason with limited supervision with a focus on deep generative models and their intersection with sequential decision making and causal inference. He is also an affiliate faculty at the UCLA Institute of the Environment and Sustainability, where he grounds his research in real-world applications in climate science. Aditya’s 45+ research works have been published at top venues including Nature, deployed in production at major technology companies, and covered in popular press venues. Amongst other honors, Aditya’s research has notably been recognized with three best paper awards, the ACM SIGKDD doctoral dissertation award, and the AI Researcher of the Year Award by Samsung. Aditya received his postdoctoral training at UC Berkeley, PhD from Stanford, and bachelors from IIT Delhi, all in computer science.
Fragile Earth 2023In an era of increasingly complex natural disasters, Geographic Information Systems (GIS), Machine Learning (ML) and Artificial Intelligence (AI) are combining to revolutionize the way we approach disaster mitigation, prediction, and management. This presentation explores innovative techniques in integrating ML and AI within the wildland fire sector and beyond, creating real-time prediction models, GIS-integrated intelligent systems, and adaptive response mechanisms. The focus will be on demonstrating how these technological advancements have changed how we approach pre-, active-, and post-disaster environments and explore applications on the horizon. Esri’s commitment to cutting-edge integrations serves as a beacon for technology-driven solutions in disaster management, signaling a new era in natural disaster management.  Bio: Anthony Schultz is the Director of Wildland Fire Solutions at Esri. His background is focused on wildland fire management and operations. He has served in a variety of capacities, but most recently as the Fire Management Officer (FMO) for the State of Wyoming. During his tenure as a Fire Management Officer, he chaired the Western State Fire Managers and was a Rocky Mountain Coordinating Group member. He also served as an FMO with the State of North Dakota and as a wildland firefighter for several federal agencies to include the Bureau of Land Management and the National Park Service.

Fragile Earth 2023Janine A. Baijnath-Rodino (UCLA) Dr. Janine Baijnath-Rodino is the Director of Meteorology and Adjunct Assistant Professor in the Department of Atmospheric and Oceanic Sciences at UCLA. Her current research focuses on identifying surface-atmospheric hydrometeorological processes that drive wildland fire behaviour across multiple spatial and temporal scales.

Fragile Earth 2023Thomas Huang (NASA JPL) is a Group Supervisor at NASA JPL’s Instrument Software and Science Data Systems section and the Strategic Lead for Interactive Analytics. Thomas is the NASA Principal Investigator for Earth System Digital Twins and the System Architect for NASA’s Sea Level Change Portal. As an expert in large-scale, distributed intelligent data systems, Thomas led both planetary and Earth information system projects.

Fragile Earth 2023Tom Gulbransen (NSF) is Program Director, Office of Advanced Cyberinfrastructure, Computer and Information Science and Engineering where his concentration is on the nexus of cyberinfrastructure and workforce development. Tom focuses on the Advanced Cyberinfrastructure Coordination Ecosystem of Service & Support program (

Fragile Earth 2023Prof. İlkay Altıntaş (UCSD), a research scientist at the University of California San Diego, is the Chief Data Science Officer of the San Diego Supercomputer Center as well as a Founding Fellow of the Halıcıoğlu Data Science Institute. She is the Founding Director of the Workflows for Data Science (WorDS) Center of Excellence and the WIFIRE Lab.

Fragile Earth 2023

Accepted Papers


Towards Machine Learning-based Fish Stock AssessmentStefan Lüdtke, Maria E. Pierce
Mapping Construction Grade Sand: Stepping Stones Towards Sustainable DevelopmentAndo Shah, Suraj R Nair
Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring
Qingzhi Hu, Daniel Daza, Laurens Swinkels, 
Kristina Ūsaitė, Robbert-Jan ‘t Hoen, Paul Groth
Assessing forest carbon offset additionality with dynamic baselines and uncertainty quantification
Noah Golmant, Martha Morrissey, 
Carlos Edibaldo 
Silva, Felix Dorrek,
Rachel C Engstrand
Scope 3 emission estimation using large language models
Ayush Jain, Manikandan Padmanaban,
Jagabondhu Hazra, Shantanu Godbole,
Kommy Weldemariam
Detecting Aquaculture with Deep Learning in a Low-Data Setting
Laura Greenstreet, Joshua Fan, Felipe Siqueira
Pacheco, Yiwei Bai, Marta Eichemberger Ummus,
Carolina Doria, Nathan Oliveira Barros,
Bruce R Forsberg, Xiangtao Xu, Alexander
Flecker, Carla P Gomes
Neuromorphic Edge Intelligence for Rural Environmental MonitoringAtakan Aral
Understanding the Role of 2019 Amazon Wildfires on Antarctic Ice Sheet Melting Using Data Science Approaches
Sudip Chakraborty, Chhaya Kulkarni, Atefeh
Jabeli, Akila Sampath, Gehan Boteju, Jianwu
Wang, Vandana Janeja
Submission is now closed. Thank you!

Call for Papers


Fragile Earth 2023

Naoki Abe

IBM Research
Naoki is a distinguished research staff member and manager of Foundations of Computational and Statistical Learning group within the Foundations of Trusted AI Department, IBM Research AI. He has been involved in the applications of data analytics specifically to agriculture, currently leading the IBM team in a government funded joint project with Purdue University on integrated genotype phenotype analysis for accelerating breeding of biofuel crops.
» Website
Auroop Ganguly

Auroop Ganguly

Northeastern University and Pacific Northwest National Laboratory
Auroop works in climate extremes, water sustainability and critical infrastructures, by integrating scientific knowledge and simulations with machine learning, nonlinear dynamics and network science. His experience spans academia, private IT (data) sector and government research labs.
» LinkedIn
Fragile Earth 2023

Emre Eftelioglu

Emre Eftelioglu is an Applied Scientist working with geospatial datasets to identify patterns which were overlooked by traditional machine learning methods. His main research focus is on Urban Mobility, but he also works on understanding the interconnections between Food Energy and Water resources to improve sustainability.
» LinkedIn
Fragile Earth 2023

Bistra Dilkina

University of Southern California
Bistra is an Associate Professor of Computer Science at the University of Southern California. She is also the co-Director of the USC Center for AI in Society. She is one of the junior faculty leaders in the young field of Computational Sustainability, and her work spans discrete optimization, network design, stochastic optimization, and machine learning.
» LinkedIn
Fragile Earth 2023

Kathleen Buckingham

Kathleen is Director of Impact for veritree, a ground-based, restoration monitoring tool that unlocks finance for planting organizations. She is an experienced research lead tackling natural resource and planetary health data challenges. At veritree, Kathleen leads a team that investigates new methods and technologies to collect and assess data in order to understand restoration impact. Kathleen holds a Ph.D in Geography and the Environment from the University of Oxford.
» LinkedIn
Ramakrishnan Kannan

Ramakrishnan Kannan

Oak Ridge National Laboratory
Ramki is the Group leader for Discrete Algorithms in Oak Ridge National Laboratory focusing on large scale data mining, machine learning and graph algorithms on HPC systems and modern architectures with applications from scientific domain.
» Website
Fragile Earth 2023

James Hodson

AI for Good Foundation
James is the AI for Good Foundation’s Co-founder and CEO, who has previously spearheaded Artificial Intelligence initiatives at a number of global firms, and has built successful (and sustainable) for-profit ventures in a variety of industries.
» LinkedIn
Fragile Earth 2023

Rose Yu

UC San Diego
Rose is an assistant professor at UC San Diego department of Computer Science and Engineering and Halıcıoğlu Data Science Institute. She is a primary faculty with the AI Group and am affiliated with Contextual Robotics Institute, Bioinformatics and Systems Biology, and Center for Machine-Integrated Computing and Security.
Fragile Earth 2023

Yuzhou Chen

Temple University.
Yuzhou Chen is an Assistant Professor in the Department of Computer and Information Sciences at Temple University. He is also a Visiting Research Collaborator in the Department of Electrical and Computer Engineering at Princeton University. Before joining Temple University, he worked as a postdoctoral scholar in the Department of Electrical and Computer Engineering at Princeton University.
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Fragile Earth 2023

Jiafu Mao

University of Tennessee
Jiafu Mao is a Senior Staff Scientist at Oak Ridge National Laboratory and a Professor in the Department of Industrial and Systems Engineering and Institute for a Secure & Sustainable Environment of University of Tennessee. His work primarily involves understanding and modeling of carbon, hydrology and vegetation dynamics in the Earth terrestrial ecosystem using field measurements, satellite data, process- oriented land surface and Earth system models.
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Fragile Earth 2023

Yulia R. Gel

University of Texas at Dallas
Yulia R. Gel is a Professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of Data Science, inference for random graphs and complex networks, time series analysis, and predictive analytics. She holds a Ph.D in Mathematics, followed by a postdoctoral position in Statistics at the University of Washington.
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Fragile Earth 2023

Huikyo Lee

Jet Propulsion Laboratory
Huikyo Lee is a Data Scientist with Jet Propulsion Laboratory, California Institute of Technology. He has a Ph.D. in Atmospheric Sciences from the University of Illinois at Urbana-Champaign, and a B.S. in Earth and Environmental Sciences from Seoul National University in Korea. Dr. Lee has also had extensive experience with stratospheric dynamics, neural network modeling for satellite remote sensing, atmospheric chemistry, and air quality modeling.
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Fragile Earth 2023