Simone Pezzuto is an Assistant Professor in the Department of Mathematics at the University of Trento, specializing in computational cardiac electrophysiology and mathematical biology. His research integrates mathematical modeling, numerical analysis, and biomedical applications. Research focuses on inverse problems in electrocardiography, arrhythmia mechanisms, and cardiac digital twins. Recent work (2024-2025) develops novel methods for Purkinje network reconstruction, atrial fibrillation source localization, and fibrosis-based inducibility prediction. Computational approaches include physics-informed neural networks, multirate schemes, and eikonal modeling for efficient simulations. Key innovations address cardiac conduction system identification from surface ECGs, ablation strategy optimization, and anatomically-accurate atrial modeling. Methodological contributions span regularization techniques for ill-posed problems and parallel-in-time algorithms for large-scale electrophysiology simulations.
Salvatore Pontarelli is a Researcher at CNIT (Italian National Inter-University Consortium for Telecommunications), affiliated with the University of Rome Tor Vergata. He holds a Master's in Electronic Engineering from the University of Bologna (2000) and a PhD in Microelectronics and Telecommunications from the University of Rome Tor Vergata (2003). His work focuses on hardware design for network devices, fault tolerance, error correction codes, and high-speed packet processing. He contributed to EU-funded projects and received a CISCO Research Award (2011) for work on Bloom filters and Ternary CAM. Research interests include hash-based structures (e.g., Bloom filters, hash tables), stateful programmable data planes, and FPGA-based network intrusion detection systems. He led the Defect and Fault Tolerance Group (DFTGroup) under Prof. Adelio Salsano and has extensive experience in collaborative projects with academic and industry partners. Publications highlight innovations in flow monitoring, data plane optimization, and efficient memory management. His work often bridges theoretical algorithms (e.g., cuckoo hashing) with practical hardware implementations, emphasizing real-world applications in networking and telecommunications.
Emma Perracchione is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at Politecnico di Torino, where she conducts research at the intersection of approximation theory, machine learning, and scientific computing. Her work focuses on kernel-based methods, inverse problems, and applications in space weather and solar physics. She is actively involved in teaching and research leadership, including PhD supervision and national projects. PhD in Mathematics, University of Turin (2017, cum laude) M.Sc. and B.Sc. in Mathematics, University of Turin (2013, 2011) Her research interests center on approximation theory and its applications, particularly greedy methods and two-layered kernel machines used for feature reduction in geomagnetic storm forecasting and optimal sampling for solar nanosatellites. These efforts contribute significantly to advancements in inverse problems and scientific computing . Her expertise spans machine learning , imaging , and data-driven modeling , with applications in climate action and space weather. The most recent publications highlight a strong trend in developing and analyzing variably scaled kernels , feature selection via greedy algorithms, and machine learning applications in solar and astrophysical contexts. These works integrate numerical analysis with real-world data from solar wind and imaging instruments, demonstrating a blend of theoretical rigor and practical relevance. Scientific awards received include: GNCS Young Researchers Funding (2020) GNCS Young Researchers Funding (2016) "Luciana Picco Botta" Study Award (2015) COST Short Term Scientific Mission (STSM) grant (2015) Emma Perracchione supervises PhD student Matteo Trombini in the Mathematical Sciences program (40th cycle, 2025–ongoing) and leads the PRIN-funded project GOSSIP – Greedy Optimal Sampling for Solar Inverse Problems (2025–2027). She has taught various courses including Linear Algebra and Geometry , Numerical Methods and Scientific Computing , and advanced topics on Kernels for Machine Learning in aerospace, automotive, and computer science engineering programs. She is a member of the Space Weather Italian Community (SWICo) and the National Scientific Computing Group (GNCS-INdAM) , and serves as Guest Editor for Dolomites Research Notes on Approximation . She has also participated in organizing major conferences such as DWCAA24 and GIMC-SIMAI Young.
