Dr. Caleb Robinson, Microsoft AI for Good Research Lab

Bio: Caleb is a Research Scientist in the Microsoft AI for Good Research Lab. He graduated from Georgia Tech with a PhD in 2020 and his work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. For example: self-supervised methods for training deep learning models with large amounts of unlabeled satellite imagery, human-in-the-loop methods for creating and validating modeled data layers, and domain adaptation methods for developing models that can generalize over imagery captured through space and time.

Dr. Anthony Schultz (ESRI)

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.

Dr. Dave Thau (WWF)

Bio: Dave Thau is WWF's Global Data and Technology Lead Scientist. He joined WWF in 2019, bringing with him over 30 years of software development experience. Prior to WWF, he worked at Google where he helped launch Google Earth Engine, Google's geospatial big-data processing platform, and managed developer relations for Google Earth Engine and Google Earth Outreach. He has also worked with the Global Biodiversity Information Facility, the California Academy of Sciences, the Kansas University Museum of Natural History, and the All Species Foundation.

Dave’s work in the fields of data management, sustainability, artificial intelligence, and remote sensing has appeared in journals like Science, Nature, Remote Sensing of Environment, Sustainability, and Ecological Informatics. While at Google, he helped develop many projects including Global Forest Watch, with the World Resources Institute, and Map of Life, with researchers from Yale and the University of Florida.

Dave holds degrees from the University of California, Los Angeles, the University of Michigan, Ann Arbor, and a doctorate in computer science from the University of California, Davis. He also has an ant named in his honor - the charming Plectroctena thaui.

Dr. Rose Yu (UCSD)

Bio: Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. 

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 Army ECASE Award, NSF CAREER Award, Hellman Fellow, 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’.

Fragile Earth Workshop KEYNOTE: Prof. İlkay Altıntaş (UCSD)

Title: Using Data for Wildland Fire Science and Management 

Abstract:  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.

Bio: Dr. İlkay Altıntaş, 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. The WoRDS Center specializes in the development of methods, cyberinfrastructure, and workflows for computational data science and its translation to practical applications. The WIFIRE Lab is focused on artificial intelligence methods for an all-hazards knowledge cyberinfrastructure, becoming a management layer from the data collection to modeling efforts, and has achieved significant success in helping to manage wildfires. Since joining SDSC in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible. Her work has been applied to many scientific and societal domains including bioinformatics, geoinformatics, high-energy physics, multi-scale biomedical science, smart cities, and smart manufacturing. She is also a popular MOOC instructor in the field of “big” data science and reached out to more than a million learners across any populated continent. Among the awards she has received are the 2015 IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers and the 2017 ACM SIGHPC Emerging Woman Leader in Technical Computing Award. Ilkay serves on the Board of Governors for the IEEE Computer Society, and was appointed by California Governor Newsom to the Wildfire Technology Research and Development Review Advisory Board. She also a founding board member of nonprofit organizations Data Science Alliance, and Climate and Wildfire Institute.  Ilkay received a Ph.D. degree from the University of Amsterdam in the Netherlands.

Fragile Earth Workshop KEYNOTE: Prof. Aditya Grover (UCLA)

Title: ClimaX: A foundation model for weather and climate

Abstract: Most 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 Workshop KEYNOTE: Dr. Anothony Schultz (ESRI)

Title: Towards the Future: Harnessing GIS, Machine Learning, and Artificial Intelligence in Disaster Management 

Abstract:

In 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.

Thomas Huang (NASA JPL)

Thomas Huang 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. As an advocate for free and open-source software, Thomas led the open-sourcing of many JPL-developed technologies. He is the founder and creator of the Apache Science Data Analytics Platform (SDAP) technology as a community-driven, cloud-based analytics platform. Thomas is a frequently invited speaker and panelist at various US and international events. He is the lead editor of a newly released book, titled Big Data Analytics in Earth, Atmospheric, and Ocean Sciences. It is part of the AGU Special Publication Series. Previously, Thomas served as a member of the NOAA’s Data Archive and Access Requirements Working Group of the NOAA’s Science Advisory Board (SAB). Outside of JPL, Thomas is a Computer Science lecturer at the California State Polytechnic University, Pomona, and a member of its Industry Advisory Board.

Tom Gulbransen (NSF)

Tom Gulbransen is Program Director, Office of Advanced Cyberinfrastructure, Computer and Information Science and Engineering (NSF) where his concentration is on the nexus of cyberinfrastructure research and workforce development. Tom focuses on the Advanced Cyberinfrastructure Coordination Ecosystem of Service & Support program (https://ACCESS-CI.org). Prior to joining NSF, he served as Project Manager for Construction of Cyberinfrastructure and Data Products for NSF’s National Ecological Observatory Network (https://www.neonscience.org) where his focus was on the cyberinfrastructure and ecological data products.  As Senior Scientist in Environmental Informatics at Battelle Memorial Institute, his teams created data acquisition, integration, and knowledge management systems in response to the multi-agency activities associated with the Deepwater Horizon incident, as well as designed and built architecture and logical models for the relational data model and ontology knowledgebase. Finally, he has 27 years of experience as a volunteer firefighter, Basic Life Support EMT, and Technical Rescue Technician. 

Prof. İlkay Altıntaş (UCSD)

Dr. İlkay Altıntaş, 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. The WoRDS Center specializes in the development of methods, cyberinfrastructure, and workflows for computational data science and its translation to practical applications. The WIFIRE Lab is focused on artificial intelligence methods for an all-hazards knowledge cyberinfrastructure, becoming a management layer from the data collection to modeling efforts, and has achieved significant success in helping to manage wildfires. Since joining SDSC in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible. Her work has been applied to many scientific and societal domains including bioinformatics, geoinformatics, high-energy physics, multi-scale biomedical science, smart cities, and smart manufacturing. She is also a popular MOOC instructor in the field of “big” data science and reached out to more than a million learners across any populated continent. Among the awards she has received are the 2015 IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers and the 2017 ACM SIGHPC Emerging Woman Leader in Technical Computing Award. Ilkay serves on the Board of Governors for the IEEE Computer Society, and was appointed by California Governor Newsom to the Wildfire Technology Research and Development Review Advisory Board. She also a founding board member of nonprofit organizations Data Science Alliance, and Climate and Wildfire Institute.  Ilkay received a Ph.D. degree from the University of Amsterdam in the Netherlands.