جستجوی استاد راهنمای دانشگاهی

Iacopo Colonnelliمشاهده پروفایل

استادیار

• Scientific Workflow Systems
• High-Performance Computing
• Cloud Computing
• +۶ مورد دیگر

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 Corsoمشاهده پروفایل

دانشیار

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +۳ مورد دیگر

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
• +۶ مورد دیگر

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

• Data Logistics
• Exascale Computing
• High Performance Computing
• +۷ مورد دیگر

François Bodin is a Professor at University of Rennes 1, actively involved in research at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires). He leads the Logica research team which focuses on data logistics—developing methods to organize distributed processing chains while considering technical performance, environmental sustainability, data governance, and long-term viability. His work spans multiple interdisciplinary projects including AQMO for air quality monitoring, RUDI for metropolitan open data platforms, and Numpex for Exascale computing. His research interests primarily focus on: Data Logistics and Distributed Processing Systems Exascale and High Performance Computing Sustainable and Energy-Efficient Computing Practices Open Data Platforms for Smart City Applications Parallel Programming and Software Optimization Professor Bodin's recent work demonstrates a strong commitment to reconciling digital performance with environmental sustainability. His projects like AQMO (air quality monitoring) and RUDI (metropolitan data platform) showcase practical applications of his research in urban environments. The Numpex project represents his leadership in preparing French scientific and industrial applications for exascale computing capabilities. Notably, he has made a personal commitment to environmental sustainability by declaring 'For environmental reasons, starting the 01/01/2020 I will not take the plane anymore.' His scientific contributions span several key areas: Exascale computing preparation and software development Metropolitan open data platforms and governance Energy-efficient computing practices Astronomical data processing through the ECLAT laboratory Professor Bodin actively participates in research recruitment and collaborates with various institutions including CNRS, Inria, and industry partners to advance his field. His work bridges theoretical computer science with practical urban and environmental applications, making significant contributions to both academic research and real-world problem solving.

• Numerical Analysis
• Machine Learning
• Optimization
• +۳ مورد دیگر

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 Pomanteمشاهده پروفایل

استادیار

• Electronic System-Level HW/SW Co-Design
• Heterogeneous Parallel Systems
• Embedded Systems
• +۵ مورد دیگر

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 CALLARIمشاهده پروفایل

دانشیار

• Rational Mechanics
• Structural Mechanics
• Geotechnical Engineering
• +۵ مورد دیگر

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'Angeloمشاهده پروفایل

دانشیار

• Quantum Imaging
• Quantum Optics
• Photonics
• +۳ مورد دیگر

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)
• +۳ مورد دیگر

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.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

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.

• High Performance Computing
• Grid Computing
• Cloud Computing
• +۷ مورد دیگر

Giovanni Aloisio is a Full Professor of Information Processing Systems at the Department of Innovation Engineering, University of Salento, Italy. He is a leading figure at the Euro-Mediterranean Center on Climate Change (CMCC), where he serves as Director of the Supercomputing Center and Director of the Scientific Computing and Operations (SCO) Division. He is also a member of CMCC’s Governance bodies, including the Strategic Council and Executive Committee. His research focuses on High Performance Computing (HPC) , grid and cloud computing , and distributed data management . He has played a pivotal role in major European and international initiatives such as the European Grid Forum (Egrid), the EU FP7 IS-ENES projects, the European Exascale Software Initiative (EESI), and the International Exascale Software Project (IESP). His work bridges advanced computing infrastructure with Earth system modeling and climate science. Recent publications highlight a strong trend toward integrating HPC simulations , big data analytics , and machine learning in climate science. His team develops tools like PyOphidia for scalable data analytics and explores AI-driven methods for weather and climate modeling, pandemic forecasting, and environmental data downscaling. The research spans interdisciplinary domains including climate modeling, epidemiology, and environmental health. He has authored over 100 peer-reviewed journal articles. His leadership in EU-funded projects and international working groups, such as the ENES HPC Task Force and the WCES Working Group, reflects his significant contributions to the scientific community. He leads the HPC laboratory at the University of Salento and oversees advanced computing operations at CMCC. His work supports large-scale scientific workflows, climate data spaces within the European Open Science Cloud, and the convergence of HPC, AI, and data analytics.

• Quantum Computing
• Machine Learning
• Parallel and Distributed Systems
• +۱ مورد دیگر

Giacomo Vitali is a Ph.D. candidate in Computer and Systems Engineering at the Politecnico di Torino , Department of Control and Computer Science (DAUIN), while also serving as a Lecturer at the same institution. His research focuses on Quantum Computing , Machine Learning , and Parallel and Distributed Systems . Education: M.Sc. in Physics of Fundamental Interactions (2018) from the University of Pisa Research: Pioneering hybrid quantum-classical algorithms for industrial applications, including BBQ-mIS and DEN models for graph problems and quantum hardware optimization Teaching: Contributing to courses on Quantum Hardware Design and Quantum Computing at the M.Sc. level, as well as undergraduate courses in Computer Science

Stefano Querمشاهده پروفایل

دانشیار

• Computer Science
• Electrical Engineering
• Formal Methods
• +۷ مورد دیگر

Stefano Quer is an Associate Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He holds a PhD in Electronic Engineering from the same institution and has been affiliated with DAUIN since 1996. Researcher (1996-2000) Associate Professor (2000-present) Visiting Faculty at UC Berkeley (1994-1995) Research Interests His work spans Formal Verification BDD/SAT Techniques Embedded Systems Hardware/Software Co-Verification Parallel Computing with applications in VLSI CAD, industrial IoT, and energy-efficient systems. Recent articles focus on GPU-accelerated graph algorithms, wireless sensor calibration, and AI-driven test optimization. Scientific Awards Best Paper Award, IEEE EURO-DAC'94 Academic Contributions Supervised PhD students Lorenzo Cardone and Andrea Calabrese Member of DATE, ICSOFT Technical Program Committees Topical Advisor for Sensors MDPI 60+ publications in IEEE/ACM venues

• Numerical Analysis
• Machine Learning
• Computational Mathematics
• +۴ مورد دیگر

Elena Toscano is a researcher in the Department of Mathematics and Computer Science at the University of Palermo. She specializes in numerical analysis, machine learning, and computational mathematics, with a focus on mesh-free methods like Smoothed Particle Hydrodynamics (SPH) and applications to physics, engineering, and interdisciplinary fields. Teaching: Numerical Analysis (Master's in Informatics and Mathematics, 2025/2026) Research Areas: Signal/image processing, SPH consistency restoration, genetic algorithms for tomography, and mathematical-literary collaborations (e.g., Oulipo). Her publications span computational physics, machine learning, and mathematical modeling, emphasizing numerical stability and interdisciplinary innovation.

• Combustion Theory
• Turbulent Flame Propagation
• Hydrogen and Ammonia Combustion
• +۵ مورد دیگر

Francesco Creta serves as Associate Professor in the Department of Aeronautical and Space Engineering at Sapienza University of Rome's School of Engineering. His research focuses on advanced combustion phenomena with emphasis on hydrogen and ammonia fuels for decarbonization applications. His primary research interests include thermodynamic instability in premixed flames , turbulent combustion modeling , and rocket propulsion systems . Creta investigates fundamental flame behaviors through direct numerical simulation (DNS) and develops data-driven models for thermodiffusively unstable flames, particularly examining hydrogen-ammonia mixtures for sustainable energy applications. His work bridges theoretical combustion physics with practical engineering solutions for gas turbines and liquid rocket engines. Analysis of his 15 most recent publications reveals a strong trend toward hydrogen-ammonia combustion for decarbonization, with significant focus on flame instability mechanisms (particularly Darrieus-Landau and thermodiffusive effects), data-driven modeling approaches , and rocket engine applications . His research increasingly integrates machine learning techniques with traditional combustion modeling to address complex flame behaviors under high-pressure conditions. Creta maintains active research collaborations with leading combustion scientists across Europe, evidenced by co-authorship on high-impact publications in Proceedings of the Combustion Institute , Combustion and Flame , and Physics of Fluids . His work receives consistent recognition through publication in top combustion venues and presentation at major international conferences including the European Combustion Meeting and AIAA SciTech Forum. His research group conducts advanced numerical simulations of combustion systems, with particular expertise in liquid rocket engine modeling, transcritical flows, and flame instability analysis. Current projects focus on enabling hydrogen and ammonia combustion for gas turbine retrofitting and next-generation propulsion systems, addressing critical challenges in flame stability and pollutant formation.