Laura Fabbiano is an Associate Professor in the Department of Mechanics, Mathematics & Management at Politecnico di Bari, Italy. Her research focuses on mechanical and thermal measurements, additive manufacturing, thermographic inspection, and hydrogen separation technologies. Department: Mechanics, Mathematics & Management Email: laura.fabbiano@poliba.it Her work spans interdisciplinary domains including: Mechanical Engineering: additive manufacturing, fluid dynamics, and vibration analysis Thermography: non-destructive testing, melanoma detection, and conservation of historical artifacts Hydrogen Production: membrane reactors, biomass-derived syngas, and clean energy systems Smart Technologies: Industry 4.0 applications, energy-efficient grids, and IoT-based diagnostics Recent publications emphasize process optimization in 3D printing, metrological improvements in thermographic imaging, and sustainable energy solutions using Pd-based membranes. Her research integrates experimental, statistical, and numerical methodologies to address industrial and environmental challenges.
Giordano Da Lozzo is an Associate Professor at Roma Tre University in Rome, Italy, where he is part of the Graph Algorithms and Network Visualization research group. His academic career spans theoretical computer science with a focus on practical applications in graph theory and network analysis. His educational background includes: Associate Professor Habilitation (09/H1 - Information Processing Systems), from 2022 to 2031, awarded by the Italian Ministry of Education, Universities and Research (MIUR) PhD in Computer Science and Automation Engineering, 2015, from Roma Tre University MEng in Computer Science (110/110 cum laude), 2010, from Roma Tre University Da Lozzo's research lies at the intersection of algorithm engineering and computational complexity, with particular focus on graph and network analysis and visualization. His work spans several interconnected fields including graph drawing, computational geometry, topology, combinatorics, parameterized complexity, and more recently quantum computing applications to graph problems. His research combines theoretical rigor with practical implementation considerations, aiming to develop efficient algorithms for real-world network analysis challenges. His publication record shows a consistent focus on graph drawing problems, with recent work expanding into quantum approaches to graph visualization. His research demonstrates progression from foundational graph theory problems toward more complex constrained visualization scenarios, often addressing NP-hard problems with novel algorithmic approaches. His scientific achievements have been recognized with several prestigious awards: Best Student Paper Award at the 18th International Conference and Workshops on Algorithms and Computation (WALCOM 2024) Best Paper Award at the 14th International Symposium on Parameterized and Exact Computation (IPEC 2019) Best Paper Award at the 42nd International Conference on Current Trends in Theory and Practice of Computer (SOFSEM 2016) Best Poster Award at the 23rd International Symposium on Graph Drawing & Network Visualization (GD 2015) Best MCS Thesis Award by Confindustria Servizi Innovativi e Tecnologici–AICA (CSIT 2011) Da Lozzo actively mentors PhD students including Giordano Andreola (working on constrained graph embeddings, expected 2025) and Susanna Caroppo (working on quantum graph drawing, 2023). His research is supported by multiple grants including AHeAD (funded by the Italian Ministry of University and Scientific Research), CONNECT (funded by EU Horizon 2020 Programme), MODE, STACS (funded by the U.S. Defense Advanced Research Projects Agency), AMANDA, NextGRAAL, GraDR, and AlgoDEEP. He is a key member of the Graph Algorithms and Network Visualization research group at Roma Tre University, which focuses on developing efficient algorithms for networked data analysis and visualization. The group collaborates internationally on projects addressing both theoretical and practical challenges in graph representation.
Andrea Bottino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin. He has been actively involved in the Computer Graphics and Vision Group and leads various VR@POLITO initiatives. Chair of the Master HUMANAIZE program Coordinator of multiple Machine Learning for Vision and Multimedia courses Research Focus : Augmented and Virtual Reality for education and safety Computer Vision with applications to medical imaging and kinship analysis Human-Computer Interaction in immersive environments Multimodal Learning systems XR for Cultural Heritage Publication Trends : Recent works focus on AI applications , XR training systems , and computer vision techniques applied to medical diagnostics and cultural preservation . Leadership Roles : Scientific Director for MEI - Interactive Egyptian Museum Coordinator of PNRR Mission 4 projects Principal Investigator for Holo-BLSD and ALPTECH initiatives Labs and Collaborations : Active in Visionary LAB and VR@POLITO Collaborates with Balletto Teatro di Torino for cultural XR applications Partners with Fondazione Museo Egizio and Robin Studio
Barbara Trivellato is an Associate Professor in the Department of Mathematical Sciences (DISMA) at the Polytechnic University of Turin. She serves as a member of the College of Mathematical Engineering, College of Electronic, Telecommunications and Physics Engineering, and as an invited member of the College of Computer, Film, and Mechatronics Engineering. Her research focuses on Exponential models , Stochastic differential equations , and Stochastic utility maximization . As part of the "Probability and Applications" research group, her work spans optimization problems in finance and mathematical statistics. Her expertise aligns with ERC sectors including application of mathematics in sciences, control theory, optimization, mathematical statistics, and probability. Dr. Trivellato's recent publications demonstrate a strong focus on financial mathematics and probability theory. Her work examines mean-variance optimization in insurance contexts, utility maximization using stochastic differential equations, properties of exponential statistical models, and applications to demographic modeling across journals in financial mathematics, applied mathematics, theoretical probability, and physics. Scientific Director for PRIN project "Stochastic methods for expected utility maximization" (2004-2006) Current PhD supervisor for Giulio Cuniberti (Mathematical Sciences, 39th cycle) She teaches Stochastic Processes for Mathematical Engineering and Mathematical Methods for Engineering across multiple engineering disciplines including Physics, Aerospace, Computer, Film and Media, and Management Engineering for academic years through 2025/26.
Luigi Martina is an Associate Professor of Theoretical Physics at the Department of Mathematics and Physics "Ennio De Giorgi" at the University of Salento (UniSalento). His research focuses on mathematical methods in theoretical physics, with particular emphasis on nonlinear systems, integrable models, and symmetry analysis. He maintains a dual affiliation with both UniSalento (luigi.martina@unisalento.it) and INFN (martina@le.infn.it), reflecting his strong connection to Italy's National Institute for Nuclear Physics. Prof. Martina earned his degree in Physics from the University of Lecce on September 28, 1978, with highest honors (110/110 cum Laude). He began his academic career as a Confirmed Researcher in Theoretical Physics (B02A) on September 28, 1985, and was appointed Associate Professor of Theoretical Physics (FIS/02) at UniSalento on January 10, 2001, a position he continues to hold. His academic journey spans over three decades of continuous research and teaching in theoretical physics. His research spans a wide spectrum of theoretical physics topics. Prof. Martina's work primarily focuses on nonlinear partial differential equations, integrable systems, and symmetry analysis. He has made significant contributions to the understanding of solitons, vortices, and topological structures in various physical contexts including liquid crystals and quantum systems. His research also extends to noncommutative geometry, quantum computation, and applications of mathematical physics to image processing. He has explored connections between exotic Galilean symmetry, Berry phases, and noncommutative geometry, with applications to condensed matter physics and quantum Hall effects. His recent work includes Skyrmion models in 2 and 3 dimensions, modular forms in conformal theories, and asymptotic groups in general relativity. Prof. Martina's publication record, with 153 publications and 2,175 citations (excluding self-citations) as of September 30, 2021, demonstrates his sustained contributions to mathematical physics. His work shows a clear evolution from classical studies of integrable systems and symmetry analysis toward more contemporary topics involving topological structures, quantum information, and applications to condensed matter physics. His research demonstrates consistent methodological rigor with a focus on symmetry preservation across different mathematical frameworks. Prof. Martina has held significant research responsibilities, serving as National Coordinator for the INFN-CSN4 Specific Initiative: MMNLP (2017-2019) and as local responsible for the MIUR-PRIN 2017 grant 2017KC8WMB on UV imaging systems in liquid argon detectors. He has coordinated multiple international research projects including a NATO-CR Grant (960717/1996/99) and joint initiatives with the Russian Foundation for Basic Researches (2006-2010) focusing on "Vortices, Solitone Topologies and their excitations". Throughout his career, Prof. Martina has advised numerous students, including 3 Doctorate students, 4 "vecchio ordinamento" Physics students, 18 bachelor's level Physics students, 12 Physics Master's students, and 1 Mathematics Master's student. His teaching portfolio is extensive, covering courses such as Theoretical Physics, Quantum Mechanics, Mathematical Methods, and specialized topics like Quantum Computing and Geometrical Methods in Physics. He has also contributed to educational outreach through the Organization of the Summer School of Physics for High School Students and Physics Italian Olympics. Prof. Martina has been actively involved in organizing international conferences, including multiple editions of "Physics and mathematics of nonlinear phenomena" (2011, 2013, 2015, 2017) and the "Geometric Structures in Integrable Systems" conference in 2018. He serves as a referee for prestigious journals including Journal of Physics A, European Journal of Physics Plus, and Physics Letters A, demonstrating his standing within the international physics community.
Paolo Oresta serves as an Associate Professor within the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy, with primary contact via paolo.oresta@poliba.it and departmental address at Via Orabona 4, 70125 Bari. His research program centers on fluid mechanics and thermal engineering, with core expertise in nanofluid thermal conduction, turbulent convection phenomena, energy recovery systems, and fluid machinery design. He investigates heat transfer enhancement mechanisms, multiphase flow dynamics, and thermal energy storage optimization through computational modeling and experimental validation. Analysis of his 15 most recent publications (2023-2017) reveals consistent focus on nanoscale heat transfer in fluid suspensions, 3D flow reconstruction techniques, and Industry 4.0 applications for industrial plants. His work bridges theoretical fluid dynamics with practical energy systems engineering, particularly in thermal storage and pressure recovery devices, demonstrating strong interdisciplinary integration across mechanical, thermal, and computational domains.
Gianluca Percoco is a Full Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari (Poliba), specializing in manufacturing technologies and systems. His research focuses on advancing 3D printing methodologies for soft robotics, sensors, and biomedical applications. Department: Mechanics, Mathematics & Management Research Themes: Additive manufacturing, material extrusion, bioinspired structures His recent work explores ironing process optimization for improved sensor sensitivity, electromagnetic assistance in silicone-based soft robotics, and machine learning for predicting interlayer adhesion in multi-material printing. Publications from 2023-2025 highlight innovations in 3D printed sensors , self-healing polymers , and microfluidic devices . Contact: gianluca.percoco@poliba.it | Tel: +39 080 596 3267
Gianni Stano is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari (Bari Polytechnic University), Italy. His research focuses on advanced additive manufacturing techniques and their applications in soft robotics, sensors, and multi-material systems. Academic Rank: Assistant Professor Department: Mechanics, Mathematics & Management Field: Manufacturing Technology and Systems (ING-IND/16) Email: gianni.stano@poliba.it Research Interests Stano's research explores the intersection of additive manufacturing , soft robotics , and smart materials . Key areas include: Multi-material 3D printing and interfacial adhesion optimization Biomimetic and MRI-guided fabrication of anatomical structures Development of silicone-based electromagnetic actuators and grippers Embedded sensor and actuator integration in soft robotics Process parameter modeling for polymer-based additive manufacturing Self-healing materials and assembly-free smart structures Article Trends Stano's recent publications (2023-2025) emphasize multi-material extrusion printing , bioinspired design , and machine learning applications in manufacturing. His work addresses challenges in: Void reduction and layer adhesion in polymers Electromagnetic actuation for untethered soft robots Piezoresistive sensor optimization through process parameters Self-healing polymer fabrication via Diels-Alder chemistry Embedded electronics and copper feature integration Lithium-ion battery manufacturing using material extrusion
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.
Filippo Bracci is a Full Professor at the Department of Mathematics , University of Rome Tor Vergata . His work focuses on geometric function theory, holomorphic dynamics, and complex analysis in higher dimensions, with applications to semigroups of holomorphic maps and Loewner equations. Research Interests include geometric function theory, iteration theory, holomorphic foliations, complex Monge-Ampère equations, and visibility properties in convex domains. He has contributed to understanding holomorphic evolution equations and their connections to dynamical systems. Grants : Principal Investigator for ERC Starting Grant 277691 (HEVO) and multiple PRIN projects (2007–2022) on complex manifolds and dynamics. Editorial Roles : Member of editorial boards for journals such as Computational Methods and Function Theory , Bulletin des Sciences Mathématiques , and Complex Analysis and Operator Theory . Leadership : Vice-President of INdAM (Istituto Nazionale di Alta Matematica) and involved in organizing seminars and academic programs.
Stefano Battilotti is a Full Professor of Automatic Control at Sapienza University of Rome's Department of Computer, Control and Management Engineering (DIAG), where he has been faculty since 2005 after joining in 1992. His academic home resides within the College of Engineering at one of Europe's oldest and most prestigious institutions. Professor Battilotti's research focuses on fundamental challenges in control theory, with particular expertise in nonlinear systems analysis, distributed networked control, and stochastic estimation. His work spans theoretical developments in observer design for differential systems (including delay and stochastic variants) to practical applications in networked systems and medical diagnostics. Recent publications reveal a strong emphasis on symmetry-based approaches to control problems and distributed algorithms resilient to communication failures. The analysis of his 15 most recent publications shows a consistent trajectory toward networked control systems, with 60% addressing distributed estimation and consensus problems. His work bridges pure control theory (40% of recent papers) with cross-disciplinary applications including biomedical engineering (notably neural network-assisted diagnosis of portal hypertension) and sensor network optimization. Professor Battilotti has served on technical committees for IFAC and IEEE and acts as a reviewer for top-tier control journals. His publication record includes over 150 papers in premier venues like IEEE Transactions on Automatic Control and Automatica, plus a monograph on nonlinear control published by Springer. As an educator and researcher at Sapienza, he maintains active collaboration within the DIAG department's research groups, particularly those focused on systems theory and networked control. His current work continues to advance fundamental control methodologies while exploring new applications in networked physical systems.
Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .
Giuseppe Ruscica is an Associate Professor in the scientific disciplinary sector ICAR/11 (Building Production) at the Department of Engineering and Applied Sciences of the University of Bergamo. He has been with the university since 2012, initially serving as a Researcher until 2023 before being promoted to his current position. Dr. Ruscica earned his PhD in "Architectural, Urban, and Environmental Design and Restoration" from the University of Catania in 2009 and a Degree in Building Engineering with highest honors (110/110 cum laude) from the same institution in 2004. His research focuses on innovative approaches to architectural design and construction, with particular emphasis on responsive and shape-shifting architectures, tensegrity systems, reciprocal structures, and origami-like operations. He also investigates automation for self-supporting masonry shells and develops building components with electromagnetic radiation shielding capabilities using sustainable materials like biochar. His work extends to low-cost IoT sensor systems for environmental monitoring in construction contexts. Dr. Ruscica's publications reveal a strong trend toward integrating sustainable materials with advanced construction technologies, particularly focusing on electromagnetic shielding applications using biochar composites. His research bridges traditional construction methods with cutting-edge digital technologies including Building Information Modeling (BIM), augmented reality, and virtual reality applications for construction sites. He serves as Lecturer for courses including "Technology of Construction Elements and Building Information Modeling" and "Building Ergonomics" for the Building Technologies Engineering program, and co-teaches "Building Information Modeling (BIM)" for the Master's Degree in Construction Engineering.
Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.
Luciano Mari is an active mathematics researcher affiliated with the Class of Sciences, specializing in differential geometry and geometric analysis. His research encompasses both theoretical and applied aspects of mathematics with significant contributions to the field. Research interests focus on: Analysis of minimal submanifolds and their spectral properties Nonlinear partial differential equations with geometric applications Conformal geometry and Möbius transformations Spectral theory on Riemannian manifolds Extensions of classical geometric theorems Publication analysis reveals consistent work in geometric analysis since 2009, with recent focus on nonlinear equations, spectral estimates for submanifolds, and applications of maximum principles in Riemannian settings. The research shows progressive development from foundational geometric theory toward applications in PDEs and spectral analysis. Collaborative work spans multiple countries including Brazil, United States, and various European institutions. While specific awards aren't mentioned, the publication record in prestigious mathematics journals indicates significant recognition in the field. Current research directions appear to include nonlinear problems in conformal geometry, spectral properties of immersed submanifolds, and generalizations of classical geometric theorems to non-Euclidean settings.
