HDCRS Summer School 2024
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...
“Thanks for the effort that the committee put into the organization. Hope this Summer School will thrive along the years and attract more people from different scientific communities!“
“Thank you for providing me this opportunity. I am really glad to be the part of HDCRS 2024. I am really looking forward to collaborate with you in future”
“It is wonderful to be here and to learn from this community. I believe there are things I could learn from this summer school at each stage of the PhD. It kind of set another starting point for my next learning journey. A big thank you for organising this!“
“The organisation was perfect!! From support with room reservation to the order of activities and the social events. Thanks to the team for an opportunity to build my capacity in trending subjects.“
“Amazing work you all have put in. Really enjoyed my stay and made some nice friends. Santiago is a wonderful city to be honest and thank you so much for hosting us.“
“The food was so delicious, and I loved that everything was in a great walking distance. This made it very easy to access!“
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 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.
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
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
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
Quantum computing is rapidly advancing, bringing the prospect of a new computing paradigm closer to reality. As the race to find practical use cases intensifies, Earth observation has emerged as a promising area, with several prototypes already developed and tested. In this discussion, we will explore some of the latest developments in the field of Quantum Computing for Earth Observation (QC4EO) and highlight the current challenges. Specifically, we will focus on Quantum Generative Artificial Intelligence, including Quantum Generative Adversarial Networks (GANs) and diffusion models, as well as optimization of Quantum Neural Networks for various Earth observation use cases related to climate change and energy efficiency.
The Instructors
Bertrand Le Saux
Biography
BiographyBertrand Le Saux (Ms. Eng. 1999, MSc. 1999 INP Grenoble, PhD 2003 Univ. Versailles / Inria, Dr. Habil. 2019 Univ. Paris-Saclay) is a research scientist who designs data-driven techniques for visual understanding, in particular for our planet: AI for Earth, for short. He’s interested in tackling practical problems that arise in Earth Sciences and Climate, to bring solutions to current environmental and societal challenges. He has been with the Φ-lab at ESA/ESRIN and the Image-Vision-Learning team at ONERA, and made research stays at CNR/ISTI Pisa (IT), Univ. of Bern (CH), and ENS Cachan (FR). He was co-chair [2015-2017] and chair [2017-2019] for the IEEE GRSS technical committee on image analysis and data fusion (IADF TC). He is currently an associate editor of the Geoscience and Remote Sensing Letters. He is also a co-organiser of the CVPR / EarthVision workshop series.
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
Alvaro Moreno-Martínez
Biography
BiographyAlvaro Moreno-Martínez earned a Ph.D. degree in Physics (2014, summa cum laude) from the University of València, and he is currently a Senior Researcher at the Image Signal Processing Group (ISP) at the same university. Dr. Moreno is a Google Developer Expert (GDE), and his research has been mainly focused on the development of physical and advanced machine learning models and the implementation of operational methodologies for the study of vegetation cover through satellite imagery at different spatial/temporal scales. He has published 44 papers in international peer-reviewed journals, 3 book chapters, and more than 80 international conference presentations. Dr. Moreno is an external professor of the Master of Remote Sensing and the Data Science degree, both at the University of Valencia, and he has participated in 12 projects (7 national, 5 international), and member of the European Geosciences Union, IEEE, and the American Geophysical Union.
Emma Izquierdo-Verdiguier
Biography
Emma Izquierdo-Verdiguier (Ph.D., 2014, University of Valencia) is an Assistant PostDoc in the Institute of Geomatics at the University of Natural Resources and Life Sciences (BOKU, Vienna). Dr. Izquierdo-Verdiguier is a Google Developer Expert from 2022, and 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).
Day 2: Large-Scale AI for Geosciences with Supercomputing and Cloud Computing
Lecture content
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.
Manil Maskey, “An experiential session on Large Geospatial Foundation & Language Models” (get slides)
Thomas Brunschwiler and Carlos Gomes, “Climate Resilience through Earth System Foundation Models” (get slides)
Rajat Shinde, “Geo-Croissant Croissant for Geospatial Data” (get slides)
Gabriele Cavallaro, “Advancing Geoscience through Large-Scale AI with Supercomputing for Earth Observation and Remote Sensing” (get slides)
Repository: github.com/NASA-IMPACT/HDCRS-school-2024/
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).
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.
Iksha Gurung
Biography
BiographyIksha 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
BiographyMuthukumaran Ramasubramanian received the M.S. degree in computer 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
Biography
BiographyThomas Brunschwiler
Biography
BiographyThomas 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
Biography
BiographyCarlos 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
Biography
BiographyAlexandre 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.
Day 3: Large-Scale AI for Geosciences with Supercomputing and Cloud Computing
Lecture content
The second day of the curriculum on Large-Scale AI for Geosciences focuses on Large Language Models for Science. The objective is to equip participants with the knowledge and skills necessary to understand and develop large language models. These models are crucial for advancing research in geosciences, enabling more efficient processing and interpretation of scientific data. The hands-on training will help participants apply these models in various practical and research contexts within the geoscience field.
Bishwaranjan Bhattacharjee and Aashka Trivedi, “INDUS : Effective and Efficient Language Models for Scientific Applications” (get slides)
Repository: github.com/NASA-IMPACT/HDCRS-school-2024/
The Instructors
Bishwaranjan Bhattacharjee
Biography
Bishwaranjan Bhattacharjee is a Senior Technical Staff Member (STSM) and Master Inventor at IBM Research, where he leads projects in deep learning and data management. He has published 40 papers in top conferences such as ACL, EMNLP, and AAAI, and has filed over 60 patents. His work has earned multiple awards, including IBM Outstanding Innovation Awards and Technical Achievement Awards, and has been cited in IBM’s Research Division Accomplishments. Recognized at IBM’s Corporate Technical Recognition Event, his contributions have been integrated into various DB2 LUW versions and Watson Vision Services. Additionally, he is an IEEE Senior Member and has served on numerous conference program committees.
Aashka Trivedi
Biography
Aashka Trivedi is a AI Research Engineer with a focus in knowledge distillation, retrieval augmented generation, model alignment for LLMs, and neural architecture search. A large part of her work makes large language models faster, better and more curated to individual business needs.
Muthukumaran Ramasubramanian
Biography
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.
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.
Day 4: 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. Transitioning to practical applications, the series introduces quantum machine learning, focusing on Quantum Kernel Machines and Quantum Neural Networks. Through hands-on sessions, students will have the opportunity to design and implement quantum machine learning models using simulators. The series culminates with an overview of the current quantum computing landscape and a discussion on future prospects in the field.
Learning Outcomes
- Grasp the basic principles underlying quantum mechanics and quantum computing
- Understand the advantages and limitations of quantum computation, enabling them to navigate through various terms and concepts in the field
- Comprehend the design and real-life applications of quantum kernels and quantum neural networks
Agenda
Introduction to quantum mechanics
- Review on complex numbers
- Hilbert space
- Postulates of quantum mechanics
- Unitary evolution
- Simple Quantum Gates
Introduction to quantum computation
- Quantum computing models
- Current quantum computing hardware
- Quantum circuits
- Examples of quantum algorithms
Quantum machine learning
- What is Quantum Machine Learning
- Adiabatic quantum devices and optimization
- Quantum kernel machines
- Quantum neural networks
Quantum algorithms for Remote Sensing
- Creating a Quantum Machine Learning model for remote sensing dataset (Hands-on session)
- Current challenges and future of Quantum Machine Learning
- Other quantum algorithms for Remote Sensing and Earth Observation
Artur Miroszewski, “Introduction to Quantum Mechanics” (get slides)
Artur Miroszewski, “Quantum Machine Learning” (get slides)
Hands-On Jupyter Notebook, colab.research.google.com/drive/1nWqBuPmck5z1HNxfR2xh2AqyhZi9aaDx?usp=sharing
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.
Grzegorz Czelusta
Biography
Grzegorz is a doctoral candidate at the Faculty of Physics, Astronomy, and Applied Computer Science at Jagiellonian University in Krakow. He earned his master’s degree from the same department, with a thesis on a quantum gravity model of Causal Dynamical Triangulations. Grzegorz’s current research involves using quantum algorithms to simulate physics phenomena at the Planck scale. He is investigating the relationship between the quantum structure of space-time and quantum information. Besides his PhD research, he is also participating in projects related to quantum machine learning in satellite data analysis as well as cryptography, both quantum and post-quantum.
Organizers
- GRSS ESI TC/HDCRS WG: Dora Blanco Heras, Rocco Sedona, Iksha Gurung, Manil Maskey, Gabriele Cavallaro
- CiTIUS – University of Santiago de Compostela
- Simulation and Data Lab Remote Sensing (University of Iceland)
In cooperation with and sponsored by
Information overview
- Date and time
Starts 4 June 2024 09:30
Ends 7 Jun 2024 17:00
Central European Summer Time (CEST)
- Registration
Start date: 01 January 2024
End date: 29 March 2024
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. There will be approximately 10 participation grants awarded.
- 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.
