Find Academic Supervisors

• Sensor network data analysis
• Crowdsourced data analysis from citizen science initiatives
• Statistical modeling and analysis of spatio-temporal data
• +3 more

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 BartoliniView profile

Associate Professor

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +5 more

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.

Federica MucciView profile

Associate Professor

• International Law
• Cultural Heritage Protection
• European Union Law
• +4 more

Federica Mucci is an Associate Professor of International Law at the University of Rome Tor Vergata, affiliated with the Department of History, Humanities and Society. She specializes in international protection of cultural heritage, European Union law, and treaty law. Her teaching includes courses on cultural heritage protection and EU law for programs in Education and Tourism. Legal Expert: Italian Ministry of Foreign Affairs UNESCO Delegation Member: Contributed to the 2005 UNESCO Convention on cultural diversity and its implementation. Her research focuses on international law frameworks for cultural heritage, maritime law, treaty interpretation, and environmental protection. Key publications include monographs on cultural heritage law (2012) and a PhD thesis on EU copyright law (1998). Publications span interdisciplinary topics such as topological data analysis, though the majority of her work aligns with legal and humanities disciplines. Awards: No specific prizes are mentioned, but her scholarly output includes influential books and articles on international law. Advising/Grants: No formal student advisees or grant details provided in text. Labs/Teams: No specific lab affiliations mentioned, though her role at UNESCO implies collaboration with international bodies.

• Bioinformatics
• Computational Biology
• Pharmacogenomics
• +5 more

Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +6 more

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.

Massimo Orazio SpataView profile

Research Fellow

• Deep Learning
• Biomedical Engineering
• Computer Science
• +3 more

Massimo Orazio Spata is a Research Fellow in Computer Science at the University of Catania's Department of Mathematics and Computer Sciences, specializing in deep learning applications for biomedical, audio, and biometric systems. He has held roles at STMicroelectronics since 1999, focusing on system integration, image processing, and biomedical device R&D. He teaches courses such as Mobile Programming and Computer Architecture at secondary schools and has advised numerous students on grid computing and middleware projects. Education: PhD in Computer Science (University of Catania, 2008), MSc in Computer Science (University of Catania, 2013), and a teaching certification in Computer Science (University of Catania, 1998). Research interests include deep learning algorithms, biomedical device development, grid scheduling, and cybersecurity. He has authored patents on lab-on-chip systems, bio-computer analysis, and scheduling methods, and his work has been recognized with STMicroelectronics Innovation Awards (2016–2014) and a Cisco CCNA certification. Key collaborations include projects with Google (Mediapipe Objectron for robotics), Huawei (video deblurring), and involvement in the PNRR Horizon HiCONNECTS project (2024–present). He serves on conference committees (e.g., ICAETA 2023) and has developed e-learning systems and CAD tools for STMicroelectronics.

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

Iacopo ColonnelliView profile

Assistant Professor

• Scientific Workflow Systems
• High-Performance Computing
• Cloud Computing
• +6 more

Iacopo Colonnelli is an Assistant Professor (RTDA) at the University of Turin's Computer Science Department and a member of the Alpha Research Group (Parallel Computing) and CINI HPC Key Technologies and Tools lab. He has established himself as a leading researcher in scientific workflow systems, bridging cloud computing and high-performance computing environments. His research focuses on workflow modeling and management in heterogeneous distributed architectures, with significant contributions through open-source tools including StreamFlow (a container-native workflow management system), Jupyter Workflow, and CAPIO (middleware for I/O streaming). These tools enable hybrid workflows execution on cloud-HPC infrastructures and have gained recognition through multiple European projects and initiatives. Colonnelli's publication record shows consistent output in top venues related to parallel and distributed computing, with recent work focusing on federated learning, confidential computing, and workflow standardization. His most recent publications (2024-2026) demonstrate strong engagement with cutting-edge challenges in cross-facility federated learning, workflow representation languages, and performance evaluation of confidential computing for bioinformatics applications. Scientific Awards: ITADATA 2023 Best PhD Thesis Award for 'Workflow models for heterogeneous distributed systems' StreamFlow selected as one of the three exploitable results of the EUPEX European project (2023) StreamFlow selected as an exploring technology by the EC Innovation Radar initiative (2023) 'Distributed Workflows With Jupyter' selected as Fall 2022 Editor's Choice by Future Generation Computer Systems journal HPC-Europa3 grant recipient for Barcelona Supercomputing Center visit (2019) HiPEAC grant recipient for ACACES 2019 Summer School (2019) As an active contributor to research infrastructure, Colonnelli serves as a member of the CWL Technical Team and founder of the CWL4HPC Working Group. He has organized multiple international conferences including PDP 2025 as program chair and CWLCon 2024. His service extends to numerous program committees for major HPC conferences and journals, demonstrating his standing in the international research community. Colonnelli leads significant contributions to multiple European research projects including Space Center of Excellence, European Pilot, EUPEX, TEXTAROSSA, ACROSS, DeepHealth, and HPC4AI, focusing on advancing HPC and AI technologies for scientific applications.

Gianna Del CorsoView profile

Associate Professor

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +3 more

Gianna Del Corso is an Associate Professor at the Department of Computer Science, University of Pisa. Her research interests span Quantum Computing, Numerical Linear Algebra, and Spectral Analysis. She has advised PhD students in quantum computing topics such as quantum algorithms and machine learning. Her teaching includes courses on Numerical Calculus, Parallel Scientific Computing, and Introduction to Quantum Computing, delivered across multiple academic years at the University of Pisa. She has also contributed to advanced courses in the PhD program in Computer Science. Her research focuses on quantum algorithms for machine learning, quantum walks, eigenvalue computation of structured matrices, and spectral techniques for web analysis. Recent work includes applications of quantum k-means clustering and variance estimation subroutines. Her publications span quantum computing, numerical methods, and scientific citation models. Her articles highlight contributions to PageRank computation, matrix factorization for recommendation systems, and analysis of citation-based research evaluation models. She has actively engaged in international conferences, presenting on topics such as orthogonal iterations for nonlinear eigenvalue problems and quantum hitting time algorithms. Labs/Teams: Research conducted within the Department of Computer Science at the University of Pisa, focusing on quantum computing and numerical methods.

• Computational Logic
• Answer Set Programming
• Bioinformatics
• +6 more

Alessandro Dal Palu' is an Associate Professor at the Department of Mathematical, Physical, and Computer Sciences at University of Parma. He holds a PhD in Computer Science from University of Udine and has been with University of Parma since 2005, transitioning from Researcher to Associate Professor in 2014. His teaching portfolio includes courses on Computer Architecture, Constraint Programming, and Algorithms & Data Structures. His research spans computational logic, bioinformatics, and GPU computing. Notable achievements include the 2007 GULP award for his Ph.D. thesis and the ICLP 2010 best paper award. He has led Italian INdAM-GNCS research projects on GPU applications (2011) and Logic Programming in cancer genomics (2016). Recent publications focus on explainable AI frameworks, bioinformatics applications, and sustainable logistics solutions. His work integrates Answer Set Programming with biomedical challenges like protein structure analysis and cancer genome evolution. He chairs the International Conference on Logic Programming (ICLP 2018) and has organized multiple international workshops on constraint programming. 2007 GULP Award ICLP 2010 Best Paper PI for INdAM-GNCS projects (2011, 2016) Program Committee member for international conferences

• Numerical Analysis
• Machine Learning
• Optimization
• +3 more

Luca ZANNI is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, Università di Modena e Reggio Emilia. His academic career spans numerical analysis, machine learning, and optimization methods, with a focus on gradient projection algorithms, stochastic gradient techniques, and their applications in imaging and deep learning. His research includes developing adaptive steplength rules for gradient methods, variance control in stochastic optimization, and GPU-accelerated reconstruction techniques for medical imaging. He teaches courses such as Numerical Calculation with Python, Fundamentals of Machine Learning, and Mathematical Methods for Machine Learning, covering topics like numerical optimization, support vector machines, and deep learning. He has contributed to methodologies for hyperparameter-free training, active set identification in constrained optimization, and edge-preserving image reconstruction. His work bridges theoretical advancements in optimization with practical implementations in large-scale machine learning and medical imaging applications.

Luigi PomanteView profile

Assistant Professor

• Electronic System-Level HW/SW Co-Design
• Heterogeneous Parallel Systems
• Embedded Systems
• +5 more

Luigi Pomante is a tenured Assistant Professor at the University of L'Aquila, Italy, where he is affiliated with the Department of Engineering and Information Science and Mathematics (DISIM) and the DEWS Center of Excellence. His academic career focuses on research and teaching in embedded systems and hardware-software co-design methodologies. Dr. Pomante's primary research interest is Electronic System-Level Hardware-Software Co-Design of heterogeneous parallel dedicated systems. He is the principal developer of the HEPSYCODE framework, which provides comprehensive methodologies and tools for system-level design space exploration. His work addresses critical challenges in embedded systems development, including handling functional and non-functional requirements, heterogeneous architectures, and mixed-criticality constraints. He has published extensively in journals like IEEE Transactions on Computers and IET Computers & Digital Techniques, as well as at major international conferences. Analysis of Dr. Pomante's publication record reveals a consistent research trajectory focused on hardware-software co-design methodologies, with increasing attention to real-time constraints and mixed-criticality systems in more recent work. His publications demonstrate expertise in design space exploration techniques, system modeling using CSP-like approaches, and the development of metrics for evaluating hardware-software partitioning solutions. The HEPSYCODE framework represents his most significant contribution to the field. Dr. Pomante actively supervises student projects and theses related to electronic design automation and embedded systems. He has contributed to European research projects including EMC2 (Embedded Multi-Core systems for Mixed Criticality applications), where he was responsible for deliverables related to design methodologies, implementation approaches, and validation frameworks. His work has practical applications in aerospace and other safety-critical domains through collaborations with industry partners. He leads the HEPSYCODE research group at DEWS, which focuses on developing methodologies and tools for hardware-software co-design of heterogeneous parallel dedicated systems. The group's current work includes extensions for real-time and mixed-criticality systems, frameworks for embedded system monitoring, and techniques for handling approximate computing and energy/power constraints within the design space exploration process.

Carlo CALLARIView profile

Associate Professor

• Rational Mechanics
• Structural Mechanics
• Geotechnical Engineering
• +5 more

Prof. Carlo CALLARI is an Associate Professor at the University of Molise, affiliated with the School of Biosciences and Territory. His research focuses on mechanics of solids and structures, rational mechanics, and tunneling. He specializes in computational mechanics, geotechnical engineering, and numerical modeling of quasi-brittle materials. His work includes developing advanced damage models, non-local gradient methods, and hydro-mechanical coupling analysis. Research Interests: Prof. CALLARI’s expertise spans structural mechanics, tunnel engineering, and computational methods. He has pioneered eikonal gradient-enhanced damage models for analyzing material failure in quasi-brittle systems. His studies also address historical engineering practices and modern tunneling innovations, bridging technical and historical perspectives. Publications: His recent work (2024–2020) emphasizes anisotropic damage models, numerical simulations of tunnel stability, and historical analyses of Roman and 19th-century infrastructure projects. His 2022 editorial on soil erosion highlights innovation in numerical methods for geotechnical hazards. Awards: No specific awards listed, but his contributions to computational mechanics and tunnel engineering are widely recognized in academic circles. Grants/Advising: No direct grants or student advisees listed in provided texts, but his active publication record suggests ongoing research collaborations. Labs/Teams: Engaged with computational mechanics and geotechnical engineering research groups at the University of Molise, focusing on numerical methods and infrastructure safety.

Milena D'AngeloView profile

Associate Professor

• Quantum Imaging
• Quantum Optics
• Photonics
• +3 more

Milena D'Angelo is an Associate Professor in the Department of Physics at the University of Bari (UniBA). She leads the Quantum Optical Technologies Lab, established through national competitive projects, which integrates experimental activities into the long-standing quantum research group at UniBA and INFN. Her work focuses on quantum imaging, including quantum optical lithography, ghost imaging with chaotic light, and correlation plenoptic imaging, with 5 patent applications. She has published over 55 papers, accumulating 1,700+ citations. Education: B.Sc. in Physics (UniBA, 1999); Ph.D. in Physics (University of Maryland, Baltimore County, 2004). Research Interests: Quantum imaging technologies, correlation-based imaging systems, plenoptic microscopy, and applications in quantum metrology. Her lab explores robust imaging techniques against environmental disturbances and develops high-resolution volumetric imaging methods. Recent work emphasizes GPU-based data processing, hyperspectral imaging, and turbulence-resistant systems. She has contributed to projects like the Qu3D initiative for quantum 3D imaging devices, advancing remote sensing and Earth observation applications. Her experimental innovations include light-field microscopy with correlated beams and correlation plenoptic tomography. Labs/Teams: Quantum Optical Technologies Lab (QuOTLab), collaborating on national and international projects to bridge quantum foundations with applied imaging technologies.

• Electromagnetic Fields
• RFID Technology
• Internet of Things (IoT)
• +3 more

Luca Catarinucci is an Associate Professor in Electromagnetic Fields (ING-INF/02) at the University of Salento's Department of Innovation Engineering. He leads courses in Microwaves and Electromagnetic Solutions for Hi-Tech , focusing on IoT, RFID, and 3D-printed electronic devices. His research spans electromagnetic interactions with biological systems, RFID sensor integration, and additive manufacturing for high-frequency applications. He has co-authored over 200 publications, including 80+ journal articles and 4 book chapters, and holds patents in RFID technology. His work bridges academic research and industry applications, emphasizing practical solutions for wireless communication and environmental monitoring. Education: Laurea in Electrical Engineering from the University of Perugia (1998). Professional roles include Researcher at the University of Salento (2004–2015) and collaboration with ICEMB (Interuniversity Center for Electromagnetic Field Studies). His expertise includes electromagnetic simulation, antenna design, and IoT-enabled systems. Research interests: RFID technology, IoT infrastructure, microwave engineering, 3D-printed antennas, and electromagnetic characterization of novel materials. Projects include smart waste management systems, thermal comfort monitoring, and worker safety solutions using RFID and sensor networks. Labs/Teams: Part of the Department of Innovation Engineering's research groups focused on electromagnetic design, IoT, and additive manufacturing. Collaborates with industry partners on projects involving 3D-printed RF devices and energy-autonomous sensors.