Stefano Magrini is a Full Professor at Ca' Foscari University of Venice's Department of Economics. His research examines economic policy, regional and urban economics, income inequality, and spatial econometrics. Recent publications analyze urban growth patterns, regional convergence/divergence dynamics, inequality measurement, and pandemic economic impacts using advanced spatial and statistical methodologies.
Giuseppe Ciraolo is a Full Professor at the University of Palermo's Department of Engineering, holding a PhD in Hydraulic Engineering. His research focuses on remote sensing applications for hydrological monitoring, coastal dynamics, and marine water quality assessment. Research Areas: Remote sensing of hydrological processes; spatial-temporal vegetation dynamics; marine/coastal water quality monitoring; hydrodynamic modeling; integration of remote sensing with in-situ data; coastal erosion analysis. Projects: Led the CALYPSO project establishing permanent HF Radar systems for marine current monitoring in the Sicily-Malta channel. Collaborated on EU projects including SIMIT for cross-border civil protection systems. Teaching: Courses include Remote Sensing Laboratory, Coastal Defense, Hydrology and Climate Change, and Remote Sensing for Environmental Monitoring.
Andrea Bontempelli is a Research Fellow at the University of Trento within the Knowdive research group . He completed his Ph.D. in Information and Communication Technologies at the University of Trento in 2024, supervised by Prof. Fausto Giunchiglia and Prof. Andrea Passerini. During his doctoral studies, he conducted a visiting Ph.D. at the IDIA Research Institute in Switzerland under the guidance of Prof. Daniel Gatica-Perez, head of the Social Computing Group. Research Interests : Andrea specializes in interactive and incremental machine learning , with a focus on handling noisy data , knowledge drift , and context recognition from mobile sensor streams . His work bridges human-in-the-loop systems with real-world applications , emphasizing robust evaluation of machine learning models in both controlled and naturalistic environments. Publication Trends : Andrea’s articles explore interactive alignment of prototypical networks , cross-cultural behavioral modeling , and lifelong context recognition . Key themes include data drift mitigation , model debugging , and real-time sensor data processing , reflecting his commitment to adaptive, user-centric machine learning solutions. Affiliations : Knowdive group, University of Trento Social Computing Group, IDIAP Research Institute (visiting Ph.D.)
Mario Lauria is an Associate Professor at the Department of Mathematics of the University of Trento, also affiliated with the Interdepartmental Center for Mind/Brain Sciences (CIMEC). He holds a PhD in Electrical and Computer Engineering from the University of Naples Federico II and conducted postdoctoral research at the University of Illinois and UC San Diego. He has held academic positions at The Ohio State University, TIGEM in Naples, and the Microsoft Research-COSBI Centre. His research focuses on computational methods for biomarker discovery, systems biology, and gene regulatory network analysis. Lauria has coordinated multiple EU and industry-funded projects, including work on metabolic markers of pre-diabetes and systems biology approaches to neurodegenerative diseases. Education: PhD in Electrical and Computer Engineering, University of Naples Federico II (1997) M.S. in Computer Science, University of Illinois at Urbana-Champaign (1996) Laurea in Electrical Engineering, University of Naples Federico II (1992) Research interests emphasize bioinformatics , systems biology , and high-performance computing , with applications to gene regulatory networks, diagnostic biomarkers, and multi-omics data integration. His work bridges computational methods with biological systems, particularly in aging, neurodegenerative diseases, and metabolic disorders. Publications reflect a focus on Alzheimer’s biomarkers , diabetes , and gene network analysis . Key contributions include rank-based biomarker algorithms and consensus clustering methods for metabolic profiles. His interdisciplinary approach spans computational tools (e.g., SCUDO, rScudo) and collaborative projects with pharmaceutical and academic partners. Awards include the 2012 SBV IMPROVER Challenge win and IEEE Senior Membership. He serves on editorial boards for journals like IEEE Transactions on Parallel and Distributed Systems and BMC Bioinformatics . Grants and collaborations include leadership in the EarlyBird pre-diabetes project and contributions to EU-funded initiatives like AnEUploidy . His academic service roles include coordinating the Data Science and Quantitative Biology master’s programs at Trento University. Labs/Teams: Active in the Microsoft Research-COSBI Centre for systems biology and the CIMEC interdisciplinary neuroscience hub.
Spairani Edoardo is a Research Fellow at the Department of Industrial and Information Engineering, University of Pavia. His work focuses on biomedical engineering, particularly in medical imaging, signal processing, and fetal monitoring. He has contributed to advancements in ultrasound imaging enhancement and fetal health diagnostics using machine learning techniques. Recent research includes deep learning applications for ultrasound beamforming, spectral analysis of fetal heart signals, and hidden Markov models for cardiotocography analysis. These studies aim to improve clinical diagnostics and non-invasive fetal health assessment. No scientific awards or advising roles are explicitly listed in the provided materials. His disciplinary expertise aligns with ING-INF/06 - Electronic and Computer Bioengineering.
Barbara Russo is a Full Professor at the Faculty of Computer Science of the Free University of Bozen-Bolzano, Italy. She is a member of the International Software Engineering Research Network (ISERN) and has held visiting researcher positions at the Max-Planck Institute for Mathematics (Germany) and the University of Liverpool (UK). Her research focuses on Machine Learning in Software Engineering, Software Reliability/Testing, and Systems Engineering. She founded and coordinated the Software and Systems Engineering (SwSE) research group (2014–2017) and is involved in initiatives like the European Masters Program in Software Engineering (EMSE). Awarded the 2007 Best Public-Private Collaboration Award for establishing a dual BSc program in Computer Science. Established a makerspace (now FabLab) at her faculty and contributed to projects like the Euregio Trustworthy AI Lab. Research spans DevOps processes, embedded systems vulnerability, and collaborative AI assurance. Her work emphasizes empirical software engineering and practical industry collaboration, with notable contributions to scalability testing, performance modeling, and vulnerability detection.
Sara Comai is an Associate Professor at the Politecnico di Milano , affiliated with the Department of Electronics, Information and Bioengineering . She coordinates the Assistive Technology Group (ATG) and leads the MEP (Maps for Easy Paths) project. Research Focus : Database systems, smart home technologies, indoor localization, and assistive solutions for elderly/dementia patients Recent Trends : 2025-2023 publications highlight Occupancy detection via Wi-Fi/infrared sensors Behavior classification for Alzheimer's patients Wearable/RFID-based health monitoring Human-robot collaboration in agriculture Adaptive machine learning for land use segmentation
Prof. Alessandro Repici is a Full Professor of Gastroenterology at Humanitas University Medical School and Director of the Digestive Endoscopy Unit at Humanitas Research Hospital in Milan, Italy. He specializes in advanced endoscopic techniques, including ERCP, diagnostic EUS, ESD, and stenting, with a focus on gastrointestinal malignancies and innovative endoscopic therapies. His academic career includes professorships at the University of Turin (2000–2005) and visiting roles at Georgetown University (2016) and the Mayo Clinic (2017). Educations: MD from University of Messina (1990), fellowships at Molinette Hospital (Turin), Centre Chirurgical de l'Alma (Paris), Universitatskliniken (Tubingen), and Wellseley Hospital (Toronto). Research: Over 50 studies on endoscopic innovations, including FDA/EMA-approved devices. Lead roles in international collaborations with institutions like the University of Rotterdam and Mayo Clinic. Leadership: Chair of the Italian Society of Digestive Endoscopy’s Strategic Team (2012), member of World Gastroenterology’s Publication Committee, and editorial board roles in journals like Gastrointestinal Endoscopy . He founded webendoscopy.com and organizes the IMAGE live endoscopy course. His work emphasizes AI integration in endoscopy and microbiota research in IBD.
Cecilia Surace is an Associate Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin. She is a member of the Interdepartmental Centre R3C (Responsible Risk Resilience Centre), the Polito Spin Off Evaluation Commission, and the Patent Commission. Her research focuses on structural dynamics, medical devices, and structural health monitoring, with expertise in biomechanics, nonlinear dynamics, and biomimetic materials. She has been recognized with the Elsevier Journal Exemplary Reviewer award (2013). She teaches courses in structural mechanics, dynamics of structures, and bio/nano construction science at both undergraduate and graduate levels. Her research involves projects like DIMOSS (displacement monitoring using strain sensors) and T-SURE (tissue surgical repair), and she leads teams in developing innovative repair technologies for biological tissues and aerospace structures. She advises three PhD students and holds patents for medical devices such as SurgiFET for tendon repair. Her work spans interdisciplinary applications, including bridge health monitoring, soft tissue repair, and bio-inspired nanomechanics. She contributes to editorial boards and international conferences, demonstrating leadership in her field.
Samanta Rosati is an Assistant Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin. She holds a Master’s Degree and Ph.D. in Biomedical Engineering from the same institution. Her research focuses on biomedical data interpretation, signal processing, and medical device software development. Key areas include radiomics, AI for CAD systems, and clinical process modeling. Education: Ph.D. in Biomedical Engineering (2014), Politecnico di Torino Master’s Degree in Biomedical Engineering (2010), Politecnico di Torino Research Interests: Medical imaging analysis (e.g., CT/MRI segmentation) Machine learning applications in healthcare Wearable sensor systems for telemedicine Clinical workload modeling Current Projects: Development of a wearable multi-sensor array for heart failure monitoring AI-driven systems for personalized chemotherapy prediction Telemedicine frameworks for heart failure management Grants & Advising: Actively supervises projects on medical device software certification and hernia repair outcomes analysis. Collaborates on multi-center studies for cancer treatment response prediction. Labs/Teams: Leads projects in medical AI and wearable technology within the Department’s biomedical engineering group, focusing on bridging clinical needs with engineering solutions.
Kristen M. Meiburger is a Tenure-track Assistant Professor at the Department of Electronics and Telecommunications, Politecnico di Torino. She holds a Master’s in Biomedical Engineering (2010) and a Ph.D. in Biomedical Engineering (2015) from Politecnico di Torino. She has conducted research at the University of Texas at Austin (2013–2014) and the University of Toronto (2015). Her work focuses on biomedical image processing, including ultrasound and photoacoustic imaging, radiomics, deep learning, and signal reconstruction methods. Her research interests emphasize innovative imaging techniques, non-invasive vascular analysis, and dermatological applications. She leads ongoing projects like REAP (cancer imaging with optical coherence/photoacoustic tomography), AI-VASCUES (vascular dysregulation analysis), and ImPACT-AI (ethical AI in photoacoustic imaging). She teaches and proposes thesis topics at Politecnico di Torino’s DET (Department of Electronics and Telecommunications). Recent publications highlight advancements in AI-driven imaging, such as texture analysis in OCT, hybrid deep learning frameworks for microscopy, and GANs for color normalization. Her work bridges medical imaging innovation with clinical applications, focusing on disease diagnosis and treatment monitoring. Ongoing efforts integrate ethical AI practices and multi-center imaging harmonization to improve medical decision-making.
Jacopo Sini is a Research Fellow at the Department of Automatic Control and Computer Science (DAUIN) of the Polytechnic University of Turin. His research focuses on safety-critical automotive systems, software reliability engineering, and emotion recognition. He collaborates on Master's courses related to model-based software design and autonomous vehicles. He is affiliated with the CAD - Electronic CAD & Reliability Group, contributing to projects involving ISO26262 compliance, hardware fault tolerance, and simulation methodologies. His work includes developing real-time driver monitoring systems and enhancing social acceptance of autonomous vehicles through emotion analysis. Key research outputs include studies on control flow checking for automotive embedded systems, emotion recognition algorithms, and test benches for hardware failure diagnostics. Recent publications explore Rust programming language safety features and AR-based educational platforms.
Pier Luca Lanzi is a Full Professor at Politecnico di Milano, affiliated with the Department of Electronics, Information, and Bioengineering (DEIB). He leads the Artificial Intelligence and Robotics Lab (AIRLab), focusing on interdisciplinary research at the intersection of AI, robotics, and human-centric technologies. His work emphasizes applications in gaming, healthcare, and education, leveraging machine learning, evolutionary algorithms, and virtual reality. Education: Not explicitly stated in the provided texts. Affiliations: Director of the AIRLab; core member of DEIB. His research interests span artificial intelligence, multiagent systems, procedural content generation for games, serious games for rehabilitation, and AI-driven medical imaging. He explores how AI can enhance learning experiences (e.g., VR classrooms for physics and history) and improve therapeutic outcomes through interactive frameworks like the IGER system for stroke rehabilitation. Notable contributions include frameworks for automated level design in games (e.g., DOOM level generation via GANs), AI-assisted diagnostic imaging systems, and exergaming solutions for juvenile arthritis patients. His work often bridges computational intelligence with real-world applications, emphasizing human-centered design. Advising & Grants: While no specific grant details are provided, his prolific publication record suggests sustained research activity. Students and collaborators include prominent names in AI and robotics, though formal advisee名单 are not listed here. Labs/Teams: Directs the AIRLab, which develops cutting-edge technologies in AI, robotics, and human-technology interaction. Collaborates with industry and academic partners on projects ranging from VR educational tools to medical exergames.
Lara Mauri is a Postdoctoral Researcher in Computer Science at the Department of Computer Science, University of Milan, where she is a member of the SEcure Service-oriented Architectures Research Lab. She currently oversees the HH4AI project at Human Hall, focusing on AI-human rights impact assessment frameworks. Her educational background includes a Ph.D. in Computer Science (dissertation in Machine Learning security), a Master's degree in Information Security, and a Bachelor's degree in Computer Science, all earned summa cum laude from the University of Milan. Dr. Mauri's research spans several critical areas at the intersection of artificial intelligence and security. Her primary interests include Artificial Intelligence security , Adversarial Machine Learning , Distributed Ledger Technology , and Data Protection and Privacy . Her work demonstrates particular expertise in developing security frameworks for machine learning systems and analyzing blockchain technologies. Analysis of her publication record from 2018-2025 reveals a clear research trajectory moving from foundational blockchain analysis toward increasingly sophisticated AI security frameworks, culminating in her current work on human rights impact assessment for AI systems under the EU AI Act. Her research shows consistent contributions to both theoretical security frameworks and practical implementation approaches. Her scientific contributions focus on cybersecurity and AI ethics, with particular emphasis on: Machine Learning security frameworks Blockchain and distributed ledger technologies AI ethics and regulatory compliance Critical infrastructure protection As lead researcher on the HH4AI project, Dr. Mauri directs interdisciplinary work connecting technical AI development with human rights considerations, particularly within the context of the European regulatory landscape. Her research lab focuses on developing methodological approaches to assess potential human rights impacts of AI systems before deployment.
Jessica Gliozzo is a research fellow in bioinformatics at the IRCCS Ca' Granda – Ospedale Maggiore Policlinico of Milan and collaborates with the AnacletoLab (Bioinformatics and Computational Biology laboratory). She holds a BSc in Medical Biotechnology from the University of Milan (2014) and an MSc in Molecular Biotechnology and Bioinformatics from the same institution (2016). Her primary research interests include: Bioinformatics Machine Learning Deep Learning applications in genomics Analysis of Next Generation Sequencing data Dr. Gliozzo's current research focuses on the analysis of Next Generation Sequencing (NGS) data from patients affected by primitive hematopoietic proliferative disorders of the skin. She is actively developing deep learning neural networks for the prediction and prioritization of regulatory variants in human diseases and creating network-based semi-supervised models to predict patients' clinical outcomes by leveraging heterogeneous biological data. Her recent publications demonstrate a strong trend toward integrating multi-omics data, developing knowledge graphs for RNA interactions, and applying advanced machine learning techniques to clinical problems, particularly in cancer research and genomic analysis. Her work spans computational biology, medical informatics, and artificial intelligence applications in healthcare, with a particular emphasis on dimensionality reduction techniques, network-based approaches, and resource-efficient computational methods. Network-based patient similarity analysis RNA knowledge graph construction Resource-limited medical image processing Large language models for protein science Multi-omics data integration Dr. Gliozzo has maintained extensive collaborations with researchers including Giorgio Valentini, Elena Casiraghi, Marco Mesiti, and Alessandro Petrini across numerous publications in reputable journals such as BMC Bioinformatics, Scientific Reports, and Frontiers in Bioinformatics, demonstrating consistent research productivity from 2017 through 2025.
