جست‌وجوی استاد و پژوهشگر

Gemma Catolinoمشاهده پروفایل

استادیار

• Software Engineering
• Empirical Software Engineering
• Human Factors in Software Development
• +۱۰ مورد دیگر

Gemma Catolino is an Assistant Professor at the Department of Computer Science, University of Salerno, and affiliated with the Software Engineering (SeSa) Lab. She has also served as an Assistant Professor at Tilburg University and Eindhoven University of Technology through the Jheronimus Academy of Data Science from September 2022 to December 2023, and previously as a Postdoctoral Researcher at Delft University of Technology and Tilburg/Eindhoven institutions. PhD in Computer Science, University of Salerno (2020), supervised by Prof. Filomena Ferrucci MSc in Management and Information Technology, University of Salerno (2016, magna cum laude) BSc in Computer Science, University of Molise (2014) Her research centers on empirical software engineering, focusing on both technical and social aspects affecting software development. Key areas include code smells, defect prediction, testability, changeability, and the emerging concept of “Community Smells”—social dysfunctions in developer teams. She investigates how human factors, team diversity (especially gender), and developer experience influence software quality and maintenance effort, often using mining software repositories and machine learning techniques. Her recent publications span high-impact journals and conferences such as IEEE TSE, EMSE, JSS, ICSE, and ICSME, with a strong trend toward integrating social and technical metrics for just-in-time defect prediction in mobile applications, analyzing community dynamics, and applying software quality metrics to cybersecurity contexts like dark web analysis. She has also contributed to MLOps and serverless computing. She has received several honors including a DEI research grant (2020), Best Technical Paper at BENEVOL 2019, first and second place in ACM Student Research Competitions (2018, 2017), and the Best Master Thesis award from the Italian Software Metrics Association (2017). Gemma Catolino has been actively engaged in academic service as a referee for top journals like IEEE TSE, EMSE, JSS, and IST, guest editor for special issues, and program/organizing committee member for major conferences including ICSE, MSR, SANER, and MobileSoft, where she served as Program Co-Chair in 2022. She has also contributed as a teaching assistant, lecturer, and course coordinator in machine learning and software engineering courses. She leads and contributes to research projects involving international collaborations, particularly with researchers such as Prof. Filomena Ferrucci, Prof. Andy Zaidman, Prof. Willem-Jam van den Heuvel, and Prof. Alexander Serebrenik. Her work bridges empirical software engineering with practical tool development and socio-technical analysis, positioning her at the forefront of modern software engineering research.

Fosca Giannottiمشاهده پروفایل

استاد

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +۶ مورد دیگر

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.

Marta Catilloمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Machine Learning
• Intrusion Detection Systems
• +۳ مورد دیگر

Marta Catillo is a Researcher at the Department of Engineering (DING) of the University of Sannio (UNISANNIO) . She specializes in Cybersecurity , with focus on Machine Learning applications for intrusion detection , IoT security , and cloud auto-scaling mechanisms . Her research addresses challenges in Denial of Service (DoS) mitigation , anomaly detection , and deep learning architectures for security. Teaching: [803004] PROGRAMMING 1 for Electronic and Biomedical Engineering students (2025 cohort) Contact: Office hours: Thursdays 3-5 PM, Room 23, Bosco Lucarelli Palace Research trends: From 2019-2025 publications, her work spans adversarial attack resistance , collective anomaly detection , outlier-aware architectures , and empirical analysis of defense mechanisms , with recurring collaborations with Antonio Pecchia , Umberto Villano , and Massimiliano Rak . Key methodologies include deep autoencoders , hybrid detection systems , and measurement-based security evaluation . Technical Contributions: Developed the ZED-IDS framework for zero-day threat detection, MultiCIDS for multivariate time series intrusion detection, and DEFEDGE for edge-cloud security testing.

• Sensor network data analysis
• Crowdsourced data analysis from citizen science initiatives
• Statistical modeling and analysis of spatio-temporal data
• +۳ مورد دیگر

Francesco Finazzi is a full professor of statistics (SECS-S/02) at the Department of Economics of the University of Bergamo and maintains a research affiliation with the School of Mathematics and Statistics at the University of Glasgow. He is internationally recognized as the founder of the Earthquake Network citizen science initiative (www.sismo.app), which pioneered the first globally implemented smartphone-based seismic early warning system. His research expertise spans sensor network data analysis, crowdsourced data from citizen science initiatives, statistical modeling of spatio-temporal data, and parallel scientific software development. He has made significant contributions to developing statistical methodologies for earthquake parameter estimation using smartphone sensor networks and has advanced techniques for analyzing environmental time series data. His recent publications reveal a strong research trajectory focused on earthquake early warning systems, spatio-temporal environmental modeling, and statistical approaches to crowdsourced seismic data. His work uniquely bridges statistics, seismology, and computer science to develop practical, real-world early warning solutions that leverage mobile technology and citizen participation. Finazzi has secured substantial research funding as Principal Investigator for the University of Bergamo in major Horizon 2020 projects, including RISE (Real-time earthquake risk reduction for a ReSilient Europe, 2019-2023, €8,000,000) and TURNkey (Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System, 2019-2022, €7,999,948). He leads the Earthquake Network initiative, a groundbreaking citizen science project that has created the first operational smartphone-based seismic early warning system worldwide. This platform not only provides early warnings but also facilitates rapid impact assessment and supports search and rescue operations following seismic events.

Sandro Bartoliniمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +۵ مورد دیگر

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

Alfredo Bensoمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +۱۶ مورد دیگر

Alfredo Benso is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. He has been continuously involved in the Doctoral College for Computer and Systems Engineering since 2008, guiding PhD education across numerous cycles. His academic roles span teaching and research leadership in bioinformatics, systems biology, and computer engineering. Research interests: Bioinformatics and computational biology Systems biology and gene regulatory networks Artificial intelligence and machine learning in biomedicine Molecular modeling and multiscale simulation Biological database systems and data integration Health informatics and public health modeling His recent publications reflect a strong trend in applying AI and machine learning to biological and medical challenges, including protein function prediction, Alzheimer’s and Multiple Sclerosis modeling, food fraud detection, and viral genome analysis. These works span journals and conferences in bioinformatics, computational biology, and biomedical engineering, demonstrating interdisciplinary innovation. Scientific Awards: BIOINFORMATICS 2014 BEST PAPER AWARD (INSTICC, United Kingdom) Advising and Grants: He supervises PhD students, including Sofia Ostellino, and leads major research initiatives such as the BIGMECH (2023–2025) and FISHUB (2016–2018) projects. He has served as Scientific Director for collaborative agreements with public administrations and as Principal Investigator on EU, national (PRIN), and regional research grants focused on bioinformatics, space systems, and reliable digital technologies. Labs and Teams: He is a key member of the SBG - System Biology Group (DAUIN) and contributes to PolitoBIOMed Lab, fostering collaborative research in biomedical engineering and computational biology.

• Control Systems Engineering
• Neural Networks
• Robotics
• +۳ مورد دیگر

Nikolas Sacchi is a Research Fellow at the Department of Industrial and Information Engineering, University of Pavia. His research focuses on advanced control systems, particularly the integration of neural networks with sliding mode control methodologies for nonlinear systems. He specializes in actuator fault diagnosis, adaptive control strategies under unknown dynamics, and robust observer design. Key research areas include control engineering, robotics, and artificial intelligence applications in automation. His work addresses challenges in system reliability, dynamic stabilization, and intelligent control architectures. Recent publications emphasize neural network-based solutions for integral sliding mode control, fault diagnosis mechanisms, and deep learning approaches for systems with uncertain dynamics. His contributions bridge theoretical control frameworks with practical engineering implementations.

• Functional Safety
• Functional Testing
• Microprocessor Testing
• +۹ مورد دیگر

Riccardo Cantoro is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. He is affiliated with the CAD - Electronic CAD & Reliability Group and the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His work bridges academic research and industrial applications through multiple commercially funded projects. Scientific Disciplinary Sector: IINF-05/A - Information Processing Systems ERC Sectors: PE7_4, PE6_2, PE6_11, PE6_12 His research focuses on functional safety, functional testing, and microprocessor testing, with a strong emphasis on embedded systems and reliability. He applies machine learning and formal methods to enhance test efficiency and system robustness, particularly in automotive and safety-critical domains. His work integrates computer-aided design, fault modeling, and resilience assessment in both hardware and AI systems. The recent publications highlight a trend toward data-efficient and intelligent testing methodologies, combining machine learning (e.g., TabPFN, active learning) with traditional electronic design automation. Topics include microcontroller performance screening, CNN resiliency, FeFET device testing, and system-level test optimization, reflecting a cohesive research agenda in trustworthy computing and hardware reliability. Scientific Awards: No awards explicitly mentioned in the provided texts. Advising and Grants: Dr. Cantoro supervises numerous PhD students in Computer and Systems Engineering, focusing on functional safety, test methodologies, and AI for CAD. He leads multiple industry-funded research projects, including collaborations with Infineon Technologies and Dana-TM4 Italia, on topics such as ATPG tools, speed monitor modeling, and power module reliability. His role as Scientific Manager/Head underscores his leadership in applied research and technology transfer. Labs and Teams: He is a core member of the CAD - Electronic CAD & Reliability Group (DAUIN) and contributes to the CARS@PoliTO center, fostering interdisciplinary research in automotive systems and sustainable mobility.

Gianluigi Ferrariمشاهده پروفایل

استاد

• Telecommunications
• Internet of Things
• Wireless Networks
• +۵ مورد دیگر

Gianluigi Ferrari is a Full Professor of Telecommunications at the Department of Engineering and Architecture, University of Parma, Italy. He has been at the University of Parma since 2002, following his Laurea and PhD degrees from the same institution in 1998 and 2002 respectively. Professor Ferrari serves as the coordinator of the Internet of Things (IoT) Lab since 2006, Vice-director of the Future Technology Lab (2020-), and President of the Degree Course in Computer, Electronics and Telecommunications Engineering (2021-). He has held visiting researcher positions at USC, CMU, KMITL, NATO in Brussels, and NCTU. Professor Ferrari's research focuses on complex networked communication systems with emphasis on signal processing and network communication. His work spans Internet of Things architectures, wireless sensor networks, vehicular communications, and edge computing systems. He has made significant contributions to IoT protocols, sensor network optimization, and vehicular communication systems, with applications in smart cities, healthcare monitoring, and intelligent transportation systems. His interdisciplinary approach bridges theoretical foundations with practical implementations in real-world scenarios. His recent publications demonstrate a strong trend toward privacy-preserving techniques in IoT systems, edge intelligence for real-time analytics, and optimization of communication protocols for emerging applications. The research spans healthcare monitoring with wearable devices, vehicular communications with federated learning approaches, and novel routing protocols for BLE mesh networks. His work consistently addresses the challenges of energy efficiency, security, and reliability in networked systems. best paper/technical awards at various international conferences Senior Member of the IEEE Editor-in-Chief of Future Internet (MDPI, 2023-) Editor of ICT Express (Elsevier, 2021-) Professor Ferrari participates in numerous research projects funded by public and private institutions with research grants exceeding €5 million. He has served on the editorial boards of multiple international journals and organized several workshops and special sessions at major conferences. His professional activities include serving as Technical Team Member in NATO Research Task Group HFM-260 (2015-2018) and member of the Scientific Council of ARTEMIS association (2019-) and CNIT (2020-). As director of the IoT Lab at the University of Parma, Professor Ferrari leads a research team focused on developing innovative solutions for Internet of Things applications. The lab investigates communication protocols, network architectures, and data processing techniques for various IoT domains including smart cities, healthcare, and intelligent transportation systems. The lab maintains strong collaborations with international research institutions and industry partners, facilitating technology transfer and practical implementation of research outcomes.

Andrea Sgambelluriمشاهده پروفایل

استادیار

• Software Defined Networking (SDN)
• Optical Networks
• Network Reliability
• +۶ مورد دیگر

Andrea Sgambelluri is an Assistant Professor at the TeCIP Institute of Scuola Superiore Sant'Anna in Pisa, Italy. He holds a Master's degree in Telecommunications Engineering (2007) and a Ph.D. (2015) from the University of Pisa, where he received the 2015 OFC Corning Outstanding Student Paper Competition Grand Prize. His research focuses on Software Defined Networking (SDN) for optical networks, network reliability, and AI applications in telecommunications. Education: M.Sc. and Ph.D. in Telecommunications Engineering (University of Pisa) Current Role: Assistant Professor (RTDB), TeCIP Institute, Scuola Superiore Sant'Anna Previous Role: Postdoctoral Researcher at KTH Royal Institute of Technology (2016) Research interests span SDN protocol extensions, YANG/NETCONF solutions, network telemetry, and Machine Learning techniques for optical network management. Recent publications highlight innovations in free space optics, failure forecasting, and edge orchestration frameworks. Key scientific contributions include experimental evaluations of OTN transceivers, augmented reality latency monitoring, and open-source optical software development. His work integrates AI with network function virtualization (NFV) and real-time monitoring systems. Scientific Award: 2015 OFC Corning Outstanding Student Paper Competition Grand Prize Andrea participates in European research projects on next-generation optical networks and cloud-based orchestration, bridging satellite communication and terrestrial network technologies.

• Algorithm design and analysis
• Cellular networks
• Connected cars
• +۱۱ مورد دیگر

Carla Fabiana Chiasserini is a Full Professor and Deputy Director at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin. She serves as a Component of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and acts as a Spoke leader for research and innovation activities. Her academic career spans multiple prestigious institutions and she maintains active collaborations worldwide. Professor Chiasserini's research interests span algorithm design and analysis, cellular networks, connected cars, edge computing, heterogeneous wireless networks, Internet of Things, machine learning, mobile networks, mobile services, and performance evaluation. Her work bridges theoretical algorithm development with practical applications in next-generation telecommunications systems. She leads the TNG research group at DET and focuses on Machine Learning for Networking, with specific research lines in Network Slicing in 5G, Connected autonomous cars, and Opinion dynamics in social networks. Her research aligns with Sustainable Development Goals including Industry, Innovation and Infrastructure; Sustainable Cities and Communities; and Climate Action. Her recent publications demonstrate a strong trend toward integrating machine learning with edge computing, 5G/6G systems, and automotive applications. The research spans from theoretical algorithm development to practical implementations for XR offloading, distributed service provisioning, reliability assessment of AI-based automotive systems, and satellite networking. Her work shows increasing focus on practical implementations with industry applications, particularly in the automotive sector and next-generation telecom infrastructure. Best Paper Award - Wireless Telecommunications Symposium (WTS) 2018 Best Paper Award - IEEE WoWMoM 2016 Best Paper Award Runner-up at ACM MSWiM 2016 Top Paper Award at the ACM CoNEXT 2016 Cloud-Assisted Networking (CAN) Workshop Best Paper Award at SPACOMM 2014 Best Paper Award at AD HOC NOW 2014 2010 Editor of the Year Award for the Ad Hoc Networks journal (Elsevier) IEEE Fellow (2018-) ACM Fellow (2024-) Professor Chiasserini actively advises numerous PhD students working on cutting-edge topics in network systems, with recent graduates focusing on edge services in 5G networks, resource-aware learning in mobile networks, and deployment of microservices at the network edge. She leads multiple significant research grants including O-RAN (2024-2027), CSI-Future (2023-2025), RESTART - Spoke 4 (2023-2025), PREDICT-6G (2023-2025), and several others funded by PNRR, EU Horizon programs, and industry partnerships. Her work has substantial practical impact through numerous patents including OffloaDNN and SEM-O-RAN. She leads the TNG research group within the Department of Electronics and Telecommunications and participates in the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. Her laboratory work focuses on practical implementations of theoretical concepts, particularly in the areas of connected vehicles, edge computing, and 5G/6G systems. She maintains strong industry connections through projects with Intel Corporation and other technology partners, ensuring her research has direct real-world applications.

• Operations Research
• Healthcare Optimization
• Predictive Maintenance
• +۴ مورد دیگر

Rosita Guido is an Associate Professor in the Department of Mechanical, Energy and Management Engineering at Università della Calabria, specializing in Operations Research (MATH-06/A). Her academic role focuses on mathematical optimization and artificial intelligence applications across healthcare, manufacturing, and sustainable systems. She teaches graduate courses including Tools and Methods for Innovation in Healthcare and Business Intelligence within the Management Engineering program. Her research interests span healthcare optimization, predictive maintenance, machine learning applications, and sustainable supply chain management. Guido develops advanced mathematical models and algorithms for complex decision problems under uncertainty, with particular emphasis on healthcare systems optimization, tool condition monitoring, and resource allocation. Her methodological expertise includes mathematical programming, combinatorial optimization, stochastic programming, and hybrid AI techniques. Analysis of her recent publications reveals a strong interdisciplinary focus bridging engineering and healthcare. Key trends include the application of machine learning (particularly SVMs and CNNs) to medical diagnostics, optimization of healthcare operations (patient admission scheduling, bed management), and integration of AI with IoT for industrial applications. Her work demonstrates consistent innovation in solving real-world problems through mathematical modeling and algorithm development. As an active member of the Operations Research (Ricerca Operativa) research group at DIMEG, she contributes to projects addressing logistics optimization, green transportation systems, energy infrastructure management, and clinical process optimization. The group maintains international collaborations with prestigious research centers and companies including Amazon. Guido's teaching portfolio includes graduate-level courses in Management Engineering, where she integrates her research expertise into curriculum development. Her academic service includes participation in departmental governance and research initiatives within the Department of Mechanical, Energy and Management Engineering.

Marco Temperiniمشاهده پروفایل

دانشیار

• Technology Enhanced Learning
• Student Modeling
• Teacher Modeling
• +۹ مورد دیگر

Marco Temperini is an Associate Professor of Information Processing Systems at Sapienza University of Rome, where he teaches programming techniques and web programming languages. With a Mathematics degree (1986) and Computer Science PhD (1992) from Sapienza University, he has established himself as a prominent researcher in educational technology. His educational background includes: Laurea in Matematica (1986, Sapienza University of Rome) Dottorato in Informatica (1992, Sapienza University of Rome) Professor Temperini's research focuses on Technology Enhanced Learning, with particular expertise in Student and Teacher Modeling, Adaptive e-learning systems, and the application of Data Mining and Artificial Intelligence techniques to personalize learning content. His work extends to Social and Collaborative Learning environments, Game Based Learning approaches, and innovative Automated and Peer Assessment methodologies. With extensive international collaboration experience, he has coordinated research units and served as work-package leader in numerous projects including EuroCompetence, UnderstandIT, and 9Conversations. His recent publications (2023-2024) reveal a strong trend toward applying advanced AI techniques, particularly large language models, to educational assessment challenges. These works demonstrate increasing sophistication in leveraging natural language processing for automated analysis of student work, concept maps, and programming assignments, while maintaining a strong focus on practical educational applications. Notable scientific recognition includes: 2023 Best Reviewer Award 2023 Best Paper Award 2022 Best Paper Award 2021 Best Paper Award 2018 Best Paper Award at ICWL2018 Membership on the Human-Computer Interaction Awards Committee (2021.6-2024.7) Professor Temperini has been highly active in the academic community, serving as Conference co-Chair for ICWL (2016), Technical Program co-Chair for multiple conferences including MIS4TEL and SETE, and General Chair for MIS4TEL2022 and SETE2023. He is a member of the Steering Committee for ICWL and MIS4TEL conferences and serves on the IEEE Technical Committee on Learning Technology. As an associate editor for the International Journal on Distance Education Technology, Frontiers in Artificial Intelligence, and Computers & Education: Artificial Intelligence, he plays a significant role in shaping research directions in educational technology. His leadership extends to numerous international projects where he has served as coordinator of research units and work-package leader, including EuroCompetence, Socrates Project 56544-CP-1-98-1; CIOC, NFU Project 18; mENU, e-learning Project 2002-0510/001-001; QUIS, e-learning Project 2004-3538/001-001; UnderstandIT, Leonardo Da Vinci Transfer of Innovation; and 9Conversations: Network building for self-employment of refugees. These projects demonstrate his commitment to applying technology to address real-world educational challenges across diverse contexts.

• Antenna Design
• Microwave Engineering
• Radar Systems
• +۳ مورد دیگر

Pietro Rosatti is a Research Fellow at the Department of Industrial Engineering, University of Trento, specializing in electromagnetic systems and artificial intelligence applications. His research focuses on: Antenna Design for automotive radar and communication systems Microwave Engineering and mm-wave technologies Artificial Intelligence integration in electromagnetic problem-solving Inverse Problems and electromagnetic imaging System-by-Design optimization frameworks Real-world applications in medical imaging and radar systems Analysis of his 17 publications (2018-2025) reveals a dominant trend toward AI-driven solutions for electromagnetic challenges, particularly in automotive radar antenna design (77 GHz systems), microwave medical imaging, and ground-penetrating radar. His work consistently employs multi-objective evolutionary optimization and system-by-design methodologies to overcome the curse of dimensionality in inverse scattering problems, with increasing emphasis on real-time deep learning frameworks since 2021.

• Autism Spectrum Disorder (ASD)
• Virtual Reality (VR) Interventions
• Machine Learning in Behavioral Analysis
• +۳ مورد دیگر

Gianpaolo Alvari is a Research Fellow at the Department of Psychology and Cognitive Science, University of Trento. His work focuses on developing technology-driven interventions for Autism Spectrum Disorder (ASD), leveraging Virtual Reality (VR) and Machine Learning (ML) methodologies. His research emphasizes clinical applications, including eye-tracking systems for social behavior analysis and game-based therapies for adolescents with ASD. Key research areas include VR-based interventions, ML analysis of micro-expressions in infants, and real-world clinical tools for ASD assessment. He collaborates with multidisciplinary teams to design user-centered solutions for ASD therapy and early diagnosis. His publications span topics from physiological responses in VR environments to fine-grained behavioral analysis in clinical settings. Alvari’s work integrates engineering approaches with psychological insights, aiming to improve clinical outcomes through innovative technologies. His projects often involve co-design methodologies with end-users, ensuring practical applicability in therapeutic contexts.