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

Hybrid Quantum-classical Workflows In Modular Supercomputing Architectures With The Jülich Unified Infrastructure For Quantum Computing
The implementation of scalable processing workflows is essential to improve the access to and analysis of the vast amount of high-resolution and multi-source Remote Sensing (RS) data and to provide decision-makers with timely and valuable information. The Modular Supercomputing Architecture (MSA) systems that are operated by the Jülich Supercomputing Centre (JSC) are a concrete solution for data-intensive RS applications that rely on big data storage and processing capabilities. To meet the requirements of applications with more complex computational tasks, JSC plans to connect the High Performance Computing (HPC) systems of its MSA environment to different quantum computers via the Jülich UNified Infrastructure for Quantum computing (JUNIQ).
Courses, lectures, materials
Teaching

GRSS ESI HDCRS End-to-End Machine Learning with High Performance and Cloud Computing
The participants will work through an end-to-end machine learning project for an application from remote sensing by exploiting modern distributed systems (i.e., HPC and cloud computing systems) and state-of-the-art distributed deep learning frameworks.
Calls for papers, funding, vacancies
News and Jobs

Special session on ''Novelty Detection and Lifelong Learning for Robust Performance Under Ever-Changing Conditions''
This special session is for contributions with detection and lifelong learning approaches that tackle remote sensing applications
Academic papers
Recent Publications
Conference Papers
Practice and Experience using High Performance Computing and Quantum Computing to Speed-up Data Science Methods in Scientific Applications
Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification
Practice and Experience in using Parallel and Scalable Machine Learning in Remote Sensing from HPC over Clouds to Quantum Computing
Journals
Remote Sensing Image Classification Using CNNs With Balanced Gradient for Distributed Heterogeneous Computing
Predicting Classification Performance for Benchmark Hyperspectral Datasets
Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images
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
