Giusy Macrina is a Researcher at the Department of Mechanical, Energy and Management Engineering (University of Calabria, Italy). Her work focuses on Operations Research and Machine Learning applications to complex logistics and transportation problems. Specializes in Vehicle Routing Problems with drones and crowd-shipping Develops hybrid algorithms combining mathematical optimization and artificial intelligence Active in green logistics and sustainable transport systems Collaborates with international institutions like Amazon Her research spans smart mobility , energy-efficient delivery systems , and IoT localization challenges . Recent publications demonstrate her focus on integrating machine learning into traditional optimization problems . She teaches courses in Management Engineering , including Methods and Tools for Engineering and Production Management and Control at the graduate level. Her work addresses both static and dynamic optimization challenges in logistics and energy systems.
Alessandro Tasora is a Full Professor at the University of Parma in the Department of Industrial Engineering and Department of Engineering and Architecture . He directs the Digital Dynamics Lab and serves as Scientific Director of the Smart Production Lab 4.0 . His work spans theoretical mechanics, robotics, and high-performance computing. Director, Digital Dynamics Lab (2019–present) Scientific Director, Smart Production Lab 4.0 (2017–present) National Scientific Qualification for Full Professor (2016) Honorary Associate, University of Wisconsin-Madison (2009–present) Research focuses on non-smooth multibody dynamics , GPU-accelerated simulation , and industrial robotics . He developed the Chrono::Engine software for multibody physics and HyperOCTANT for NLCP problems. His work addresses granular flows in nuclear reactors, vehicle mobility on deformable terrain, and historical structural analysis. Key article trends include GPU-based HPC for large-scale simulations, cone complementarity in contact dynamics, and isogeometric beam formulations for flexible bodies. Collaborations with Argonne National Laboratory and Fraunhofer ITWM highlight his international impact. Top-SNIP Paper (2017) International CAE Conference Poster Award (2015) Best Paper, Asian Conference on Multibody Dynamics (2010) TOP4 Paper, RAAD Robotics Workshop (2011) He has supervised over 40 theses in automation, tribology, and robotics. Grants include FFABR-MIUR , US Army RIF , and CNR projects . Projects involve seismic protection, autonomous AGVs, and Industry 4.0 consulting.
Flavio SARTORETTO is an Associate Professor in Scientific Computing at Ca' Foscari University of Venice. He holds a Mathematics degree from the University of Padua and has held academic positions at University of Padua (1982-1992) and Sapienza University of Rome (1992-1993) before joining Ca' Foscari in 1993. His research focuses on numerical analysis, computational methods, and interdisciplinary applications including environmental modeling, cognitive processes, and assistive technologies. Key research areas include numerical solutions of PDEs, meshless methods, EEG signal analysis, and e-learning tools for impaired individuals. He has participated in major research projects such as EC Network (1992-1995), PRIN initiatives (1997-2010), and contributed to software development for geomechanical models and air quality systems. His work spans computational fluid dynamics, robotics applications, and cognitive studies. Recent publications highlight advancements in mesh refinement strategies, robotic assistive devices, and spatial cognition research. He has reviewed for prestigious journals and served in academic committees for state examinations and international conferences. He maintains active involvement in academic service, including roles in evaluation committees and contributions to professional societies like SIAM and CICAP.
Marta Lazzaretti is a Research Fellow at the Department of Mathematics (DIMA) of the University of Genoa. Her work focuses on inverse problems in imaging, numerical analysis, and optimization algorithms in non-standard functional spaces. Affiliation: Department of Mathematics, University of Genoa Academic Rank: Research Fellow Research Interests: Specializing in regularization techniques and numerical optimization, her research spans: Off-the-grid methods for Poisson inverse problems Banach space formulations for geophysical data inversion Stochastic gradient descent in variable exponent Lebesgue spaces Dual descent regularization algorithms Publication Trends: Recent work emphasizes non-Hilbertian optimization frameworks (2023-2025), combining stochastic methods with deterministic regularization for imaging and subsoil inversion applications. Collaborations include Claudio Estatico, Luca Calatroni, and Giuseppe Rodriguez.
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.
Luca Barbierato is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, specializing in applied artificial intelligence, cybersecurity, and co-simulation infrastructures for integrated energy systems. He actively contributes to research in edge computing, IoT, and sustainable energy technologies. Research Focus: AI applications in energy systems, secure IoT infrastructures, and co-simulation frameworks Teaching Roles: Invited PhD teaching component (2025/26), teaching assistant across multiple programs His recent publications address critical areas including OpenTitan-based security controllers, urban building energy modeling, and distributed power system co-simulation. Barbierato's work aligns with SDG Goals 7 (Affordable Energy), 9 (Innovation Infrastructure), and 11 (Sustainable Cities). Notable scientific recognition includes the Learning to Teach (L2T) 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.
Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.
Stefano Lucidi is a Full Professor of Operations Research at Sapienza University of Rome, where he is affiliated with the Department of Computer, Automatic and Management Engineering within the Faculty of Information Engineering, Computer Science and Statistics. He has held this position since November 1, 2000, after serving as Associate Professor from November 1, 1992 to October 31, 2000. He was coordinator of the PhD program in Operations Research from 2010 to 2012 and was a shareholder of the university spin-off ACTOR SRL until July 2024. His research interests span Nonlinear Optimization, Derivative-Free Optimization, Mixed Integer Programming, and Global Optimization. His methodological work focuses on unconstrained optimization methods, constrained optimization methods, non-differentiable optimization methods, derivative-free methods, and global optimization techniques. His applied research includes mathematical modeling of biological phenomena in cell kinetics, identification of astrophysical parameters, optimal management of bookable seats in rail transport, and optimal design of electromagnetic devices and industrial electric motors. His recent publications demonstrate a strong focus on derivative-free optimization methods, complexity analysis of algorithms, multi-objective optimization, and applications in diverse fields including healthcare, transportation, and neuroscience. His work bridges theoretical advances in optimization with practical applications across multiple domains. His significant contributions to the field include developing algorithms for simulation-driven design optimization, addressing nonsmooth optimization problems, and creating methods for multi-fidelity computations. His research has been published in top journals including Optimization Methods & Software, Journal of Optimization Theory and Applications, and Optimization Letters. Full Professor of Operations Research since 2000 PhD program coordinator (2010-2012) Shareholder of ACTOR SRL spin-off (2011-2024) Active contributor to optimization theory and applications Professor Lucidi has been instrumental in advancing optimization methodologies while maintaining strong connections to practical applications across various industries. His work continues to influence both theoretical developments and real-world implementations of optimization techniques.
Roberto Vezzani is an Associate Professor at the University of Modena and Reggio Emilia's Enzo Ferrari Department of Engineering, specializing in information processing systems (ING-INF/05). Previously Director of the Artificial Intelligence Research and Innovation Center (2018-2021), he holds a PhD in Information Engineering from the same institution. As senior member of AimageLab, he coordinates research on human-computer interaction using multi-sensor systems. His research spans: Computer vision for IoT and video surveillance Motion detection and action classification 3D vision with depth/thermal/event cameras Sensor fusion and automatic video annotation He leads competitive projects funded by Toyota, Ferrari, and EU programs, focusing on industrial applications of computer vision. Recent publications (2024-2025) demonstrate strong focus on 3D pose estimation, robot perception, and efficient embedded vision systems, with applications in automotive, robotics, and UAVs. Awards include: Best Paper - ICPR 2020 (IAPR) Best Paper - VISAPP 2020 Best Paper - THEMIS'2008 Industrial collaborations feature multi-year projects with Ferrari (RedVision lab), Toyota Europe, and Tetra Pak. Teaching includes courses on Computer Architecture, IoT systems, and industrial AI.
