Lorenzo Peroni is a Full Professor at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS). He serves as Deputy Coordinator of the Doctoral School of Mechanical Engineering and is a member of the Interdepartmental Center J-Tech@PoliTO. His research focuses on high strain rate mechanics , materials behavior under extreme conditions , and non-linear finite element modeling . Research Interests include experimental analysis of materials under laser-driven shocks, dynamic deformation of composites, and numerical simulation techniques for blast injury prediction. His recent work explores 3D printing of crash-absorbing structures and recycling of fiber-reinforced polymers . Scientific Contributions : AIAS Capocaccia Award (2007) Fellow of Italian Association of Stress Analysis (2001-) Associate Editor for Metals (2021-) Teaching Activities span doctoral courses on dynamic structural simulation (2013-2025) and master's level instruction in Mechanics of Materials, Numerical Modeling, and Machine Construction. He has supervised numerous research projects including EU H2020 ARIES infrastructure and commercial contracts for motorcycle chassis development.
Paul Major is a Full Professor in the Department of Mechanical and Aerospace Engineering at the Polytechnic University of Turin, where he has established himself as a leading researcher in aerospace systems. He serves as a member of the PhotoNext Interdepartmental Center for Applied Photonics and the University Internship Commission, demonstrating his commitment to interdisciplinary research and student development across multiple domains of engineering. Professor Major's research focuses on digital twin technology, prognostics and diagnostics of aerospace systems, and embedded sensor systems. His work bridges theoretical modeling with practical applications, particularly in the areas of augmented reality for aircraft monitoring, optical fiber sensors for structural health monitoring, and machine learning applications for predictive maintenance of electromechanical systems. His research has significant implications for improving aircraft safety, efficiency, and sustainability, with applications extending to lunar exploration technologies and space habitat design. His recent publications reveal a strong trend toward integrating advanced computational methods with physical systems, particularly in the domains of lunar exploration technology, additive manufacturing for aerospace applications, and sustainable aviation solutions. The interdisciplinary nature of his work spans aerospace engineering, computer science, materials science, and control systems, reflecting the increasingly interconnected nature of modern engineering research. His team has made significant contributions to optical sensor integration, AR visualization for maintenance, and prognostic frameworks for electromechanical systems. Professor Major actively mentors doctoral students, with current advisees including Matteo Bertone, Pierluigi Vergari, Armando Vittorio Atzori, and several others working on cutting-edge aerospace projects including lunar drones, aircraft anti-icing systems, and electromechanical actuator diagnostics. He has secured numerous research grants from both competitive funding bodies and commercial contracts, including projects like ASTRA (Advanced Space Tethers for Remote-sensing Applications), SmartCore, and FreME (Freno Multidisco Ad Attuazione Elettromeccanica Smart). He leads the ASTRA research group focused on Additive manufacturing for Systems and sTRuctures in Aerospace and is actively involved with the student team ICARUS. His work has practical applications in both terrestrial and space environments, with recent projects addressing lunar exploration technologies, sustainable aviation solutions, and advanced monitoring systems for aerospace applications.
Michele Taragna is a Tenured Associate Professor in the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, actively teaching across degree programs: Experimental Modeling for PhD students in Electrical, Electronic and Communications Engineering (2019-2025), Estimation and System Identification for Mechatronic Engineering Master's program (2019-2026), and Automatic Control for Computer Engineering Bachelor's program (2019-2026) as course holder or collaborator. His research centers on Systems and Control Engineering , with primary interests in data-driven control for autonomous vehicles and fleets, direct virtual sensors, and machine learning-enhanced system identification. Key areas include Set Membership methods for robustness under bounded noise, computational complexity reduction in Nonlinear Model Predictive Control (NMPC), sensor fusion for robotics, and applications in automotive suspensions. This work aligns with ERC sectors PE7_1 (Control engineering), PE1_20 (Control theory), and PE6_12 (Scientific computing). Trends in his publications (2024-2004) reveal sustained innovation in applying Set Membership identification to NMPC for autonomous vehicles, achieving real-time feasibility through search domain reduction. Sensor fusion techniques using Kalman filters for mobile manipulators and data-driven filter design for uncertain LTI systems with bounded noise are recurring themes, emphasizing practical implementation and computational efficiency. Scientific awards: None documented in provided materials. Advising and research funding: Supervised PhD student Mattia Boggio (2020-2024) in Electrical, Electronic and Communications Engineering; thesis on Real-time Nonlinear Model Predictive Control with domain reduction. Led the nationally funded PRIN project Controllo ad alte prestazioni a partire dai dati sperimentali (2007-2009) as Scientific Responsible. He is a core member of the Automatica research group within DET, focusing on system identification, control design, and validation for dynamic systems with applications in automotive and robotics domains.
Marcello Dalpasso is an Associate Professor of Computer Science at the School of Engineering, University of Padova, Italy, and a member of the Department of Information Engineering. He has held this position since 2004 after serving as a researcher and teaching assistant at the same university from 1998. Born in Ferrara, Italy (1965) Graduated with highest honors in Electronic Engineering (1990), University of Bologna PhD in Electronic Engineering and Computer Science (1994), Rome His research focuses on integrated circuit testing , fault simulation , and algorithm design . He has developed techniques for IDDQ testing , bridging fault modeling , and Boolean satisfiability applications in digital systems. Other contributions include optimization algorithms for Traveling Salesman Problem (TSP) and efficient data structures. Recent publications highlight his work on Answer Set Programming for timing analysis, Python programming education , and TSP neighborhood exploration . His research spans both theoretical and applied domains, from hardware testing to software development and computational biology. He has co-authored textbooks on Computer Networks , Software Design , and Programming in Java/Python/C++ , serving as a key contributor to educational materials in computer science.
De Marsico Maria is a Full Professor at the Department of Computer Science , Sapienza University of Rome. Her office is located at Via Salaria, 113, 3rd floor, room 313. Email: demarsico@di.uniroma1.it Phone: +39.06.4991.8312 (ext. 28312) Her research interests are aligned with the Department of Computer Science, focusing on broad areas within Computer Science , including Artificial Intelligence , Software Engineering , and Data Structures .
Avola Danilo is an Associate Professor at the Sapienza University of Rome , affiliated with the Department of Computer Science . His research interests span core areas of computer science, including artificial intelligence, algorithms, data structures, and software engineering. Contact: avola@di.uniroma1.it
Alessandro Checco is an Assistant Professor in the Computer Science Department at University of Rome La Sapienza. His research focuses on crowdsourcing, distributed systems, and privacy-preserving technologies, bridging theoretical computer science with practical applications that consider human factors in technological systems. He has established himself as a significant contributor to the field of human computation and privacy-aware systems. His educational background includes: 2020: Fellowship of Higher Education from The University of Sheffield, Higher Education Academy 2015: Ph.D. in Mathematics from Hamilton Institute (Design of decentralised algorithms applied to channel/code selection and convex optimisation for throughput fairness of 802.11 networks) 2010: M.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) 2009: Erasmus Scholarship at Universiteit Gent, Department of Telecommunications 2007: B.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) Checco's research spans multiple areas at the intersection of computer science and social implications of technology. He is particularly interested in Crowdsourcing for Human Computation, Distributed Private Recommender Systems, Information Retrieval, Data Privacy, Distributed Systems, User Data Obfuscation in Web Systems, Societal and Economic Analysis of Online Work, Crowd Workers Unionisation, and Algorithmic Bias. His work often examines how technological systems can be designed to respect user privacy while maintaining functionality, and how crowd work can be structured to be more equitable for workers. His recent publications demonstrate a clear evolution in research focus, beginning with foundational work in wireless networks and distributed algorithms, then shifting toward human computation and privacy-preserving systems. His most recent work increasingly addresses the societal implications of crowd work, including investigations into crowd worker unionization and cooperative models. Several publications examine gender bias in algorithmic systems, reflecting growing attention to fairness and ethical considerations in his field. Among his notable achievements: All That Glitters is Gold-An Attack Scheme on Gold Questions in Crowdsourcing (Best Paper Award) Checco has secured significant research funding and led important projects including the H2020-funded FashionBrain project as Research Director and the EPSRC-funded BetterCrowd project as Research Associate. His work on the FashionBrain project demonstrates his ability to lead large-scale, interdisciplinary research initiatives. He has also received the Technology Innovation Development Award (TIDA) from Science Foundation Ireland. His research has practical applications across multiple domains including recommendation systems (BLC: Private Matrix Factorization Recommenders), peer review assistance using AI, smart farming technologies, and cooperative models for crowd workers (CrowdCO-OP). He has developed frameworks for understanding worker behavior in crowdsourcing platforms and created methods for improving quality control in human computation systems.
Stefano Faralli is an Associate Professor at the Department of Computer Science, Sapienza University of Rome. His academic focus lies in fundamental areas of computer science, including data structures and algorithms. He is affiliated with the University of Rome La Sapienza, contributing to research and education in software engineering and computational theory.
Gabriele Tolomei is an Associate Professor of Computer Science at Sapienza University of Rome. He leads the HERCOLE Lab (Human-Explainable, Robust, and Collaborative Learning) and focuses on advancing AI systems that are interpretable, robust, and decentralized. His research spans machine learning, computer security, and adversarial learning. PhD in Computer Science, Ca' Foscari University of Venice (2011) MSc in Computer Science, University of Pisa (2005) BSc in Computer Science, University of Pisa (2002) His research interests are centered on Explainable AI , Robust Machine Learning , and Collaborative Learning . He has contributed to areas such as counterfactual explanations for graph neural networks, community membership privacy, and fairness in graph algorithms. His work intersects Web Search and Mining with Computational Advertising , emphasizing user engagement and post-click satisfaction. He teaches courses like Operating Systems for BSc Computer Science and Big Data Computing for MSc Computer Science. Current PhD students under his supervision include Edoardo Gabrielli, Fabiano Veglianti, Flavio Giorgi, Matteo Silvestri, and Vittoria Vineis. The HERCOLE Lab, established in 2021, promotes interdisciplinary research on human-centered AI, adversarial resilience, and edge computing. Collaborators include Fabrizio Silvestri (Full Professor), Federico Siciliano (Postdoctoral Researcher), and Ziheng Chen (Research Scientist at Walmart Labs).
Leonardo Soria is an Associate Professor at the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari. His research focuses on applied mechanics, structural dynamics, and vehicle vibrations, with a strong emphasis on computational modeling and experimental validation. Department: Mechanics, Mathematics & Management Email: leonardo.soria@poliba.it Contact: +39 080 596 3484 His work spans topics such as viscoelastic foundations, flutter analysis, road roughness identification, and structural health monitoring. He employs advanced methodologies like the unsteady compressible source and doublet panel method, operational modal analysis, and machine learning techniques for diagnostics. Recent publications highlight his contributions to vibro-acoustical analysis of microsystems, robustness of measurement probes, and shock response synthesis in aerospace. His research also explores fluid-structure interactions in nanoscale systems and energy harvesting from vibrating polymers. Professor Soria actively investigates the dynamics of sharp-edged beams, helicopter cabin vibrations, and vehicle suspension performance assessment. His studies often bridge theoretical models with experimental validation, particularly in scenarios involving Brownian excitation, hydrodynamic coupling, and seismic loading.
Marco Mancini is a Full Professor at the Department of Modern Literature and Culture, Sapienza University of Rome. His academic career spans teaching and research in linguistics, with a focus on historical linguistics, philology, and structuralism. Teaching: He offers courses in Glottology and Linguistics (12 credits) for Master's students, with semesters starting in October and February. Research: His work bridges ancient languages, Iranian studies, and Mediterranean linguistic evolution, emphasizing protohistory of structuralism and cultural exchanges. Recent Publications: His 2023-2025 articles explore topics like the revival of the Pahlavi language, phonetic typology of Mediterranean languages, and the role of historical linguistics in understanding language change. Collaborative projects include ECO4CO , applying predictive algorithms to health emergencies. Academic Service: He contributes to Italian linguistic historiography, notably commemorating scholars like Walter Belardi and Palmira Cipriano. His affiliations include the Società Italiana di Glottologia and Accademia dei Lincei.
Alessandro Fasana is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Polytechnic University of Turin, where he serves as Deputy Coordinator of the College of Mechanical, Aerospace and Automotive Engineering. His academic career spans multiple decades with continuous involvement in doctoral education as Course Coordinator in Mechanical Engineering from 2018-2024 and consistent participation in doctoral colleges since the 27th cycle (2011/2012). Professor Fasana's research focuses on Mechanical Systems Dynamics, Modal Analysis, Nonlinear Dynamics, Rotating Machinery Diagnosis, Structural Health Monitoring, Viscoelastic Materials, Prognostics and Health Management, and Signal Processing. His work bridges fundamental mechanical principles with practical industrial applications, particularly in vibration analysis, structural dynamics, and condition monitoring systems. His research lines specifically include modal analysis and identification of linear dynamic systems, characterization of viscoelastic materials, dynamics of nonlinear systems, and evaluation of defects and residual life of structures. Analysis of his recent publications reveals a strong emphasis on pyroshock testing for aerospace qualification, structural health monitoring, vibration control, and advanced signal processing techniques. His work spans both theoretical developments and practical applications across aerospace, mechanical engineering, and industrial sectors, with particular attention to vibration analysis, structural dynamics, and condition monitoring systems. The research demonstrates increasing integration of computational methods, machine learning, and experimental validation. Professor Fasana has supervised PhD student Luca Viale, whose thesis focused on Pyroshock Testing for aerospace qualification. His research portfolio includes numerous commercial research projects spanning from 2007 to 2023, primarily focused on vibration testing, characterization of damping materials, and structural analysis for diverse industrial applications including aerospace, dental equipment, and power generation systems. He leads the research group 'Dinamica dei sistemi meccanici e identificazione (DIMEAS)' and has been Principal Investigator on multiple commercial research projects related to vibration analysis, structural dynamics, and material characterization. His work connects mechanical engineering fundamentals with practical industrial solutions across multiple sectors including aerospace, automotive, and energy.
Gioacchino Cafiero is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS), Polytechnic University of Turin. His research focuses on data-driven experimental fluid mechanics, particularly applying machine learning techniques like deep reinforcement learning and genetic algorithms to control turbulent flows and optimize fluidic actuators. As a member of the Fluid Dynamics research group, he leads projects such as GREENER (drag reduction via sinusoidal riblets) and WINDED (drone wind investigation), while also directing commercial research on friction stress measurement methodologies. Specializes in turbulent flow control and machine learning applications Teaches PhD courses on Machine Learning for Flow Control Supervises students in aerospace engineering programs Recent publications analyze jet turbulence with explainable AI, heat transfer fluctuations in channel flows, and riblet-induced drag reduction. His work bridges aerospace engineering and fluid dynamics, contributing to SDG goals 9 (Industry Innovation) and 13 (Climate Action). Scientific awards include the Learning to Teach (L2T) Open Badge from Politecnico di Torino.
Stefano Marchesiello is a Full Professor of Applied Mechanics at the Polytechnic University of Turin, Department of Mechanical and Aerospace Engineering (DIMEAS), a position he has held since 2019. His academic work spans theoretical studies, numerical applications, and experimental tests within the field of Applied Mechanics. He maintains active roles in doctoral education, serving on mechanical engineering doctoral colleges from 2013/2014 through 2024/2025, and teaches courses including Dynamics and Identification of Nonlinear Systems, Dynamics of Mechanical Systems, Vibration Mechanics, and Machine Mechanics for Aerospace Engineering. Marchesiello's research focuses on modal analysis and identification, damage diagnosis in structures and construction materials, damping systems, mechanical vibrations, and nonlinear dynamics. His primary research lines include vehicle-bridge dynamic interaction, dynamic identification techniques in linear and nonlinear fields, damage identification, vibrations of continuous systems with non-proportional damping, innovative vibration damping devices, diagnostics and monitoring of rotating systems, and pantograph-catenary dynamic interaction. His work bridges theoretical mechanics with practical engineering applications, particularly in transportation infrastructure and mechanical systems. His recent publications demonstrate a strong focus on nonlinear system identification, structural health monitoring, and vibration analysis across various mechanical and aerospace applications. Marchesiello's research shows increasing integration of machine learning techniques with traditional mechanical engineering approaches, particularly in system identification and damage detection. His work spans from fundamental nonlinear dynamics to practical applications in railway systems, rotating machinery, and structural components. Certificate of reviewing awarded by Journal of Sound and Vibration - Elsevier, Netherlands (2013) Certificate of Excellence in Reviewing - Mechanical Systems and Signal Processing 2013 awarded by Elsevier, Netherlands (2013) Marchesiello serves as Scientific Director for multiple commercial research contracts, particularly with Officina Fratelli Bertolotti SpA, focusing on vibration damping systems for railway catenaries and rotor dynamics modeling. He has led research projects from 2008 through 2023, demonstrating sustained research leadership and industry collaboration. His editorial work includes membership on the Editorial Board of SHOCK AND VIBRATION since 2018, and he has served on program committees for the International Conference on Damage Assessment of Structures (DAMAS) across multiple years. He is actively involved with the Dynamics of Mechanical Systems and Identification research group (DIMEAS), which focuses on developing advanced methods for analyzing and identifying mechanical systems with both linear and nonlinear behaviors. His research integrates computational modeling, experimental validation, and practical applications across multiple engineering domains.
Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.
