HDCRS Summer School 2025
Welcome to the summer school organized by the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group. HDCRS is part of the IEEE Geoscience and Remote Sensing Society (GRSS), in particular of the Earth Science Informatics (ESI) Technical Committee.
This school is the perfect venue to network with students and young professionals, as well as senior researcher and professors who are world-renowned leaders in the field of remote sensing and work on interdisciplinary research with high performance computing, cloud computing, quantum computing and parallel programming models with specialized hardware technologies.
What the participants said...
“I liked the poster sessions, which were useful to learn more about each of the participants' research interests.”
“Great work, thanks a lot for the organisation and effort!”
“Thank you again - The organization was excellent, the lectures and content were amazing and I enjoyed every day of the school - great job!!”
“The school was AMAZING! I am not a computer engineer/ electronics engineer or have a phd but this has given me lot of ideas and I hope to prepare bit more for next years' school. THANK YOU SO MUCH for including not just who are well into programming as this has inspired me to pick up programming again.”
“As my first time in a summer school, I felt excited to meet new people and extending my network.”
Publications
- Coming Soon
Lecture topics and instructors
Day 1: Opening
Opening Introduction
Welcome at the University of Santiago de Compostela and opening of the school .
The IEEE Geoscience and Remote Sensing Society (GRSS) focuses on advancing the science and technology of remote sensing and disseminating knowledge in this field. The society supports various technical committees and working groups that promote research, development, and education in geoscience and remote sensing. One such group is the “High-performance and Disruptive Computing in Remote Sensing” (HDCRS) of the GRSS Earth Science Informatics Technical Committee (ESI TC). HDCRS is the main organizer of this school, and its primary objective is to connect a community of interdisciplinary researchers in remote sensing who specialize in distributed computing (such as supercomputing and cloud computing), disruptive computing (e.g., quantum computing), and parallel programming models with specialized hardware (e.g., GPUs, FPGAs). The activities of HDCRS include educational events, special sessions, and tutorials at conferences, as well as publication activities, which will be presented.
The Instructors
Prof. Dora Blanco Heras
Biography
Dora B. Heras is a full professor in the Department of Electronics and Computer Engineering at the University of Santiago de Compostela (Spain). She received a MS in Physics in 1993 and was awarded a PhD cum laude from this university. In the period from 2005 to 2010 she was appointed as the head of the Sustainable Development Office at this university. Since 2008 she is also with the research centre CiTIUS (Centro de Investigación en Tecnoloxías Intelixentes) where she leads the hyperspectral remote sensing computing line and has received the accreditation as full professor in 2020. He is also co-chair of the High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group of the IEEE GRSS ESI Technical Committee.
Her research contributions cover a range of topics in the combined fields of image processing, remote sensing, machine learning and high performance computing. In particular, in the last ten years her research has been framed in the line of high performance computing and its application to remote sensing. She has participated in research projects funded by Spanish and European institutions, and R&D agreements. She has served as program committee, guest editor and reviewer in several conferences, in particular, the Euromicro 2021 Parallel and Distributed Conference, and serves as reviewer for different top-ranked journals. She is also a member of the Euro-Par conference Steering Committee since 2018 and has acted as co-chair of the co-located workshops for all the editions since 2017.
Gabriele Cavallaro
Biography
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.
Lecture content
The contribution of the Galician Supercomputing Centre to the infrastructures and research on High Performance and Disruptive Computing will be presented.
The Instructors
Lois Orosa
Biography
BiographyLois Orosa defended his PhD. in the University of Santiago de Compostela in 2013. He received the distinction of “Cum Laude” for the quality of his PhD thesis, and he has been doing research on Computer Architecture since then.
Lois Orosa is currently the Director of the Galicia Supercomputing Center (CESGA) since March 2022, which currently has 51 people. As part of the responsibilities assumed at CESGA, Lois Orosa is leading the Galicia Quantum Technology Hub (or “ Polo de Tecnoloxías Cuánticas de Galicia ”). The Hub strategy plan foresees an investment of 154M euros until 2030, from which around 30M were already executed.
Before joining CESGA, he was doing research at ETH Zürich for 4 years in the SAFARI Research group, lead by Onur Mutlu. Before that, he received a research grant to work on Computer Architecture for 3 years in the University of Campinas, where he co-advise a PhD student. He also has been doing other research stays on top International Institutions, both in Industry, in the companies IBM R&D (Haifa, Israel), Xilinx (Dublin, Ireland), Recore Systems (Enschede, Netherlands), and Academia, in Universidade de Illinois en Urbana-Champaign (USA), Universidade Nova de Lisboa (Portugal).
He has contributed very significantly to the field of Computer Architecture in the last few years, making very relevant contributions especially in reliability and security of computer systems. He published in the 4 top venues in this area in the last few years: 4 papers in ISCA, 5 papers in HPCA, 7 papers in MICRO, and 3 papers in ASPLOS, from which he received 19 HiPEAC awards , given to European researchers that publish in strong venues .
He also published 7 additional papers on top venues and journals (Q1 equivalent). The impact of these publications is significant in recent years (470 citations in the year 2023, 1512 citations in total). He also has presented several posters and short papers, and has given multiple talks about his research. He serviced the community by being a reviewer and a program committee member of many conferences, journals and workshops.
Lecture content
After this lecture you will be able to:
– describe the concept of Edge computing and its application to space systems,
– decsribe the motivations and applications of Edge computing on Earth Observation satellites
– describe the building blocks that compose a Payload Data-Handling Unit, the main hardware solutions and their trade-offs
– discuss the main challenges in applying Artificial Intelligence (AI) on board satellites
– describe the current research trends related to onboard payload data-handling solutions, and novel mission concepts using Artificial Intelligence onboard satellites
The application of Artificial Intelligence (AI) algorithms for the processing of spaceborne data at “the Edge” on space systems could enable novel Earth Observation (EO) applications, novel mission paradigms featuring more responsive, reconfigurable and agile satellites. This lesson will first provide an insight on how AI algorithms could complement current EO missions and benefit EO applications. To this aim, it will provide basic knowledge on modern payload data handling units and main hardware solutions for processing AI algorithms on board space systems. Furthermore, it will highlight the main current challenges that limit the applicability of AI algorithms in space. Finally, it will provide an insight on current research trends and related space missions using Edge computing in space.
The Instructors
Gabriele Meoni
Biography
Gabriele Meoni received his MsC degree in Electronic Engineering and his PhD degree in Information Engineering from the University of Pisa respectively in 2016 and in 2020. After completing his doctoral studies, from September 2020 to April 2023 he held the position of Internal Research Fellow at the European Space Agency (ESA) (Advanced Concepts Team (ACT) September 2020 – August 2021, Φ-lab September 2021 – April 2023), where he conducted research on Artificial Intelligence (AI) and neuromorphic computing for onboard spacecraft applications. During 2022-2023, he was a visiting researcher at AI Sweden, focusing on distributed edge learning for satellite constellations. From May 2023 to April 2024, he served as an Assistant Professor in the Faculty of Aerospace Engineering at Delft University of Technology. Currently, Meoni is an Innovation Officer at ESA, with research interests spanning satellite onboard processing, AI for Earth Observation, and neuromorphic computing. Meoni coauthored more than 40 scientific publications.
Lecture content
Google Earth Engine (GEE) is a cloud-based platform that democratizes access to Google’s computational capabilities, allowing for planetary-scale geospatial data analysis to address critical societal issues. Unlike other platforms, it is an integrated tool that is not only accessible to remote sensing scientists but also to a broader audience, who may lack the technical expertise to utilize traditional supercomputers or large-scale cloud computing resources. In this session, we will cover the basics of using GEE for Earth observation and data processing. You will learn how to work with GEE’s multi-petabyte analysis-ready data catalog and understand the peculiarities and logic of using the platform properly. During the hands-on sessions, you will get practical experience in utilizing GEE’s powerful machine-learning capabilities for classification and regression tasks. Additionally, we will show you how to import and export your own data and provide examples of how you can share your results with broader audiences using GEE’s shareable user interfaces.
The Instructors
Emma Izquierdo-Verdiguier
Biography
Emma Izquierdo-Verdiguier (Ph.D., 2014, University of Valencia) is an Senior Scientist in the Institute of Geomatics at the University of Natural Resources and Life Sciences (BOKU, Vienna). Her research interests are focused on the use of Earth Observation data and cloud computing environment for land surface phenology. Her previous research focused on machine learning applied to remote sensing data. In particular, nonlinear feature extraction based on kernel methods, and on automatic object identification and classification using multispectral images. She is also an external professor of the Master of Remote Sensing at the University of Valencia. In 2012, her paper was ranked second in the student’s competition of the IEEE Geoscience and Remote Sensing Symposium (IGARSS). Dr. Izquierdo-Verdiguier is a Google Developer Expert from 2022 and a member of the ELLIS AI excellence network from 2024.
Eric Smit
Biography
Eric Smit is a doctoral candidate at BOKU University, Vienna, working in the BodenPioniere 2050 project group. He completed his BSc and MSc in Civil Engineering and Water Management at the same university, focussing on geomatics and remote sensing. The goal of his current research is the evaluation of sustainable agricultural management practices in Austria, from an Earth Observation perspective. This effort includes soil samples as ground truth data, remote sensing data over multiple spatial, temporal and spectral scales, and interpretable machine learning algorithms.
Day 2: Quantum computing for Earth Observation
Lecture content
The lecture series aims to provide a foundational introduction to quantum computing, tailored for non-specialists. It begins with a presentation of the basics of finite-dimensional quantum mechanics and introduces fundamental concepts such as Hilbert space, operators, quantum states, and measurement. Building upon this essential groundwork, the series delves into the core principles of quantum computation. Topics covered include an overview of existing models of quantum computation, architectures of quantum devices, and the fundamental components of universal quantum computation models, including gates and circuits. Additionally, the lectures offer a concise review of renowned quantum computing algorithms known for their proven efficiency compared to classical counterparts, and an overview of the current quantum computing landscape and a discussion on future prospects in the field. Transitioning to practical applications, the series introduces quantum machine learning, focusing on quantum kernels and quantum neural networks. Through hands-on sessions, co-organized with IBM, students will have the opportunity to experiment with basic concepts in quantum computation, design and implement quantum machine learning models with Qiskit.
Learning Outcomes
- Grasp the basic principles underlying quantum mechanics and quantum computing
- Understand the advantages and limitations of quantum computation, enabling navigation through various terms and concepts in the field
- Comprehend the design and real-life applications of quantum machine learning models
Agenda
Introduction to quantum computation
- Basic notions and postulates of quantum mechanics
- Measurement
- Introduction of quantum gates and circuits
- Computational paradigms and quantum computing hardware technologies
Quantum algorithms
- Logic behind quantum algorithms
- Advanced circuits
- Review of main algorithms
Hands-on session 1: Qiskit
- Qiskit pattern workflow
- Qiskit primitives: Sampler, Estimator
Quantum Machine Learning (QML)
- Introduction to QML theory
- Data encoding
- QML models and applications in Earth observation
- Advantages and limitations of QML
Hands-on Session 2: Quantum Machine Learning for Satellite Data Analysis with Qiskit
- Implementation of quantum machine learning models with Qiskit
- Quantum kernels and quantum neural networks for Earth observation
The Instructors
Artur Miroszewski
Biography
Artur Miroszewski is a postdoctoral researcher at Jagiellonian University. He obtained his Ph.D. in 2021 from the National Centre for Nuclear Research, Warsaw, Poland, in the field of theoretical physics. His doctoral thesis investigated the potential existence of quantum gravitational effects in the early universe, proposing a primordial singularity avoidance scenario known as the Big Bounce. His research also explored possible observational signatures of this scenario within the gravitational waves spectrum. Currently, Artur is actively involved in a European Space Agency project focusing on the exploration of quantum machine learning applications for satellite data analysis. His primary focus revolves around the utilization of quantum kernel methods for classification tasks.
Francesco Mauro
Biography
Francesco Mauro received the Master’s degree (with honors and career mention) in Electronic Engineering for Automation and Telecommunications in 2022 from the University of Sannio, Benevento, Italy, where he is currently pursuing a Ph.D. degree. He is also a Visiting Researcher at the Φ-lab, European Space Agency, Frascati, Italy, where he collaborates on research related to Quantum Machine Learning applied to Earth Observation. He has co-authored several papers in the field of remote sensing and has presented his work at reputed international conferences. He received the Best Poster Award at the IEEE GRSS IADF School 2024. His research interests include remote sensing and satellite data analysis, as well as Artificial Intelligence and Quantum Machine Learning techniques for Earth Observation. Since 2024, he has been serving as the IEEE GRSS Chapter Coordinator for Region 8.
Alessandro Sebastianelli
Biography
Alessandro Sebastianelli received the degree (cum laude) in electronic engineering for automation and telecommunications from the University of Sannio, Benevento, Italy, in 2019, where he also pursued the Ph.D. degree in Information Technologies for Engineering.
His research topics mainly focus on remote sensing and satellite data analysis, artificial intelligence (AI) techniques for Earth observation, data fusion and quantum machine learning. He has co authored several articles to reputed journals and conferences for the sector of remote sensing. Ha has been a Visiting Researcher with Φ-lab, European Space Agency ESA/European Space Research Institute ESRIN. He has won an ESA OSIP proposal in August 2020. He received an IEEE award for one the best the thesis in geoscience and remote sensing. Currently he works as Research Fellow in Quantum Computing for Earth Observation at the ESA Φ-lab.
Amer Delilbasic
Biography
Amer Delilbasic received his Bachelor and Master degree (cum laude) in Information and Communication Engineering from the University of Trento, Italy, in 2019 and 2021 respectively. He is currently pursuing his PhD in Computational Engineering at the Jülich Supercomputing Centre, Germany, and the University of Iceland, Iceland.
His research is mainly focused on machine learning and optimization based on quantum computing and high-performance computing for Earth observation. He has co-authored several articles to reputed journals and conferences for the sector of remote sensing. He has been a Visiting Researcher with Φ-lab, European Space Agency ESA/European Space Research Institute ESRIN. He has won an ESA OSIP proposal in 2021.
Silvia Liberata Ullo
Biography
Radha Pyari Sandhir
Biography
Dr. Radha Pyari Sandhir is an EMEA Workforce & Education Advocate and the Assistant Program Manager of the Qiskit Advocate Program at IBM Quantum. She champions inclusive educational approaches and is dedicated to building a diverse future quantum workforce. Previously she worked in IBM Quantum as a Learning Experience Designer and the India Community Lead. Radha has pioneered innovative approaches to quantum education, notably designing and leading the world’s first global game-based quantum computing educational program. Her interest lies in making quantum computing accessible and engaging for diverse learners worldwide, and in this regard has created educational quantum games and a book called Quantum Kittens that teaches quantum computing concepts to non-technical audiences through stories about cats.
Albert Garcia Fernandez
Biography
Albert Garcia Fernandez is an Engineer and Master in Telecommunications from UPC. Currently, he is a member of the Quantum Technologies Council of the Generalitat and in IBM is the operations officer for Quantum Data centers and researcher for HPC-Quantum integration technologies. With over 20 years of experience in multinational companies, he has acquired a specific background in HPC, which he has combined with a transition to Quantum technology environments over the last 5 years.
Day 3: Multimodal foundation models for EO
Lecture content
Foundation Models (FMs) represent the latest leap forward in AI, following the era of Deep Learning. Trained on vast amounts of unlabeled data through self-supervised learning (SSL), these models capture rich patterns that can be applied to a wide array of downstream tasks—even with limited or no additional training data. This paradigm holds particular promise for Earth Observation (EO) and Earth Sciences by enabling breakthroughs in analytical, predictive, and even prescriptive capabilities.
In EO and Earth Sciences, FMs can significantly enhance applications such as weather prediction and geospatial semantic data mining. By analyzing large-scale climate and atmospheric datasets, they deliver more accurate forecasts across different time horizons and reveal complex patterns in environmental systems. Their latent space representations and embeddings also enable powerful insights while reducing the need for extensive labeled data—a critical advantage in remote sensing, where labeling is often expensive and time-consuming.
Despite these benefits, integrating FMs into EO workflows poses distinct challenges. EO data often spans multiple modalities, resolutions, and spectral bands, requiring specialized adaptation and careful model updating—especially for “digital twin” scenarios where AI must remain synchronized with real-world changes. Moreover, FMs demand significant computational resources and optimized training strategies, particularly when handling enormous, continuously growing geospatial datasets. Evaluating and benchmarking FMs for these specialized applications further complicates their deployment, as existing benchmarks may be limited in scope.
This session provides a comprehensive introduction to TerraMind, a large-scale generative multimodal foundation model for EO. We will begin by exploring the theoretical concepts behind TerraMind, including its dual-scale early fusion architecture that integrates both token-level and pixel-level representations across nine geospatial modalities. As a beginning, the session covers:
- A lecture on High-Performance Computing (HPC) strategies for training and deploying large-scale EO models.
- A dedicated lecture on TerraMind, focusing on its architecture, training data, and benchmark performance.
Participants will then engage in hands-on sessions including:
- Generative capabilities of TerraMind across modalities.
- Standard fine-tuning techniques for downstream EO tasks.
- Thinking-in-Modalities (TiM): a novel approach introduced by TerraMind to generate artificial data during fine-tuning and inference, enhancing model performance.
Throughout the session, participants will gain practical experience in data preparation, model fine-tuning, and deployment workflows, equipping them with the skills to effectively utilize TerraMind in operational EO settings.
Repository: github.com/NASA-IMPACT/HDCRS-school-2025
Agenda
Block 1: Introduction
- An introduction to HPC for AI
- Leveraging HPC for EO applications: opportunities and challenges
- TerraMind: Introduction and Background Theory and Implementation
Blocks 2, 3 and 4:
- Hands-on TerraMind
The Instructors
Johannes Jakubik
Biography
Johannes is a Staff Research Scientist within the AI for Climate Impact team at IBM Research Europe. In this role, he leads research activities focused on pretraining and scaling multi-modal AI foundation models for earth observation, as well as developing AI foundation models for weather and climate assessments in collaboration with NASA, ESA, and the EU Horizon program. His work on large-scale deep learning for Earth observation has been recognized with the NASA Marshall Space Flight Center Honor Award, multiple IBM accomplishment awards, and has been featured in various international and national media. He also supervises and mentors Ph.D. students at MIT and ETH Zurich. Johannes graduated from KIT and ETH, where his research spanned across all relevant subfields of deep learning-based systems: data-centricity, model-centricity, and human-centricity. During his Ph.D., he received a best paper award and a best paper award nomination for theoretical contributions to human-centric AI. In fall 2024, he was recommended as a top candidate for a tenure-track professorship at a German university of excellence. Together with a range of esteemed co-authors, his work has been published in highly recognized journals and conferences.
Alexandre Strube
Biography
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.
Link to slides: go.fzj.de/2025-06-05-talk-hdc-rs
Rocco Sedona
Biography
Rocco Sedona (Member, IEEE) received the B.Sc. and M.Sc. degrees in information engineering from the University of Trento, Trento, Italy, in 2016 and 2019, respectively, and the Ph.D. degree in computational engineering from the University of Iceland, Reykjavik, Iceland, in 2023. He is a member of the “AI and ML for Remote Sensing” Simulation and Data Lab, JSC, Germany. His research interests primarily lie in the field of deep learning and its application to remote sensing data. He has extensively utilized optical satellite data acquired by Landsat (NASA) and Sentinel (ESA) missions toward near real-time land-cover classification. In addition, he specializes in distributed deep learning on high-performance computing systems, an area of study that he has been actively engaged in since 2019.
Þorsteinn Elí Gíslason
Biography
Þorsteinn Elí Gíslason received a B.Sc. degree in Physics and an M.Sc. degree in Computational Engineering from the University of Iceland. His master’s research focused on foundation models for Earth observation as part of the Prithvi-EO-2.0 project, where he contributed to both pretraining on high-performance computing (HPC) systems and downstream validation. He is currently a Researcher at the Jülich Supercomputing Centre, Forschungszentrum Jülich, where he works on foundation model research for remote sensing. His contributions span both pretraining large-scale models on HPC systems and fine-tuning them for Earth observation applications. Additionally, he is involved in improving hybrid workflows that integrate HPC and cloud resources.
Day 4: Multitemporal foundation models for EO
Lecture Content
Foundation Models (FMs) represent the latest leap forward in AI, following the era of Deep Learning. Trained on vast amounts of unlabeled data through self-supervised learning (SSL), these models capture rich patterns that can be applied to a wide array of downstream tasks—even with limited or no additional training data. This paradigm holds particular promise for Earth Observation (EO) and Earth Sciences by enabling breakthroughs in analytical, predictive, and even prescriptive capabilities.
In EO and Earth Sciences, FMs can significantly enhance applications such as weather prediction and geospatial semantic data mining. By analyzing large-scale climate and atmospheric datasets, they deliver more accurate forecasts across different time horizons and reveal complex patterns in environmental systems. Their latent space representations and embeddings also enable powerful insights while reducing the need for extensive labeled data—a critical advantage in remote sensing, where labeling is often expensive and time-consuming.
Despite these benefits, integrating FMs into EO workflows poses distinct challenges. EO data often spans multiple modalities, resolutions, and spectral bands, requiring specialized adaptation and careful model updating—especially for “digital twin” scenarios where AI must remain synchronized with real-world changes. Moreover, FMs demand significant computational resources and optimized training strategies, particularly when handling enormous, continuously growing geospatial datasets. Evaluating and benchmarking FMs for these specialized applications further complicates their deployment, as existing benchmarks may be limited in scope.
This session provides a comprehensive introduction to Prithvi-EO, a geospatial foundation model. We will begin by exploring the theoretical underpinnings of Prithvi-EO. Participants will then engage in a hands-on workshop focused on fine-tuning the model for specific downstream tasks. Finally, we will cover the deployment of the trained model and how to interact with it in an operational setting. Throughout the session, participants will learn best practices for data preparation and model fine-tuning.
Repository: github.com/NASA-IMPACT/HDCRS-school-2025
Agenda
Block 1: Overview
- Fundamentals of Multitemporal foundation models for EO
- IEEE GRSS ESI, NASA and Foundation models
Block 2 (Hands on)
- Environment Check
- Finetuning Prithvi EO
Block 3 & 4 (Hands on)
- Deploy Finetuned model
- Interact with the Deployed Finetuned Models (including the terramind model)
The Instructors
Manil Maskey
Biography
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).
Sujit Roy
Biography
Dr. Sujit Roy is a Lead AI Researcher and Computer Scientist with NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT), a program under the Marshall Space Flight Center, where he spearheads cutting-edge machine-learning work for Earth-science missions. Roy led NASA’s first open-source Geospatial AI Foundation Model initiative (Prithvi-EO), forging a partnership with IBM Research and releasing the model on Hugging Face; for this effort he received IMPACT’s 2023 Planet Award, which recognizes innovators whose ideas rapidly attract attention and resources. His research centers on foundation models, remote-sensing time-series analysis, and climate applications of deep learning. His recent work includes the Prithvi WxC weather and climate foundation model, the Clifford neural operator, the WINDSET weather-AI benchmark, a heliophysics foundation model supported by compute resources from the NSF NAIRR program, with results published in reputed journals and conferences. Holding a Ph.D. in Computer Science, Roy has more than a decade of R&D experience, including explainable AI research at the University of Manchester, and co-founded the neuro-AI start-up BrainAlive.
Iksha Gurung
Biography
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
Biography
Muthukumaran Ramasubramanian received the M.S. degree in computer science from the University of Alabama in Huntsville (UAH), where he is currently pursuing the Doctorate degree in computer science. He is also a Computer Science Researcher and leads the Machine Learning Team for NASA–Interagency Implementation and Advanced Concepts Team, UAH. His work focuses on using deep-NLP techniques to surface novel relationships from large corpora of text and to deploy deep learning solutions to detecting earth science phenomena on a global scale. His research interests include machine learning, big data, computer vision, and scalable cloud services.
Organizers
- GRSS ESI TC/HDCRS WG: Dora Blanco Heras, Rocco Sedona, Iksha Gurung, Manil Maskey
- CiTIUS – Research Center in Intelligent Systems, University of Santiago de Compostela
- IHPC Simulation and Data Lab Remote Sensing (Gabriele Cavallaro, University of Iceland)
In cooperation with and sponsored by
Information overview
- Date and time
Starts 3 June 2025 09:00
Ends 6 Jun 2025 17:00
Central European Summer Time (CEST)
- Registration
Start date: 17 February 2025
End date: 20 March 2025
The school is free of charge
- Contact and Support
Contact person: Dora Blanco Heras and Gabriele Cavallaro
Email for questions: hdcrs.school@gmail.com
- Venue
Santiago de Compostela (Spain)
Edificio Emprendia
Avenida do Mestre Mateo, 2,
15706 Santiago de Compostela
- Participation Grants
The summer school will welcome up to 30 students. 12 IEEE GRSS Student members are eligible for participation grants of a fixed amount (no receipts required).
- Slides and Recordings
The lectures will be recorded and made available online through the GRSS YouTube channel. Course material will be available after the school on the GRSS website.
