Alessandro Paolo Daga is a Fixed-term Tenure-Track Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) , Politecnico di Torino. His research focuses on machine diagnostics, vibration monitoring, bearings, digital signal processing, and machine learning applications in industrial systems. Keywords: Bearings, Digital Signal Processing, Machine Diagnostics, Machine Learning, Rotordynamics Awards: Best Poster Award at COMSOL Conference Munich 2023 3rd Place Best Paper at IEEE MetroInd4.0&IoT 2020 1st Place at Surveillance 9 (2017) 2nd Place at Surveillance 8 (2015) Teaching: He teaches courses in Gearbox Failure Analysis and Monitoring, and collaborates in Vibration Mechanics and Vehicle Noise & Vibration at both bachelor’s and master’s levels. He also supervises PhD research in Mechanical Engineering. Recent Research Trends: His work spans aerospace pyroshock testing, wind turbine condition monitoring, and vibration diagnostics for rotating machinery. He applies statistical learning and signal processing to energy systems and industrial equipment maintenance.
Matteo Magelli is a Fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino. His research focuses on digital twins, multibody dynamics, and railway vehicle dynamics, with specific interests in wheel-rail contact mechanics, tribology, and brake simulation. Fixed-term Assistant Professor (2024-present) Teaching roles: PhD Multibody Systems Applications, Master of Science in Machine Construction, and Bachelor-level courses Supervised PhD student: Rosario Pagano (Mechanical Engineering, 40th cycle) Research projects include dynamic simulations for railway braking systems, thermal analysis of synthetic brake blocks, and design of next-generation carbody technology. His work aligns with SDGs 9 (Industry Innovation) and 11 (Sustainable Cities). Selected publications address topics such as twin-disc device adaptation for braking investigations, railway wheel wear modeling, and digital twin integration in train dynamics.
Michele Pagone is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino. His academic role focuses on advanced control theory and its applications in aerospace and automotive engineering. Current academic rank: Assistant Professor Department: DET Invited member of the College of Computer, Film, and Mechatronics Engineering Invited member of the College of Mechanical, Aerospace, and Automotive Engineering Research Interests His research spans autonomous and electric vehicles, nonlinear and optimal control, robust control, and space trajectory optimization. He integrates machine learning and data science into control systems for sustainable mobility solutions. Key areas: Control Theory, Differential Games, Lyapunov Stability, Mechatronics Applications: Aerospace (orbital maneuvers), Automotive (adaptive cruise control), Energy Systems Academic Collaborations Michele supervises PhD students Lucrezia Lovaglio and Lorenzo Calogero, focusing on advanced model predictive control strategies for sustainable mobility. His work bridges theoretical control algorithms with practical implementations in electric vehicles and spacecraft systems. Research Groups Member of the Automatica research group at DET Collaborates on interdisciplinary projects involving Big Data, Neural Networks, and Systems Automation
Gianmarco Lorenti is a Research Fellow and external teaching collaborator at the Department of Energy (DENERG), Politecnico di Torino, where he contributes to research and education in energy systems and electrical engineering. He is actively involved in modeling and optimizing integrated community energy systems, with a focus on demand flexibility and renewable integration. Research Interests: His work spans Energy Systems , Renewable Energy Integration , Demand-Side Management , Optimization of Energy Communities , Smart Grids , and Sustainable Energy . He applies modeling and data-driven techniques to improve the efficiency and sustainability of residential and community-scale energy networks. The recent publications highlight a strong trend toward integrating photovoltaic systems with flexible demand in Italian energy communities, using optimization and data-driven methods. His work bridges electrical engineering with energy policy and environmental sustainability, particularly in urban and residential contexts. Scientific Contributions: Doctoral research on integrated community energy systems (2025) Publications in Energy and Buildings , IEEE Access , and international conferences like ICEM and ICOA Collaboration with experts such as Paolo Lazzeroni, Aldo Canova, and Maurizio Repetto Teaching and Advising: Gianmarco serves as a teaching collaborator in multiple courses including Elettrotecnica , Fondamenti di Elettrotecnica ed Elettronica , and Challenge@PoliTo projects. He supports students in Mechanical, Energy, Aerospace, and Engineering and Management programs, though no formal PhD or Master’s students are listed. Laboratories and Research Groups: He is affiliated with research initiatives at DENERG focused on energy system modeling, smart grids, and sustainable building technologies, contributing to both theoretical and applied energy research projects.
Erica Pastore is a Fixed-term Researcher in the Department of Management and Production Engineering (DIGEP) at Politecnico di Torino , affiliated with the College of Management and Production Engineering . Her work spans mathematical modeling, production planning, and simulation optimization within industrial engineering contexts. Teaching roles: Course Lecturer (2024/25, Management and Production Engineering), Course Collaborator (2025/26, Management Engineering) Sciences: Production Systems , Manufacturing Technologies , Sustainable Operations Her research focuses on mathematical modeling for production planning and optimization , examining variability propagation in manufacturing systems, additive manufacturing time estimation , and circular economy strategies at scheduling levels. Publications address flow line optimization , parallel batching , and healthcare logistics through simulation and analytical modeling . Recent work trends include flow time minimization (2025), additive manufacturing scheduling (2024), idle time reduction (2023), and urban logistics (2022). She has collaborated on 15+ publications since 2012, working with colleagues like Arianna Alfieri , Claudio Castiglione , and Barbara Previtali across production optimization, healthcare systems, and logistics.
Mario Antonio Cucumo is a Full Professor of Industrial Applied Physics at the University of Calabria's Department of Mechanical, Energy and Management Engineering (DIMEG), where he has served since 1983. He coordinates the Energy Engineering program and leads the Laboratory of Building Energetics. His academic journey includes roles as Assistant Professor (1983-1992), Associate Professor (1992-2003), and Full Professor (2003-present). Education: Graduated with honors in Industrial Engineering Technology from the University of Calabria (1979). Research Focus: Professor Cucumo specializes in solar energy conversion, building thermal dynamics, and renewable systems optimization. His work combines theoretical modeling with experimental validation, emphasizing: Photovoltaic performance enhancement Passive building cooling strategies Heat transfer mechanisms in industrial applications Solar thermal system prototyping He has authored 154+ publications, including textbooks on Applied Thermodynamics , Solar Engineering , and Energetics . Publication Trends (2020-2023): Recent research focuses on energy-efficient building technologies (photochromic windows, BIPV systems), computational fluid dynamics for thermal systems, and optimization of hybrid renewable configurations in Mediterranean climates. Studies demonstrate strong integration of experimental data with numerical modeling. Laboratory Leadership: As head of the Building Energetics Laboratory, he oversees experimental research on: Thermographic measurements PV panel performance testing Solar collector prototyping Passive cooling material analysis
Fabio Frustaci is an Associate Professor in the ING-INF/01 scientific-disciplinary sector at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES), University of Calabria. Previously, he served as a Research Fellow (RTd A/RTd B), Assistant Professor, and Adjunct Professor at the same department. From 2011–2014, he was a Visiting Research Fellow at the University of Michigan, Ann Arbor. B.S. in Electronic Engineering (2003), University "Mediterranea" of Reggio Calabria Ph.D. in Electronic Engineering (2007), University "Mediterranea" of Reggio Calabria His research focuses on low-power electronic design , FPGA-based hardware acceleration , and approximate computing , with applications in image processing, wearable systems, and neural networks. He actively explores quantum-dot cellular automata , dynamic voltage scaling , and noise-tolerant logic circuits . Recent publications highlight his work in deep learning hardware , energy-efficient multipliers , and real-time imaging systems . He leads research groups in nanoelectronics and microsystems and collaborates on projects involving SRAM optimization , carry skip adders , and radiation-hardened FPGAs . National Scientific Qualification for Full Professor (2023) Frustaci is affiliated with the Nanoelectronics and Microsystems Lab at DIMES, contributing to advancements in power-aware design , embedded systems , and semiconductor device modeling .
Prof. Andrea Crivellini is an Associate Professor at the Department of Industrial Engineering and Mathematical Sciences (DIISM) within the College of Engineering at Marche Polytechnic University . His research focuses on Computational Fluid Dynamics (CFD) , Turbulence Modeling , and High-Order Numerical Methods , particularly Discontinuous Galerkin (DG) solvers for fluid dynamics and turbulence simulation. Recent publications highlight his expertise in entropy-aware DG schemes, hybrid RANS/LES models, and implicit time integration for turbulent flows. He contributes to advancing CFD applications in aerospace, mechanical systems, and marine engineering. His work emphasizes numerical stability , mesh adaptivity , and high-performance computing .
Ornella Pisacane is an Associate Professor in the Department of Information Engineering at Marche Polytechnic University, Italy. Her research focuses on Operations Research , Optimization , and Green Logistics applications in transportation and energy systems. Role: Associate Professor Department: Information Engineering Contact: o.pisacane@univpm.it Office: Dipartimento di Ingegneria dell'Informazione, Ancona (Mon: 09:30-13:30) Her recent work addresses electric vehicle routing , low emission zones , and green logistics challenges, including energy consumption models, congestion charge compliance, and fleet optimization. Publications span matheuristics , metaheuristic algorithms , and multi-objective optimization frameworks.
Roberto Riggio is a Researcher at the Department of Information Engineering within the College of Engineering at Marche Polytechnic University (UNIVPM) in Italy. His work focuses on cutting-edge networking technologies with an emphasis on edge computing, 5G/6G networks, and software-defined networking solutions. His research has significant implications for the future of telecommunications infrastructure and distributed computing systems. Dr. Riggio's research interests center around network architecture and management, with particular expertise in edge computing, network function virtualization, and federated learning systems. His work explores the intersection of artificial intelligence and networking, developing novel approaches for resource allocation, service placement, and network optimization. His publications demonstrate a strong focus on practical implementations that address real-world challenges in telecommunications, particularly regarding quality of service, latency management, and security in next-generation networks. Analysis of his recent publications reveals a clear research trajectory focusing on the convergence of AI and networking technologies. His work increasingly emphasizes edge-based AI applications, particularly federated learning implementations that maintain data privacy while enabling distributed intelligence. He has made significant contributions to network slicing, zero-touch management, and O-RAN architectures, with applications spanning from vehicular communications to IoT systems. His publications consistently address the critical challenges of latency, resource allocation, and security in modern networked environments. Dr. Riggio actively contributes to the academic community through his research projects and publications, though specific awards or fellowships aren't documented in the available materials. His work appears in reputable venues focused on networking and telecommunications research. While specific details about his advising activities and grant funding aren't provided in the available documentation, his extensive publication record suggests active involvement in research projects. His work appears to be connected to several European research initiatives, particularly those focused on 5G and beyond networks, edge computing, and software-defined networking solutions. The collaborative nature of many publications indicates participation in multi-institutional research efforts. Based on his publication topics, Dr. Riggio likely participates in research groups or labs focused on networking and telecommunications at UNIVPM. His work on projects like AI@EDGE, O-RAN implementations, and network management platforms suggests involvement with specialized research teams developing next-generation networking solutions. His research has practical applications in areas including connected and automated mobility, IoT infrastructure, and content delivery networks.
BELLINGERI MICHELE is a fixed-term researcher at the Department of Mathematical, Physical and Computer Sciences, University of Parma. His academic career focuses on interdisciplinary applications of network science, bridging ecology, epidemiology, and materials physics. He teaches "Physics applied to Gastronomy" in the Gastronomic Science program for multiple academic years. Interdisciplinary network analysis Biodiversity conservation in agricultural ecosystems Epidemiological modeling in social systems Machine learning applications in complex networks Material science under network theory His research spans complex systems theory, with recent work analyzing: (1) biodiversity loss in agricultural food webs using energetic criteria, (2) disease spreading dynamics in social systems through compartmental models, (3) network robustness in weighted systems, and (4) material properties in nanocrystalline films. Publications increasingly incorporate AI and machine learning techniques for ecological and epidemiological predictions.
Vincenzo Bonnici is an Associate Professor in Informatics at the Department of Mathematical, Physical and Computer Sciences , University of Parma, Italy. His academic career includes a PhD in Computer Science from the University of Verona (2015), preceded by a master’s degree from the University of Catania (2011). He has held research positions at prestigious institutions, including the Institute for Genomics and Bioinformatics (IGB) , University of California, Irvine (2013–2014). Education BSc/MSc in Computer Science from University of Catania (2008/2011) PhD in Computer Science from University of Verona (2015) His research focuses on bioinformatics and computational biology , with a strong emphasis on subgraph matching algorithms for biomedical graphs, genomic sequence analysis using information theory, and parallel computing for biological networks. His work spans pangenomics, phylogenomics, and non-coding RNA studies, with applications in GPU and SMP architectures. The article trends reflect his core expertise in graph algorithms and computational genomics. Key contributions include MULTI-GRAPHMATCH (2025) for multigraph analysis, ARC-MATCH (2024) for edge domain-based graph querying, and foundational work on pangenome discovery (e.g., PANDELOS series, 2023). Parallel computing and information theory are recurring themes across his publications. Scientific awards include the ICPR 2014 international graph-matching contest and a best poster award at the Jacob T. Schwartz International School for Scientific Research. He has served as a speaker at 12 international conferences. Teaching includes courses on Artificial Intelligence Algorithms (2025/2026), Artificial Intelligence Laboratory , and Software Engineering at the University of Parma. His work integrates algorithmic innovation with biological data analysis.
GIULIA DI CREDICO is a fixed-term researcher at the Department of Mathematical, Physical and Computer Sciences, University of Parma. She specializes in applied mathematics, computational mechanics, and numerical analysis, with a focus on boundary element methods (BEM) for wave equations and elastodynamics. Her work spans theoretical developments and biomedical applications, including PK-PD modeling for anesthesia control. Teaching: Courses in basic mathematics for animal sciences, laboratory of statics, and numerical analysis for mathematics and engineering programs. Research: Advanced BEM algorithms for dynamic frictional contact problems, time-domain simulations, and biomedical modeling. Publications: 15 recent works highlight her expertise in BEM, elastodynamics, wave propagation, and optimization algorithms.
Domenico Tegolo is an Associate Professor in the Department of Mathematics and Computer Science at the University of Palermo (Unipa), where he teaches courses in programming, image analysis, and computer science. His office is located at Via Archirafi 3490123, Palermo, with office hours held on Wednesdays from 3:00 PM to 5:00 PM. Professor Tegolo's research spans multiple domains with a strong focus on computer vision and image analysis , particularly in medical applications. His work encompasses retinal image processing , capillaroscopy analysis , and medical diagnostics systems. He has also made significant contributions to programming education methodologies and federated learning approaches for agricultural data. His recent publications show a growing interest in large language models and their applications in specialized domains. Analysis of Professor Tegolo's publication record from the past five years reveals a diverse research trajectory that bridges computer science with practical applications in healthcare and agriculture. His work demonstrates consistent innovation in image processing techniques while adapting to emerging technologies like deep learning and large language models. The research shows particular strength in medical imaging applications, with numerous publications on retinal analysis, oral microcirculation, and diagnostic support systems. Professor Tegolo has taught a variety of courses including Theories and Techniques for Image Analysis, Programming with Laboratory, Advanced Programming, and Structured Programming for Mathematics programs. He has also previously taught Operating Systems and Programming Languages courses for Informatics programs, demonstrating breadth across computer science disciplines.
Alessio De Angelis is a researcher affiliated with the University of Perugia, focusing on advanced measurement systems, battery technology, and localization techniques. His work spans disciplines such as electrical engineering, machine learning, and IoT applications. University: University of Perugia Email: alessio.deangelis@unipg.it His research interests include: Battery management and state-of-charge estimation Uncertainty quantification in AI-based systems Ultra-wideband (UWB) and magnetic localization Wireless sensor networks for environmental monitoring Signal processing for quantized and constrained data IoT applications in structural and health monitoring Recent publications highlight his expertise in battery diagnostics, localization algorithms, and AI-driven measurement systems. Key trends involve integrating machine learning with electrochemical impedance spectroscopy (EIS) and optimizing low-complexity sensor architectures.
