Head of Simulation and Data Lab (Jülich Supercomputing Centre)
Adjunct Associate Professor (University of Iceland)
Prof. Gabriele Cavallaro
Affiliations
Research Topics
Remote Sensing
High Performance Computing
Machine Learning
Quantum Computing
Guest lectures, presentations, workshops
Talks
Challenges and Opportunities in the Adoption of High-performance Computing for Earth Observation Applications in the Exascale Era
High Performance Computing (HPC) enables precise analysis of large and complex Earth Observation (EO) datasets. However, the adoption of supercomputing in the EO community faces challenges from the increasing heterogeneity of HPC systems, limited expertise, and the need to leverage novel computing technologies. This paper explores the implications of exascale computing advancements and the inherent heterogeneity of HPC architectures. It highlights EU-supported projects optimizing software development and harnessing the capabilities of heterogeneous HPC configurations. Methodologies addressing challenges of modular supercomputing, large-scale Deep Learning (DL) models, and hybrid quantum-classical algorithms are presented, aiming to enhance the utilization of supercomputing in EO for improved research, industrial applications, and SME support.
Courses, lectures, materials
Teaching
Machine Learning for Earth Observation powered by Supercomputers
This course exposes the students to the physical principles underlying satellite observations of Earth by passive sensors, as well as parallel Deep Learning (DL) algorithms that scale on High Performance Computing (HPC) systems.
Calls for papers, funding, vacancies
News and events
Fully-funded PhD position
In this PhD, you will develop AI methods to enhance the accessibility and analysis of a vast amount of high-resolution, multi-source EO data. You will research ML methods of encode and compress high-volume EO data into practical, much smaller representations and scale them to large datasets on our supercomputers.
Academic papers
Recent Publications
Conference Papers
Enhancing Training Set Through Multi-temporal Attention Analysis in Transformers for Multi-Year Land Cover Mapping
Challenges and Opportunities in the Adoption of High Performance Computing for Earth Observation Applications in the Exascale Era
End-to-End Process Orchestration of Earth Observation Data Workflows with Apache Airflow on High Performance Computing
Journals
Toward the Production of Spatiotemporally Consistent Annual Land Cover Maps using Sentinel-2 Time Series
Enhancing Distributed Neural Network Training Through Node-Based Communications
Deep Learning-based 3D Surface Reconstruction - A Survey
Book chapters
Remote Sensing Data Fusion: Markov Models and Mathematical Morphology for Multisensor, Multiresolution, and Multiscale Image Classification
Analyzing Remote Sensing Images with Hierarchical Morphological Representations
About me
I did my Erasmus at the University of Iceland, which eventually led to three years of doctoral studies and earning the title of PhD.
Since then, I've been a research assistant at JSC - Jülich Supercomputing Centre, later becoming Head of Simulation and Data Lab. Since 2022, I have been granted adjunct associate Professorship at the University of Iceland in Reykjavík. I work at the intersection of Remote Sensing (RS), High Performance Computing (HPC), Quantum Computing (QC) and Machine Learning (ML).
You can contact me at
g.cavallaro@fz-juelich.de
gcavallaro@hi.is
