Manil Maskey


Manil Maskey is a Senior Research Scientist with the National Aeronautics and Space Administration (NASA). He also leads the Advanced Concepts team, within the Inter Agency Implementation and Advanced Concepts at the Marshall Space Flight Center and Science Mission Directorate’s Artificial Intelligence initiative at NASA HQ.  His research interests include computer vision, visualization, knowledge discovery, cloud computing, and data analytics. Dr. Maskey's career spans over 21 years in academia, industry, and government. Dr. Maskey is an adjunct faculty at the UAH Atmospheric Science department, a senior member of Institute of Electrical and Electronics Engineers (IEEE), chair of the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics Technical Committee, member of American Geophysical Union (AGU) and AGU Fall Meeting Planning Committee, member of European Geosciences Union (EGU), and member of Association for Advancement of Artificial Intelligence (AAAI).

Dr Gabriele Cavallaro, PhD. Deputy head high productivity data processing, Juelich Supercomputing Center

Gabriele Cavallaro


Gabriele Cavallaro (Senior Member, IEEE) received his B.Sc. and M.Sc. degrees in Telecommunications Engineering from the University of Trento, Italy, in 2011 and 2013, respectively, and a Ph.D. degree in Electrical and Computer Engineering from the University of Iceland, Iceland, in 2016. From 2016 to 2021, he served as the deputy head of the "High Productivity Data Processing" (HPDP) research group at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Germany. Since 2022, he has been the Head of the "AI and ML for Remote Sensing" Simulation and Data Lab at JSC and an Adjunct Associate Professor at the School of Natural Sciences and Engineering, University of Iceland, Iceland. From 2020 to 2023, he held the position of Chair for the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group under the IEEE GRSS Earth Science Informatics Technical Committee (ESI TC). In 2023, he took on the role of Co-chair for the ESI TC. Concurrently, he serves as Visiting Professor at the Φ-Lab within the European Space Agency (ESA), where he contributes to the Quantum Computing for Earth Observation (QC4EO) initiative. Additionally, he has been serving as an Associate Editor for the IEEE Transactions on Image Processing (TIP) since October 2022.

Iksha Gurung


Iksha Gurung is a Computer Scientist working with University of Alabama in Huntsville, supporting National Aeronautics and Space Administration Inter-Agency Implementation of Advanced Concepts Team (NASA-IMPACT). He leads the development and machine learning team in NASA-IMPACT.  His projects include applying machine learning to Earth science phenomena studies and scaling the solutions to production.

Muthukumaran Ramasubramanian


Muthukumaran Ramasubramanian received the M.S. degree incomputer science from the University of Alabama in Huntsville (UAH), where heis currently pursuing the Doctorate degree in computer science. He is also aComputer Science Researcher and leads the Machine Learning Team forNASA–Interagency Implementation and Advanced Concepts Team, UAH. His workfocuses on using deep-NLP techniques to surface novel relationships from largecorpora of text and to deploy deep learning solutions to detecting earthscience phenomena on a global scale. His research interests include machinelearning, big data, computer vision, and scalable cloud services.

Rajat Shinde


Rajat Shinde, a computer scientist at The University of Alabama in Huntsville (UAH), a part of the University of Alabama System, was recently reelected to the Open-Source Geospatial Foundation (OSGeo) Board of Directors. The primary mission of the not-for-profit OSGeo organization is to “support and promote the collaborative development of open-source geospatial software, data, and education.” In April 2023, Shinde joined UAH’s Earth System Science Center, supported by NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT). His research efforts with IMPACT include developing data systems solutions to address needs identified by the science user group on the Multi-mission Algorithm and Analysis Platform (MAAP), a collaborative project between NASA and the European Space Agency that facilitates best practices in data stewardship and analysis. Additionally, he is involved in a project to create a geospatial artificial intelligence foundation model for Earth observation data. Shinde became a charter member of OSGeo, one of the largest and most active open-source geospatial communities, in 2017. He was first elected to the nine-member OSGeo board of directors in 2021, earning the distinction of being the youngest-ever board member. His reelection extends his tenure on the board until November 2025.

Thomas Brunschwiler


Thomas Brunschwiler is a research manager leading the ‘AI for Climate Impact’ activity at IBM Research in Zurich. His team pushes the frontiers of ‘Earth Science Foundation Models’ to accelerate the discovery of climate impacts and transition risks for society and industry. Further, Thomas is responsible for scientific Foundation Model Applications and the coordination of governmental projects. He earned a Certificate of Advanced Studies in Computer Science at ETH Zurich in 2019 and his PhD in Electrical Engineering at the TU Berlin in 2012. Further, Thomas is an IEEE Senior Member, in the program committee of the AAAI Fall Symposium, and an expert at InnoSuisse, an funding organization in Switzerland.

Carlos Gomes


Carlos Gomes is a machine learning engineer at the AI for Climate Team at IBM Research - Zurich. He has a background in computer science and statistics, with research interests in computer vision, neural compression and distributed training of large networks. His current projects include improving the capabilities of the Prithvi Foundation Model through pre-training and understanding how to best leverage it in finetuning downstream tasks such as scene classification, hazard segmentation and data compression.

Alexandre Strube


Alexandre has a PhD in High-Performance computing by the University Autònoma de Barcelona. He worked at the Performance Analysis team at the Jülich Supercomputing Centre from 2010 to 2015, on the Application Support team from 2015 to 2019, and since then he is a Consultant at Helmholtz AI. He is also one of the maintainers of the whole Scientific software stack on Juelich's supercomputers, and he is the official maintainer of LMOD, the module system, for Debian and Ubuntu operating systems. Alexandre develops and maintains Blablador, the LLM inference infrastructure of the Helmholtz Foundation.

Lecture content

Geospatial Foundation Models

The first day of the curriculum on Large-Scale AI for Geosciences focuses on Geospatial Foundation Models. Participants will receive comprehensive, hands-on training in the development and application of AI models specifically designed for geosciences. The lessons will cover various aspects of geospatial data analysis, providing the skills needed to effectively use these models in different stages of geoscience research and practical scenarios.


9:30 - 9:50  (Manil Makey)

IBM and NASA collaboration for foundation models Open Science, collaboration, geospatial foundation model, LLMs

9:50 - 10:20 (Thomas Brunschwiler)

Development and timeline of FM that was published Thomas

10:20 - 10:50 (Carlos Gomes)

Prithvi-Geospatial and Prithvi-WxC model overview

10:50 - 11:00 (Rajat Shinde)

MLCommons Geo-Croissant Overview

11:00 - 11:30


11:30 - 12:00 (Gabriele Cavallaro)

Advancing Geoscience through Large-Scale AI with Supercomputing for Earth Observation and Remote Sensing

12:00 - 13:00 (Alexandre Strube, Iksha Gurung, Muthukumaran Ramasubramanian)

Environment setup, access, dataset preparation  

13:00 - 14:30

Lunch Break

14:30 - 15:30 (Carlos Gomes, Iksha Gurung, Muthukumaran Ramasubramanian)

Fine-tuning based on datasets in Hugging Face and the custom datasets people might be bringing

15:30 - 16:00


16:00-17:00 (Iksha Gurung, Muthukumaran Ramasubramanian)

Deployment and interactions