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Giorgio Grisetti is a Full Professor at Sapienza University of Rome within the Department of Systems and Computer Science, maintaining active research roles in the RoCoCo lab at Sapienza since November 2010 and the Autonomous Intelligent Systems Lab at Freiburg University where he previously served as a Post Doc under Wolfram Burgard starting in 2006. His educational background includes a M.Sc. in Computer Engineering from the University of Rome (2001) and a Ph.D. from Sapienza University of Rome's Intelligent Systems Lab (2006), supervised by Daniele Nardi. His doctoral thesis focused on SLAM using Rao-Blackwellized particle filters. Dr. Grisetti's research centers on mobile robotics with emphasis on robust solutions for autonomous navigation systems. His work spans theoretical and practical advancements in Simultaneous Localization and Mapping (SLAM), robot localization, path planning, and sensor fusion, particularly leveraging LiDAR and multi-sensor configurations. Recent publications demonstrate strong focus on optimization techniques, sensor calibration, and real-time performance for autonomous systems operating in complex environments. His publication trends reveal deep specialization in LiDAR-based SLAM (7 of 15 recent articles), bundle adjustment methods (4 articles), and sensor calibration/perception (3 articles), with consistent contributions to top robotics venues like IEEE Robotics and Automation Letters and ICRA. Key recognitions include: Nomination for the best IROS paper award (2010) Open Source achievement award from Willow Garage (2010) Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles (2010) Best Paper award at ICRA 2009 (2009) His research is conducted through the RoCoCo lab at Sapienza University of Rome and the Autonomous Intelligent Systems Lab at Freiburg University, focusing on developing foundational algorithms for mobile robot autonomy. Current projects emphasize robust perception systems, optimization frameworks for sensor fusion, and practical implementations for real-world navigation challenges.

• Mobile Robotics
• SLAM
• Robot Localization
• +3 more

Vincent Lepetit currently serves as a Director of Research at École des Ponts ParisTech since 2019. His prior academic appointments include: Full Professor at the Institute for Computer Graphics and Vision, Graz University of Technology, Austria Senior Researcher at the Computer Vision Laboratory (CVLab) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland His research resides at the critical intersection of Machine Learning and 3D Computer Vision, with concentrated efforts on advancing 3D scene understanding from images. This work integrates deep learning methodologies with geometric computer vision to solve complex perception challenges, driving innovations in computational intelligence and visual recognition systems. His expertise spans both theoretical foundations and practical implementations across computer vision subdomains. Prof. Lepetit maintains significant leadership roles within the computer vision community, consistently serving as area chair for premier conferences including CVPR, ICCV, and ECCV. He further contributes through editorial positions as associate editor for top-tier journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), International Journal of Computer Vision (IJCV), and Computer Vision and Image Understanding (CVIU), shaping research standards across the field. No scientific awards or honors were explicitly documented in the provided source material. While the text confirms his past affiliation with EPFL's Computer Vision Laboratory (CVLab), current laboratory or team leadership details remain unspecified. Information regarding doctoral advisees, research grants, or funding mechanisms was not included in the available documentation, though his editorial and conference leadership roles indicate substantial research influence.

• Computer Vision
• 3D Computer Vision
• Machine Learning
• +3 more

Antonia Teresa Spano is a Full Professor at the Department of Architecture and Design (DAD) of Politecnico di Torino, Italy. She serves as Vice Coordinator of the PhD Program in Architectural Heritage and is a member of the Future Urban Legacy Lab (FULL). Her research focuses on geomatics, 3D modeling, and digital documentation for cultural heritage preservation. University: Politecnico di Torino Department: Department of Architecture and Design (DAD) Spano's work integrates advanced geomatic techniques like LiDAR, UAV photogrammetry, and SLAM-based modeling to address challenges in architectural and archaeological heritage documentation. Her expertise spans GIS, digital photogrammetry, and machine learning applications in conservation processes. Her recent publications emphasize multi-sensor 3D surveys for Pier Luigi Nervi's structures, AI-driven decay detection, and semantic classification of LiDAR data. These works align with SDG goals for sustainable cities and quality education. Scientific Awards: Premio Giovani CNR (2005) Miglior Poster at GISTAM (2016) and GEORES (2019) Fellow of CIPA Heritage Documentation (2023-2033), CIRAAS ETS (2020-2030), and ISPRS (2017-2024) Spano supervises PhD students in architectural heritage programs and leads numerous research projects, including collaborations with institutions like the Pompeii Archaeological Park, CRAST, and international universities. She has contributed to editorial boards and program committees of journals and conferences such as Virtual Archaeology Review and ISPRS symposiums.

• Geomatics
• Cultural Heritage Documentation
• 3D Modeling
• +5 more

Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.

• Applied Mathematics
• Image Processing
• Shape Optimization
• +5 more

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +5 more

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.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +9 more

Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Umberto Castellani is a Full Professor at the Department of Computer Science, University of Verona. His research spans computer vision, computer graphics, and machine learning, with applications in biomedical analysis and cultural industries. Research Focus 3D acquisition and registration Geometric deep learning and spectral methods Neuroimaging (diffusion MRI, fNIRS, EEG) Augmented/virtual reality and human-machine interaction Key Projects INDICE - Digital Innovations for Cultural Industries (2025) VVV - Virtual simulation of natural ecosystems (2023) SARAS - Robotic surgical assistant (2018) Collaborations with game industry and biomedical companies Laboratory : Member of Vision, Image Processing & Sound (VIPS) Lab.

• Computer Vision
• Computer Graphics
• Machine Learning
• +5 more

Riccardo Zaccone is a Ph.D. candidate in Computer and Systems Engineering at Politecnico di Torino’s Department of Control and Computer Science (DAUIN), specializing in machine learning and computer vision. He also serves as an external lecturer and teaching assistant, contributing to courses in Machine Learning, Deep Learning, and Algorithms and Data Structures for Computer Science Engineering. Education : Ph.D. in Computer and Systems Engineering (2022–2025), Politecnico di Torino. His research focuses on Federated Learning and Visual Place Recognition, addressing challenges in communication efficiency, distributed training, and cross-domain adaptation. Recent work includes Communication-efficient Federated Learning with Heavy-Ball Momentum (2025) MeshVPR for 3D-based Urban Navigation (2025) Distributed CosPlace Training for Large-Scale Localization (2024) He collaborates with the VANDAL laboratory on multimodal learning applications and has co-authored publications in top-tier venues like ECCV, Frontiers in Robotics and AI, and ICPR.

• Machine Learning
• Computer Vision
• Federated Learning
• +3 more

Davide Bacciu is a Full Professor of Machine Learning at the University of Pisa, where he leads the Pervasive Artificial Intelligence Laboratory (PAILab) and coordinates the EIC-Pathfinder EMERGE project. He is also Director of AI Research at Aptus.AI and founder of ContinualIST and QuantaBrain startups. His academic roles include Vice President of AIxIA (Italian Association for Artificial Intelligence) and former Senior Editor of IEEE Transactions on Neural Networks and Learning Systems. He holds a PhD in Computer Science from IMT Lucca and is affiliated with the Department of Computer Science at the University of Pisa. His research focuses on machine learning fundamentals, graph neural networks, continual learning, and applications in robotics and healthcare. Key projects include the EMERGE initiative exploring collective awareness in AI systems and QuantaBrain’s diagnostic tools for autism spectrum disorders. Bacciu’s work emphasizes interdisciplinary collaboration, integrating AI with cyber-physical systems and societal impact. His contributions span foundational theory (e.g., causal abstractions, graph structure learning) and applied domains (e.g., autonomous systems, medical AI). He has organized workshops on AI for Space exploration and Geometric Deep Learning, showcasing his commitment to advancing AI frontiers. Education: PhD in Computer Science (IMT Lucca), MSc & BSc from University of Pisa. Labs/Teams: PAILab, EMERGE project, QuantaBrain startup. Grants/Projects: EIC Pathfinder EMERGE, TEACHING_H2020, AI for Space initiatives. His recent publications address challenges in dynamic graph learning, continual learning, and causal discovery. Bacciu advocates for a national AI strategy in Italy, stressing long-term vision and ethical AI governance aligned with EU regulations like the AI Act.

• Machine Learning
• Graph Neural Networks
• Continual Learning
• +5 more

Guido Lombardi is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin, where he serves as a member of the Interdepartmental Center CARS@PoliTO - Center for Automotive Research and Sustainable Mobility. His academic career spans over two decades with significant contributions to computational electromagnetics and related fields. Dr. Lombardi's research focuses on computational electromagnetics, particularly analytical and numerical methods, electromagnetic propagation (scattering and diffraction), metamaterials, wave propagation, and the Wiener-Hopf technique. His work extends to practical applications including microgrid/technological modernization of eco-districts, triboelectricity in industrial applications, and security and science for peace initiatives. His research aligns with several Sustainable Development Goals, particularly those related to affordable and clean energy, industry innovation, and sustainable cities. His publication record demonstrates a strong focus on electromagnetic theory, with numerous papers in IEEE Transactions on Antennas and Propagation and other prestigious journals. The research trajectory shows consistent development in solving complex electromagnetic problems using advanced analytical and numerical techniques, particularly the Wiener-Hopf method applied to wedge diffraction problems, waveguide analysis, and metamaterial applications. Notable scientific awards and recognitions include: Raj Mittra Junior Researcher Award from IEEE Antennas and Propagation Society (2003) Executive Committee member of IEEE Antennas and Propagation Society (2016-2021) Honorary member of URSI (2018-) Honorary member of IEEE (2011-) Dr. Lombardi has served as a mentor to PhD students including Matteo Perrone (working on multiphysics acoustic-electromagnetic metasurfaces) and Sergio Cannata (focusing on biological image analysis through deep learning techniques). He has secured significant research funding through competitive calls including the PNRR Mission 4 HPC Spoke 6 project (2022-2025) and the GREEN TAGS project (2020-2024). His editorial service includes roles on IEEE Transactions on Antennas and Propagation, Electronics Letters, and IEEE Access. He has been actively involved in organizing major international conferences, serving as program chair for multiple IEEE-APS Topical Conferences on Antennas and Propagation in Wireless Communications and the International Conference on Electromagnetics in Advanced Applications (ICEAA) from 2001 to present. His research group, Electromagnetic Modeling and Applications, focuses on advanced numerical methods for electromagnetic problems.

• Computational electromagnetics
• Electromagnetic propagation
• Wave propagation
• +7 more

Giulia Michieletto serves as an Assistant Professor (RTD-A) at the Department of Management and Engineering, University of Padova, where she leads research in networked control systems and multi-agent robotics. She is a core member of the SPARCS research team and holds the position of publicity and dissemination lead for the IEEE Robotics and Automation Society's Technical Committee on Multi-Robot Systems. Her academic foundation includes a Laurea Degree in Automation Engineering (2014) and a Ph.D. in Information Engineering (2018), both from the University of Padova, supplemented by a Visiting Researcher appointment at LAAS-CNRS (2016-2017) funded by the Eiffel Scholarship Program. Michieletto's research centers on multi-agent system modeling with specialized focus on aerial vehicle formations and nanosatellite coordination . She pioneers thrust-vectoring aerial platform design for enhanced environmental interaction, while advancing theoretical frameworks in bearing rigidity theory. Her work spans wireless sensor networks for environmental monitoring, multi-camera systems for cooperative tracking, and ground robotics for autonomous manipulation, addressing critical challenges in robustness, security, and distributed control. Analysis of her publication trajectory reveals deep integration of bearing rigidity theory with practical aerial robotics applications, evolving toward cybersecurity-enhanced navigation and distributed estimation for sensor networks. Recent work demonstrates increasing sophistication in handling GNSS spoofing attacks, multi-robot formation control under disturbances, and real-time surveillance systems. Her scientific recognition includes: Eiffel Scholarship Program of Excellence (2016) Finalist for IEEE RAS Italian Chapter Fabrizio Flacco Young Author Best Paper Award (2019) As an active educator, Michieletto supervises master's theses on contact-aware aerial robotics and mobile manipulation while securing research leadership as Principal Investigator for the Contact-Aware Robotics (CAR) project and co-PI for formation control research. Her grant portfolio spans national initiatives including TSTARK and INTERACT, plus EU H2020's AEROARMS, frequently as fellowship recipient. She drives innovation within the SPARCS research ecosystem, focusing on networked control solutions for complex robotic systems operating in constrained environments.

• Multi-agent Systems
• Aerial Robotics
• Control Theory
• +4 more

Gabriele Berton is a postdoctoral researcher at Amazon, focusing on Multimodal Large Language Models and advanced computer vision applications. He holds a PhD in Computer Vision from the Polytechnic of Turin under Prof. Barbara Caputo. His research expertise includes visual geo-localization, place recognition, and scalable image retrieval systems. He previously worked at the Italian Institute of Technology (IIT) and was a visiting scholar at Carnegie Mellon University's AirLab under Prof. Sebastian Scherer. His work on EarthLoc is utilized by NASA for localizing International Space Station imagery. Education: PhD in Computer Vision, Polytechnic of Turin (2022) Master’s/Pre-PhD work at Italian Institute of Technology Research Focus: Berton’s work emphasizes robust visual localization systems, including 3D mesh-based place recognition (MeshVPR), astronaut photography indexing (EarthLoc), and iterative coregistration techniques (EarthMatch). He develops scalable algorithms for large-scale applications, balancing performance with computational efficiency. Key Achievements: First-author papers at CVPR, ICCV, ECCV (7+) First Prize in Best Student Research at EuroCon 2023 Co-developed CosPlace framework for large-scale visual localization Labs/Teams: Collaborates with AirLab at CMU and MaLGa Lab. Active contributor to the RoboTo robotics team, focusing on computer vision for robotics competitions.

• Computer Vision
• Visual Geo-localization
• Robotics
• +3 more

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +9 more

Claudia Califano is an Associate Professor at the Department of Computer, Control and Management Engineering, University of Rome "La Sapienza". Her work focuses on nonlinear systems, time-delay systems, and digital control with applications in biomedical systems and multi-agent networks. She has held editorial roles at the IMA Mathematical Control and Information Journal and IFAC World Congress, and served as Vice Chair for Education in IFAC Technical Committee Nonlinear Systems. B.Sc. and Ph.D. in Systems Engineering from University of Rome "La Sapienza" Research Interests: Her research spans geometric control theory for nonlinear systems, dynamic feedback linearization, and time-delay systems. Recent work includes deep learning applications for non-invasive medical diagnostics and consensus protocols for multi-agent systems with communication delays. Scientific Contributions: Her publications emphasize geometric methods for nonlinear control, integrability conditions, and observer design. Key themes include accessibility-observability duality, canonical forms, and applications to diabetic gastroparesis modeling. Best Paper Presentation Award at ACC 2010 Finalist for Best Poster at IFAC'96 Senior Member of Control Systems Society Editorial board member for IEEE and IFAC Teaching Roles: • Teaching Coordinator of Master's Degree in Emergency Engineering (2001-2013) • Courses taught: Systems Theory, Fundamentals of Automatic Control, Digital Control (in English)

• Nonlinear Systems Analysis
• Time-Delay Systems
• Digital Control
• +5 more