Federica Tomao is an Associate Professor at the Department of Maternal, Child and Urological Sciences within Sapienza University of Rome. She actively teaches and supervises courses in Medicine and Surgery , Nursing , and Midwifery programs across multiple institutions. Current faculty member with academic rank Teaching roles in 6th, 3rd, and 2nd year courses Specialized in gynecologic oncology Research Focus : Her work spans ovarian cancer, breast cancer, and endometrial cancer, emphasizing chemotherapy optimization, precision medicine, and radiomics. Notably, she contributes to understanding PARP inhibitors, immune checkpoint therapies, and fertility preservation techniques in cancer survivors. Recent Publications highlight advancements in sarcopenia analysis, BRCA testing, and radiogenomic nomograms. These studies demonstrate her commitment to bridging imaging and molecular data for improved cancer management. Email : federica.tomao@uniroma1.it
Rita Cucchiara is a Full Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. She leads the AImageLab research laboratory, part of the Artificial Intelligence Research and Innovation Center (AIRI) in Modena. Her research focuses on Computer Vision, Pattern Recognition, Machine Learning, and Multimedia, with applications in video surveillance, medical imaging, human-centered AI, and generative models. She is actively involved in interdisciplinary projects like ELIAS (European Lighthouse for AI Sustainability) and ELSA (European Lighthouse on Secure AI). Her recent roles include being elected Rector of the University of Modena and Reggio Emilia in 2025. She has organized and participated in major AI events, including workshops at NeurIPS, CVPR, and ECCV, and has contributed to advancements in multimodal models, deepfake detection, and trustworthy AI. Education details are not explicitly provided, but her extensive academic and research experience at the University of Modena underscores her expertise. She collaborates with institutions like NVIDIA, CINECA, and industry partners such as Digital Design and NVIDIA's AI Technology Center. AImageLab's projects include developing systems for medical imaging, ethical AI, and generative adversarial networks (GANs) for design surfaces. She co-organizes initiatives like the ELLIS Summer School on Large-Scale AI and contributes to policy discussions on AI ethics and societal impact. Her work spans from foundational research (e.g., vision transformers, continual learning) to applied projects (e.g., DDGan system for surface printing). Key grants and collaborations include the FAIR project and PNRR-M4C2 initiatives. She advises students and researchers in AI, with 7 PhD positions funded under national programs. Her leadership roles in AIRI and AImageLab highlight her commitment to bridging academia and industry, fostering innovation in AI-driven solutions for sustainability and healthcare.
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.
Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
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.
Benedetta Giovanola is a Full Professor at the University of Macerata (UniMC) in the Department of Political Sciences, Communication and International Relations. She holds the Jean Monnet Chair 'Ethics for Inclusive Digital Europe' (funded by the EU) and is a Visiting Professor at Tufts University, USA. She also serves as Adjunct Professor of 'Economic Philosophy and Business Ethics' at the Catholic University of Milan. Her roles include Director of the postgraduate course on 'Digital Ethics, Law, Technologies' and member of the executive board of the 'Cybersecurity, Cyber Risk and Data Protection' program, both jointly run with the Polytechnic University of Marche. Current roles: Jean Monnet Chair, Full Professor, Visiting Professor at Tufts, Adjunct Professor at Catholic University Milan. Leadership: Founder of GAIA (AI Ethics and Governance spin-off), Principal Investigator (PI) in EU-funded projects like EthicAI4CARE and REINITIALISE. Professional memberships: Member of the executive board of SIFM (Italian Association for Moral Philosophy), past Deputy Rector at UniMC (2016-2022). Research focuses on digital ethics, AI ethics, media ethics, public ethics, social and global justice. She leads projects such as ENDE (European Network on Digitalization and E-Governance) and publishes extensively on AI ethics in healthcare, cultural heritage, and business contexts. Her work emphasizes ethical frameworks for trustworthy AI and fairness in algorithmic systems. Awards and recognition include the Jean Monnet Chair, and her research has been featured in journals like AI & Society, Computer Vision and Image Understanding, and IEEE Transactions on Technology and Society. She also contributes to policy initiatives and ethical guidelines for emerging technologies.
Giuseppe Bruno Averta is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Polytechnic University of Turin, and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is affiliated with the College of Computer, Film and Mechatronics Engineering and contributes to national and international research in artificial intelligence and robotics. Averta has held a Visiting Researcher position at the Massachusetts Institute of Technology (MIT) from January to June 2019. His research interests include Computer Vision, Deep Learning, Robotics, Neural Architecture Search, Egocentric Vision, Embodied Intelligence (Edge/Tiny ML), and Human-Robot Collaboration . His work is aligned with ERC sectors in Artificial Intelligence, Machine Learning, and Robotics, and contributes to UN SDGs such as Good Health and Well-being, Industry Innovation and Infrastructure, and Responsible Consumption and Production. The recent publication trends highlight his focus on vision-language models (e.g., CLIP), egocentric action recognition, efficient neural architectures (e.g., BiSeNet, MaskFormer), and robust deep learning. His research bridges theoretical advances with practical robotics applications, including grasping and manipulation. Scientific Awards and Recognitions: Georges Giralt PhD Award (euRobotics AISBL, 2021) Wiley Best Reviewer (Wiley, Italy, 2021) Best Paper Award, ICUMT 2015 (2017) Fellow, ELLIS Network of Excellence (2022–) Fellow, DAAD AInet (2022–) DAAD AInet Fellowship Advising and Grants : Averta supervises multiple PhD students in the Artificial Intelligence and Computer and Systems Engineering doctoral programs at Politecnico di Torino. He is involved in teaching at both the master’s and doctoral levels, including courses on Robot Learning and Machine Learning and Deep Learning. He is also a co-inventor on a national and international patent for a method and algorithm for the automatic design of neural networks through machine learning, indicating active research funding and innovation. Labs and Research Groups : He is a member of the SmartData@PoliTO center and contributes to research in the VANDAL PoliTO lab (as indicated by his student Davide Buoso). His work is deeply integrated with teams working on egocentric vision, embodied AI, and neural architecture search.
Federica Spagnoli is an Associate Professor of Phoenician and Punic Archaeology at the Department of the Italian Institute of Oriental Studies (ISO), Faculty of Letters, Sapienza University of Rome. She teaches courses including "Archeologia fenicio-punica e del Mediterraneo," "The Phoenicians in Central Mediterranean," and "Pre-Classical Levant and Mediterranean." Her office is located in Building CU003, second floor, room 107 at Piazza Aldo Moro 5, Rome. Dr. Spagnoli obtained her PhD in Near Eastern Archaeology in 2008 under the supervision of Professors P. Matthiae and L. Nigro. She has been a member of the Rome "La Sapienza" Expedition to Motya since 2002, serving as supervisor of the ceramic Laboratory. Currently, she holds the position of RTDB in Phoenician and Punic Archaeology (L-OR/06). Her research focuses on Phoenician-Punic archaeology with particular emphasis on Mediterranean cultural interactions, religious practices, and material culture. Key areas of investigation include: Phoenician colonization in the central Mediterranean (Sicily, Sardinia, Malta, North Africa) Phoenician religious practices, particularly worship of Astarte Phoenician-Punic pottery technology and typology Mediterranean cultural interactions between Phoenicians and indigenous populations Sacred landscapes and coastal sanctuaries in the Mediterranean Environmental archaeology and human-environment interactions at Phoenician sites Dr. Spagnoli's recent scholarly output demonstrates a strong interdisciplinary approach, combining traditional archaeological methods with advanced scientific techniques. Her work increasingly incorporates archaeometric analyses, 3D documentation, and environmental studies to deepen understanding of Phoenician material culture, religious practices, and human-environment interactions. Key research sites include Motya in Sicily and Ras il-Wardija in Gozo, Malta, where she leads the Sapienza Archaeological Expedition. Her scholarly contributions span ceramic studies, religious archaeology, landscape studies, and archaeometry, reflecting the multifaceted nature of Phoenician-Punic studies in the contemporary academic landscape. Dr. Spagnoli actively supervises student theses and leads archaeological fieldwork, including the Sapienza Archaeological Expedition at Gozo - Ras il-Wardija Project. She also coordinates laboratory work, including ceramic analysis and digital documentation of archaeological finds. Her research team includes specialists in archaeobotany, archaeometry, digital documentation, and religious studies, reflecting the interdisciplinary nature of contemporary Mediterranean archaeology.
Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.
Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.
Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.
Fabrizio Falchi is a researcher at the Artificial Intelligence for Media and Humanities (AIMH) Lab of the Institute of Information Science and Technologies (ISTI) within Italy's National Research Council (CNR). He also maintains an associate position at the Biorobotics Institute of Scuola Superiore Sant'Anna. His work focuses on developing advanced multimedia retrieval systems, with the VISIONE platform being his most notable contribution, which has won international competitions including the Video Browser Showdown in 2024 and placed second in 2023. Falchi's educational background includes: Ph.D. in Information Engineering from University of Pisa (Italy) Ph.D. in Informatics from Faculty of Informatics of Masaryk University of Brno (Czech Republic) M.B.A. from Scuola Superiore Sant'Anna in Pisa His research spans deep learning, convolutional neural networks, deep features extraction, similarity search algorithms, distributed indexing systems, multimedia information retrieval, computer vision applications, and peer-to-peer systems. Falchi has made significant contributions to fine-grained visual understanding, cross-modal retrieval (particularly image-text matching), and robustness of deep learning systems against adversarial attacks. His work demonstrates a strong focus on practical applications of these technologies, particularly in video retrieval systems and safety monitoring solutions. Analysis of Falchi's recent publications reveals a strong focus on video and image retrieval systems, with the VISIONE platform being central to his work. His research shows increasing emphasis on fine-grained understanding in computer vision, cross-modal retrieval, and addressing practical challenges like cross-resolution face recognition. Recent work demonstrates innovation in making these systems more efficient through techniques like knowledge distillation (ALADIN) and leveraging virtual worlds for training data. His publications consistently bridge theoretical advances with practical applications in surveillance, safety monitoring, and multimedia search. Falchi's work has received significant recognition: Best paper award at CBMI 2024 for 'Is ClLIP the main roadblock for fine-grained open-world perception?' VISIONE 2024 won the Video Browser Showdown competition in Amsterdam VISIONE obtained second place at Video Browser Showdown 2023 in Bergen Best Paper Award for 'Learning Safety Equipment Detection using Virtual Worlds' at CBMI 2019 Falchi collaborates extensively with researchers at ISTI-CNR, particularly within the AIMH Lab. His work on VISIONE involves collaboration with Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Claudio Gennaro, Nicola Messina, Lucia Vadicamo, and Claudio Vairo. As co-chair of Ital-IA 2023, the 3rd National Conference on Artificial Intelligence, he plays an active role in the academic community. He is a member of ACM (since 2012), the Computer Vision Foundation, the Italian Association for Computer Vision Pattern Recognition and Machine Learning (CVPL), and the CINI Lab on Artificial Intelligence and Intelligent Systems. Falchi is a key member of the Artificial Intelligence for Media and Humanities (AIMH) Lab at ISTI-CNR, where he leads research on video retrieval systems. The lab has developed the award-winning VISIONE platform, which combines multiple scientific results in content-based video retrieval. His team focuses on developing systems that enable users to search for target videos using textual prompts, drawing objects and colors, or images as query examples. The lab's work demonstrates strong interdisciplinary collaboration, bridging computer science with practical applications in media, safety monitoring, and urban environments.
Daniela Carnevale is a Full Professor at Sapienza University of Rome, where she serves in the Department of Medical-Surgical Sciences and Biotechnologies within the Faculty of Medicine. She directs the Laboratory of Neuro and Cardiovascular Immunology at the Department of Molecular Medicine, based at IRCCS Neuromed in Pozzilli. Her academic career spans over a decade with a permanent position as Assistant Professor since 2012, progressing to her current role as Full Professor. Education: PhD in Neuroscience (2010) - Faculty of Medicine and Surgery, Catholic University of the Sacred Heart in Rome Professional qualification in Biology (2006) - Sapienza University of Rome Degree in Biological Sciences (2005) - Sapienza University of Rome, final mark 110/110 cum laude Daniela Carnevale's research primarily focuses on the intersection of cardiovascular medicine, neuroscience, and immunology. She investigates neuroimmune mechanisms in hypertension, exploring how the nervous and immune systems interact to influence blood pressure regulation and organ damage. Her work encompasses biotechnologies and translational approaches to understand arterial hypertension, chronic heart failure, Alzheimer's related dementias, and adaptive immune responses. She has pioneered research on brain-spleen communication pathways in hypertension, demonstrating how neural signals prime immune responses that contribute to cardiovascular pathology. Analysis of her recent publications reveals a strong emphasis on neuroimmune cardiovascular interfaces, with particular focus on hypertension-induced cognitive impairment, cerebrovascular remodeling, and organ damage mechanisms. Her work increasingly integrates advanced imaging techniques, genetic analyses, and immune profiling to uncover novel therapeutic targets for cardiovascular and neurodegenerative conditions. Scientific Awards: Fellow of the American Heart Association (FAHA) (2016) Council on Hypertension Mid Career Award for Research Excellence (2019) International Patent in medical technology (2010) Multiple research awards from the Italian Society of Arterial Hypertension Research Award from the Foundation "Roberto Cornelli" (2009) Professor Carnevale has secured significant research funding as Principal Investigator, including grants from Sapienza University, Istituto Pasteur-Fondazione Cenci Bolognetti, and the Italian Ministry of Health. She has supervised numerous PhD students, post-doctoral fellows, and undergraduate students across various biomedical programs. Her laboratory collaborates internationally with leading researchers from Harvard University, Vanderbilt University, University of Glasgow, and University of Munich. At IRCCS Neuromed, Professor Carnevale directs the Laboratory of Neuro and Cardiovascular Immunology, where her team investigates the cellular and molecular mechanisms underlying neuroimmune interactions in cardiovascular diseases. Her research facility includes advanced preclinical imaging capabilities, including a 7Tesla MRI for small animal research, which she secured through competitive grant funding.
Marco Maggini is a Full Professor in the Department of Information Engineering and Mathematics at the University of Siena, a position he has held since joining the university in 1996. His academic career spans over 25 years with foundational expertise in computer engineering and artificial intelligence, focusing on theoretical and applied machine learning research. His educational background includes: Laurea degree (cum laude) in Electronics Engineering from the University of Florence (1991) Ph.D. in Computer Engineering and Control Systems from the University of Florence (1995) Prof. Maggini's research encompasses machine learning, neural networks, kernel machines, and the integration of symbolic and sub-symbolic knowledge systems. He extends these foundations into practical applications including web mining, search engine technology, pattern recognition, natural language processing, and computer vision. This interdisciplinary approach bridges theoretical computer science with real-world implementation challenges across multiple domains. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on multilingual NLP applications, particularly educational puzzle generation for low-resource languages (Italian, Arabic, Persian, Turkish) using LLMs. His work demonstrates consistent innovation in named entity recognition, commonsense reasoning evaluation, and cross-lingual adaptation techniques. Secondary research threads include medical imaging segmentation, molecular property prediction, and AI security vulnerabilities, reflecting his broad technical mastery across computer vision, bioinformatics, and adversarial machine learning. No specific scientific awards were mentioned in the provided documentation, though his editorial roles indicate peer recognition within the academic community. While student mentoring details are absent from the source material, his position as Full Professor and leadership of SAILab imply active graduate supervision. His extensive publication record (120+ papers) and editorial service suggest significant research grant involvement, though specific funding sources remain undocumented. He directs the Siena Artificial Intelligence Laboratory (SAILab), which serves as an interdisciplinary hub for advancing machine learning theory and applications. The lab's current projects emphasize educational technology, multilingual NLP systems, and the integration of symbolic reasoning with neural architectures, maintaining strong industry and international academic collaborations.
