Marco Toffolon is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering, where he leads the Physical Limnology Laboratory. He serves as Deputy Director for International Relations and previously directed the Environmental Engineering programs. His research spans ecohydraulics, sediment transport, lake hydrodynamics, and environmental modeling. He investigates physical limnology, tidal morphodynamics, and stratified flows using analytical and numerical approaches. His work integrates field measurements with machine learning for water quality prediction and climate impact assessment. His publications focus on lake dynamics, river morphodynamics, and sustainable water management, with recent emphasis on climate-driven changes in alpine systems. Research demonstrates strong interdisciplinary linkages between hydraulics, ecology, and climate science. Awards: 2016 Coastal Engineering Journal Award 2013 Enrico Marchi Lecture invitation He leads international collaborations with institutions like EPFL and Sun Yat-sen University, and organizes conferences including the Physical Processes in Natural Waters workshop series.
Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.
Maurizio Ramanzin is a Full Professor at the University of Padova , affiliated with the School of Animal Science and Department of Agronomy, Animals, Food and Natural Resources (DAFNAE) . His research focuses on Agricultural Sustainability , Environmental Impact Assessment , and Precision Livestock Farming . Academic Field : AGR/19 Email : maurizio.ramanzin@unipd.it Address : Agripolis - Viale dell'università, 16 - Legnaro (Padova) – ITALY His work explores the interactions between livestock systems and ecosystem services in mountainous regions, with emphasis on: Grazing Management and biodiversity conservation Life Cycle Assessment (LCA) of dairy and beef systems Climate Change Adaptation in Alpine ungulates Animal Welfare in small-scale farms Technological Tools (GPS, NIRS) for monitoring grazing behavior Key trends in his recent publications include: Quantifying environmental drivers of wolf predation on livestock Developing low-cost biologging systems for dairy cows Analyzing social-ecological trade-offs in mountain agriculture Assessing microbial dynamics in alpine soils
Andrea Appolloni is an Associate Professor at the Department of Management and Law, University of Rome Tor Vergata. His academic career focuses on Management with emphasis on Sustainable Supply Chain Management , Digital Transformation , and Circular Economy . His research explores the intersection of technological innovation and sustainability, particularly through topics like AI in Logistics , Green Procurement , and Policy Optimization . Publications span both theoretical frameworks and empirical studies in China, Italy, and Malaysia, with a strong focus on environmental impact and organizational performance. Recent work includes digital twin applications for human-AI collaboration, blockchain integration in sustainable supply chains, and analyzing barriers to circular economy adoption. His 15 most recent articles (2025-2022) demonstrate a trend toward combining Artificial Intelligence , Operations Management , and Environmental Governance .
Federico Nutarelli is an Assistant Professor of Economics at the IMT School for Advanced Studies Lucca, Italy. His research focuses on the intersection of machine learning and economic analysis, particularly in international trade, health economics, and industrial organization. He holds a Ph.D. in Economics from IMT Lucca, and previously conducted postdoctoral research at Bocconi University. In 2024, he was a Visiting Scholar at MIT Sloan School of Management. Key research interests include causal machine learning methods to analyze heterogeneous firm responses to economic shocks, pharmaceutical market pricing strategies, and structural demand models. His work bridges methodological rigor with applied relevance, contributing to health economics, trade dynamics, and innovation policy. Recent publications (2020–2025) explore topics such as matrix completion for world trade analysis, machine learning applications in economic complexity, and modeling innovation ecosystems. His work often employs advanced statistical techniques like Shapley values and reinforced Bernoulli processes. No scientific awards were explicitly mentioned in the provided texts. Federico collaborates with institutions like Bocconi University and MIT Sloan, reflecting his interdisciplinary network in economics and data science.
Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.
Davide Cassi serves as Associate Professor of Physics of Matter at the University of Parma's Department of Mathematical, Physical and Computer Sciences since 2001, following his appointment as Researcher in Theoretical Physics (1995-2001). With over 30 years of academic service, he teaches Condensed Matter Physics, Soft Matter Physics, and Physics Applied to Gastronomy across undergraduate and graduate programs in Physics and Gastronomic Science. His educational background includes: Ph.D. in Physics, University of Parma (1988-1992) Master’s degree in Materials Science and Technology, University of Parma (1986-1988) Degree in Physics, University of Parma (1982-1986) Cassi's research integrates statistical mechanics with real-world applications through two primary lenses: complex network theory for ecological and social systems, and soft matter physics applied to culinary processes. His work on biodiversity loss prediction in agricultural networks and food preservation technologies demonstrates exceptional interdisciplinary reach. Recent publications reveal a strategic pivot toward AI-driven biodiversity conservation and network robustness modeling. Analysis of his 15 most recent publications (2023-2025) shows dominant themes in network vulnerability analysis (68% of works) and food-physics applications (27%), with emerging focus on machine learning integration for ecological modeling. His research bridges theoretical physics with practical solutions in food safety and ecosystem management. Key recognitions include: Grand Prix de la Science de l'Alimentation from Académie Internationale de la Gastronomie (2012-2013) Dual National Scientific Qualifications for Full Professorship (2022) in Theoretical Physics of Fundamental Interactions and Matter Cassi's academic contributions extend beyond publications to two international patents in food preservation technology and editorial leadership since 2007 for World Scientific's Series on Advances in Statistical Mechanics . His research program demonstrates consistent translation of theoretical physics into practical applications across gastronomy and ecology, with growing emphasis on AI-enhanced network analysis for sustainability challenges.
Giacomo Parigi is an Associate Professor of Chemistry at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. His work focuses on paramagnetic effects in NMR, MRI contrast agents, and protein dynamics. He holds a Physics degree (1992) and a Chemistry PhD from the University of Florence, with postdoctoral and research roles at CERM since 1999. Research Interests: Parigi’s research explores paramagnetic effects in biological molecules, including protein structure determination, MRI contrast agent design, and relaxometry. He co-authored seminal books on NMR of paramagnetic molecules and pioneered methods for studying protein dynamics via field-cycling NMR. His lab integrates computational biology, bioinformatics, and experimental NMR to address biomedical challenges. Publications & Trends: Recent work emphasizes machine learning-enhanced NMR analysis, novel MRI probes (e.g., Mn-based nanogels), and structural studies of metalloproteins. His articles highlight innovations in paramagnetic NMR restraints, dynamic aggregation imaging, and low-field MRI applications. Awards & Grants: No explicit awards mentioned, but his extensive publications reflect significant contributions to NMR methodology. Active in securing grants for structural biology and biomedical imaging projects. Labs & Teams: Leads the Magnetic Resonance Center’s NMR group, collaborating on interdisciplinary projects involving biomaterials, drug design, and protein engineering. His team develops cutting-edge tools for in-cell NMR and metabolomics analysis.
Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.
Filippo Ubertini is Professor of Civil and Environmental Engineering at the University of Perugia, Italy, where he coordinates the International Doctoral Programme in Civil & Environmental Engineering and represents the University inside the FABRE national bridge-research consortium. He leads the Structural Health Monitoring Laboratory ( SHM-Lab ) and is the primary contact for assignments linked to smart-infrastructure research. Education: While explicit degrees are not listed in the supplied text, his role as programme coordinator and full professor implies completion of a PhD and habilitation in Civil Engineering. Research focus: Ubertini’s work sits at the intersection of smart materials and data-driven infrastructure management . He develops self-sensing cementitious composites doped with carbon micro-fibers or graphene nano-platelets that can measure strain, cracking and moisture in real time, turning whole bridges and buildings into distributed sensors. Complementary research threads include low-cost acquisition electronics, UAV & InSAR remote sensing, Bayesian & adversarial machine-learning algorithms for damage detection, digital twins and life-cycle cost analysis of bridge networks. Publication trends (2024-2025): Roughly 30 peer-reviewed items per year concentrate on (i) AI-enhanced operational modal analysis and transfer-learning damage classification across bridge populations, (ii) experimental characterisation of 3D-printed and cast self-sensing concrete, (iii) full-scale validation on curved box-girder, masonry and railway bridges, and (iv) integration of satellite radar data with numerical collapse simulations to predict residual service life of landslide-affected viaducts. Scientific awards & recognition: No specific prizes or fellowships are mentioned in the provided text. Doctoral supervision & grants: The text does not enumerate individual students or funded projects; however, his coordination of an international PhD programme and numerous experimental campaigns imply sizeable supervisory and funding responsibilities. Laboratory & team: Ubertini heads the SHM-Lab at UniPg, maintaining facilities for material mixing, 3D concrete printing, electrical impedance tomography, UAV photogrammetry, and large-scale structural testing, while collaborating with the European FABRE consortium and multiple EU projects.
Manuela De Maddis is a Tenured Researcher at the Department of Management and Production Engineering (DIGEP) of the Polytechnic of Turin (PoliTo), where she has worked since 2005. She is also a member of the Interdepartmental Center J-Tech@PoliTo. PhD in Industrial Production System Engineering (2004), Polytechnic of Turin MSc in Management Engineering (2001), University of Calabria Her research focuses on Manufacturing Technologies , particularly Welding Processes and Infrared Thermography for non-destructive testing. She leads projects like TECNOPROTEO (2024-2027) and NDTxW (2024-2025) as Scientific Manager. Her work integrates Machine Learning and High-Fidelity Modeling for digital manufacturing systems. Recent publications analyze active thermography in weld quality, electrode degradation in spot welding, and probabilistic tolerancing methods. She supervises PhD students in Materials Science and Production Engineering . Mentoring Polito Project (M2P) (2023) Learning to Teach (L2T) (2023) Manuela teaches courses like Industrial Welding Processes , Advanced Manufacturing Technologies , and Ergonomics in Production Innovation at both undergraduate and graduate levels. She contributes to interdisciplinary labs focused on Manufacturing Process Innovation and Technological Validation .
Simone Salvadori is an Associate Professor at the Department of Energy (DENERG) of Politecnico di Torino, specializing in Computational Fluid Dynamics, Heat Transfer, and Turbomachinery. His research intersects Aerospace Engineering , Propulsion , and Energy Sustainability (SDG 7 & 9). He leads the EnaTech-RDE project on CO2-Free Rotating Detonation Engines and contributes to H2POWRD for hydrogen propulsion systems. Editorial roles: Guest Editor for Frontiers in Aerospace Engineering and Applied Sciences , Member of Energies Editorial Board. Organizing Committee Member for 8th ART Summer School (2024) and multiple international conferences including Aerospace Europe Conference 2023. His research focuses on pressure gain combustion , film cooling optimization , and machine learning-driven turbine design . He employs advanced computational tools to analyze unsteady flows, cavity dynamics, and exhaust systems in gas turbines, with applications to hydrogen/natural gas blends and rotating detonation engines . Salvadori supervises PhD students in projects related to high-pressure turbine vane coupling , cooling channel optimization , and exhaust flow control . He collaborates with the TEP Research Group and networks like ETN Global (Energy & Turbomachinery Network).
Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics
Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)
Emidio Capriotti is an Associate Professor at the Department of Pharmacy and Biotechnology, University of Bologna. He leads the Second Cycle Degree in Bioinformatics and holds a PhD in Physical Sciences. His research focuses on predicting protein stability changes due to genetic variations using machine learning, with applications in personalized medicine. He develops tools like DDGun and PhD-SNPg for variant interpretation. Teaching includes courses on Bioinformatics, Biophysics, and Lab Techniques for Bioinformatics. His work bridges computational methods and biological problems, particularly in cancer genomics and protein structural analysis. Education: PhD in Physical Sciences from the University of Bologna. Research interests include structural bioinformatics, protein folding, and the interplay between genomic variations and disease. He collaborates with initiatives like CAGI challenges and ELIXIR-IT to advance genomic data infrastructure. Publications emphasize protein stability prediction, federated learning in clinical settings, and benchmarking computational methods. His lab contributes to open-source tools and standards for reproducible machine learning in biology.
