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Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.

• Behavioral modeling
• Electromagnetic compatibility
• Macromodeling
• +7 more

Michela Spagnuolo is a Research Director at the Italian National Research Council’s Institute of Applied Mathematics and Information Technology (CNR-IMATI-GE) since 2001. Her work bridges Computer Graphics , Geometry Processing , and Computational Topology , with applications in 3D Shape Analysis , Cultural Heritage , and Bioinformatics . Co-supervised 6 PhD theses (plus 2 ongoing) Associate Editor for journals like The Visual Computer and Computers&Graphics Chair of conferences including Shape Modeling International and EG Workshops on 3D Object Retrieval Research Interests : Focus on geometric and semantic modeling of 3D objects , computational topology for shape analysis , and structural/similarity evaluation . Her work enables advanced 3D search engines , semantic annotation , and medical imaging applications . Article Trends : Recent publications emphasize mesh quality indicators for Virtual Element Methods in engineering simulations and semantic-driven 3D model analysis for medical diagnostics and cultural heritage . Key subtopics include topological decomposition , data processing services , and multi-scale modeling . Scientific Awards : Fellow of the Eurographics Association (2014) Notable Contributions : Led projects like IQmulus (geospatial data analytics) and MultiScaleHuman (medical shape modeling). Pioneered CIDOC CRMdig for cultural heritage documentation and developed techniques for kernel computation in polyhedra .

• Computer Graphics
• Geometry Processing
• Computational Topology
• +4 more

Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)

• Artificial intelligence
• Capacitive sensors
• Embedded software
• +10 more

Sonia Mazzucchi is a Full Professor in the Department of Mathematics at the University of Trento, specializing in probability theory, stochastic processes, and mathematical physics. Her research focuses on Feynman path integrals, quantum mechanics, and operator theory, with recent extensions into quantum information and photonics applications. She teaches core courses including Probability Calculus II, Mathematics and Statistics II, Quantum Information, and Stochastic Processes. Her research interests bridge abstract mathematical theory with practical quantum technologies. She investigates stochastic processes for modeling quantum systems, develops rigorous mathematical frameworks for path integrals on Lie groups, and applies probability theory to quantum random number generation and LiDAR systems. Her work combines functional analysis, measure theory, and differential equations to solve problems in quantum mechanics and information science. Recent publications (2022-2025) reveal a strong interdisciplinary trajectory merging mathematical physics with quantum engineering. Key trends include quantum random number generators using single-photon entanglement, SPAD-based LiDAR innovations for photon flux measurement, and advanced treatments of path integrals on Riemannian manifolds. Her work demonstrates consistent progression from foundational mathematical theory to quantum technology applications. No scientific awards are documented in the available information. Details regarding graduate student supervision and research grants are not specified in the source materials. Her academic activities center on teaching core mathematics courses and publishing in high-impact journals spanning mathematical physics and quantum information science.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +4 more

Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +3 more

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +5 more

Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.

• Machine learning
• Numerical analysis
• Numerical methods
• +10 more

Dr. Annabella Astorino is a researcher at ICAR-CNR (Italian National Research Council) in Rende, Italy, with expertise in machine learning, optimization, and computational biology. Her work focuses on classification algorithms using nonsmooth optimization, spherical separation, and multiple instance learning (MIL) approaches. Key Research Themes Geometric separation techniques (polyhedral, spherical) Multiple instance learning for biomedical applications Nonsmooth optimization in semisupervised learning Clustering methods for edge detection in images Recent publications highlight her development of Lagrangian relaxation for MIL, kernel-enhanced spherical separation algorithms, and DC optimization for computer vision tasks. Her collaborations include researchers like A. Fuduli, M. Gaudioso, and P. Veltri.

• Machine Learning
• Optimization
• Data Mining
• +2 more

Tommaso Bradde is a Fixed-term Researcher (Law 240/10 art.24-a) in the Department of Electronics and Telecommunications (DET) at Politecnico di Torino. His academic affiliation includes membership in the College of Electronic, Telecommunications and Physics Engineering. Dr. Bradde teaches courses across multiple degree programs, including Electromagnetism and Circuit Theory (2025/26, Computer Engineering), Electrical Engineering (2023/24, Electronic Engineering), and Convex Optimization and Engineering Applications (2021/22–2019/20, Mechatronic Engineering). Research Interests Dr. Bradde's work focuses on Electromagnetic Compatibility (EMC) Model Order Reduction techniques Machine Learning applications in IC modeling Convex Optimization for engineering systems Data-driven modeling of electrical components Stability-preserving numerical algorithms Recent Publications His research output spans 2025 and 2024, covering bias-dependent powder core permeability, modified AAA algorithms for stable reduced-order models, and machine learning-based modeling of IC buffers and switched systems. These works intersect Electrical Engineering, Machine Learning, and Scientific Computing. Academic Activities He contributes to doctoral-level instruction through the Introduction to Model Order Reduction course (2024/25, Electrical/Electronics/Communications Engineering) and holds teaching responsibilities in Master's and undergraduate programs.

• Electrical Engineering
• Electromagnetic Compatibility
• Machine Learning Applications in IC Modeling
• +3 more

Marco Corneli is a Professor at the University of Côte d'Azur , where he focuses on developing AI models for archaeology and history. He collaborates with the CEPAM laboratory , is a member of the INRIA research team MAASAI , and part of the LJAD laboratory. His research spans network analysis, optimal transport, and 3D shape analysis. Research Interests : Static/Dynamic Network Analysis (Spectral Clustering, SBMs, HRGs) Optimal Transport for Machine Learning Image and 3D Shape Analysis Probabilistic and Deep Learning Methods Clustering in High-dimensional and Sparse Data Advising : Davide Adamo (2023 - ongoing, co-advised with E. Vila and M. Vuillien) Seydina Ousmane Niang (2023 - ongoing, co-advised with C. Bouveyron and P. Latouche) Giulia Marchello (2020-2023, co-advised with C. Bouveyron) Cédric Vincent-Cuaz (2020-2023, co-advised with R. Flamary) Dingge Liang (2019-2022, co-advised with C. Bouveyron and P. Latouche) Software Development : Co-developed CpDyna (R/C++ package for change point detection in dynamic graphs) Contributed to ordinalLBM (R package for co-clustering ordinal data) Teaching : Delivered courses in statistical learning, graph theory, and data science at University of Côte d'Azur (M2 MA IM), Université Paris 1 (Licence MIASHS), and University of Côte d'Azur (MSc Data Science).

• Static and dynamic network analysis
• Spectral clustering
• Stochastic block models
• +16 more

Francesco Marchetti is a fixed-term researcher (RTDa) at the Department of Mathematics "Tullio Levi-Civita" of the University of Padova. He holds a Master's degree in Mathematics and a PhD in Health Planning Sciences, both from the University of Padova (2016 and 2021 respectively). His research spans interdisciplinary areas including mathematical approximation theory, machine learning, and applications in medical imaging and space weather forecasting. Education Master in Mathematics, University of Padova (2016) PhD in Health Planning Sciences, University of Padova (2021) Research Interests Approximation theory and kernel-based methods Machine learning and deep learning applications Magnetic Particle Imaging (MPI) signal reconstruction Space weather forecasting via neural networks He actively collaborates with the MIDA group at the University of Genova and participates in research networks like CAA (Constructive Approximation and Applications) and RITA (Rete Italiana di Approssimazione). Current work focuses on p-Laplacians on hypergraphs and associated machine learning frameworks. Scientific Awards INdAM Postdoctoral Grant (2021)

• Approximation Theory
• Kernel-Based Approximation
• Machine Learning
• +3 more

Giorgio Stefano Gnecco is a Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences at IMT School for Advanced Studies Lucca, where he works within the Analysis of compleX Economic Systems (AXES) research unit. His academic career spans multiple disciplines including optimization, machine learning, game theory, and their applications in economics, finance, and engineering. His research interests focus on optimization applied to actuarial sciences, economics, finance, and engineering; game theory; statistics; machine learning theory and applications; causal inference for economic policy evaluation; and environmental economics. His work demonstrates interdisciplinary connections between mathematical theory and practical applications across diverse fields, with particular emphasis on developing computational methods for complex economic systems. His publication record shows consistent output across multiple domains, with recent work spanning machine learning algorithms, image processing techniques, economic modeling, and applications in music performance analysis. The breadth of his research indicates strong methodological foundations in mathematical optimization and statistical learning, applied to problems ranging from flood hazard assessment to Parkinson's disease classification. Among his notable achievements are five Italian National Scientific Qualifications for professorial positions in various fields, demonstrating his recognized expertise across multiple academic disciplines. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Neural Networks, and Associate Editor for Neurocomputing. Professor Gnecco leads numerous research projects including the "PRIN PNRR 2022" project "MOTUS - Automated Analysis and Prediction of Human Movement Qualities," the "INdAM-GNAMPA 2023" project on machine learning methods for Shapley Value estimation, and the "ROBOFARM" project on edge computing for precision agriculture. He has coordinated multiple international research collaborations between Italy and France through the Galileo program. His research unit (AXES) focuses on complex economic systems analysis, with applications spanning environmental economics, financial systems, and human movement analysis. The collaborative nature of his work is evident through his extensive network of domestic and international collaborators across multiple universities and research institutions.

• Optimization
• Machine Learning
• Game Theory
• +6 more

Wolfgang Erb is an Associate Professor in Numerical Analysis at the Department of Mathematics “Tullio Levi-Civita”, University of Padua (Italy). He specializes in multivariate approximation and computational methods for inverse problems, with applications in biomedical imaging. Current Position: Associate Professor, University of Padua Previous Positions: Postdoctoral Fellow at University of Lübeck, Assistant Professor at University of Hawaii at Manoa Education: PhD in Mathematics, Technical University of Munich (2010) Research Interests: Wolfgang Erb's research focuses on mathematical models for signal processing, including: Lissajous curve-based approximation techniques Uncertainty principles applied to manifolds and graphs Kernel methods for learning on networks Fast reconstruction algorithms for inverse problems Applications in Magnetic Particle Imaging and biomedical imaging Recent Publications Trends: His work centers on numerical analysis and computational mathematics, particularly in polynomial interpolation frameworks and biomedical imaging applications. Facilities & Collaborations: Wolfgang engages with advanced university facilities including High Density EEG systems, MRI & PET scanners, and computational infrastructure at University of Padua. Teaching: He teaches “Mathematical Models and Numerical Methods for Big Data” in the Data Science master’s program.

• Multivariate approximation
• Lissajous curves
• Uncertainty principles on manifolds and graphs
• +5 more

Alvise Sommariva is an Associate Professor with Full Professor habilitation in Numerical Analysis (MATH-05/A) at the Department of Mathematics, University of Padua. He maintains an active research program in approximation theory and numerical analysis while contributing significantly to academic service through conference organization and editorial work. Department of Mathematics, Tullio Levi-Civita, University of Padua Visiting Professor at Jagiellonian University, Kraków Member of multiple scientific committees for international conferences Co-managing editor of Journal of Approximation Software His research focuses on Numerical Analysis and Approximation Theory, particularly Quadrature and Cubature Formulas, Multivariate Polynomial Approximation, and Meshless Methods. He develops practical computational tools for numerical integration and approximation across various geometric domains including intervals, triangles, squares, disks, spheres, and tetrahedrons. Professor Sommariva actively contributes to the approximation theory community through leadership in the RITA (Ricerca Italiana in Teoria dell'Approssimazione) research network and editorial work, including special issues dedicated to prominent researchers in the field. Editor of Special Issue Dedicated to Len Bos (2024) Scientific committee member for multiple international conferences through 2026 Regular presenter at major approximation theory conferences He teaches in the Doctoral Program in Mathematical Sciences and supervises bachelor students in the Laurea degree program at University of Padua, with particular recognition for his Numerical Analysis courses in Energy Engineering. Professor Sommariva maintains an extensive online presence with computational resources for approximation theory, including MATLAB and Python implementations of numerical methods, and actively collaborates with researchers across Europe and beyond.

• Numerical Analysis
• Approximation Theory
• Quadrature and Cubature Formulas
• +5 more

Matteo Scandella is an Assistant Professor at the University of Bergamo , Italy, since February 2024. Previously, he served as a post-doctoral researcher at Imperial College London (2020–2024). He holds a PhD in Control Systems (2019) and advanced degrees in Computer Science Engineering from the University of Bergamo (Bachelor 2014, Master 2016). His research focuses on kernel-based machine learning techniques applied to system identification , nonlinear dynamics , and control systems , with emphasis on aerospace applications and mechatronics. He has developed methods for stable nonlinear system modeling, continuous-time system identification, and data-driven control strategies like SelfMPC. Education: Bachelor Degree in Computer Science Engineering (2014) – University of Bergamo Master Degree in Computer Science Engineering (2016) – University of Bergamo PhD in Control Systems (2019) – University of Bergamo Research interests span kernel methods (e.g., manifold regularization, RKHS), stability analysis of nonlinear systems, and data-driven control . His work bridges theoretical advancements with engineering applications such as health monitoring of aerospace actuators and urban traffic optimization. Recent publications highlight innovations in automated MPC tuning and graph-based system identification techniques. Teaching includes courses like Automatica (6 CFU) and laboratory modules in sustainable industrial systems. His research has been published in top journals like Automatica and conferences such as L4DC and SYSID.

• Control Systems
• System Identification
• Machine Learning
• +3 more