Find Academic Supervisors

• Computer Engineering
• Embedded Systems
• Fault Tolerance
• +4 more

Corrado De Sio is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN) , Politecnico di Torino , affiliated with the College of Computer, Film, and Mechatronics Engineering . His academic roles include course instruction and collaboration for Reconfigurable Computing , High Performance Computing (HPC) , and Operating Systems for High-Performance Supercomputers across multiple academic years (2020-2025). Research Interests focus on: Reliability of reconfigurable systems and FPGAs under radiation effects Hardware-software co-design for fault tolerance Embedded systems in aerospace and safety-critical applications Machine learning acceleration on reconfigurable hardware Radiation effects on real-time operating systems and CNN implementations Recent publications address: 2025: Selective hardening of RISCV soft-processors for space applications 2025: Real-time 'signal for help' gesture recognition systems 2024: Reliability analysis of RISC-V processors and CNN placement algorithms 2023: Fault tolerance in FPGA-based CNNs and radiation effects on RTOS Patents include: PyXEL - Python Toolkit for Reconfigurable Hardware Surveillance Software for Real-Time Violence Detection Academic Supervision : Co-supervisor for PhD candidate Arash Amini Bardpareh in Computer and Systems Engineering .

• High-Performance Computing
• GPU Acceleration
• Computational Geoscience
• +4 more

Ludovic Räss is a computational geoscientist at the University of Lausanne and lecturer at ETH Zurich's Glaciology Lab. His research intersects high-performance computing (HPC), geophysics, and applied mathematics, with specialization in GPU-accelerated scientific computing and supercomputing applications. He leads the GPU4GEO initiative developing multi-physics solvers and pioneers differentiable modeling techniques for geophysical simulations using Julia. Research focuses include: Portable HPC software development Ice dynamics and porous media deformation GPU-optimized computational methods Scalable simulation architectures Differentiable programming for geophysics He designed and teaches Solving partial differential equations in parallel on GPUs at ETH Zurich, providing hands-on training in GPU programming and Julia-based scientific computing. Contributes significantly to Julia's open-source ecosystem through JuliaGPU and JuliaParallel projects.

Laurence T. YangView profile

Professor

• parallel and distributed computing
• ubiquitous and pervasive computing
• embedded computing
• +3 more

Dr. Laurence T. Yang is a Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology, China, and Department of Computer Science of St. Francis Xavier University, Canada. He maintains active research and professional service commitments across both institutions with significant contributions to computing disciplines. His research spans parallel and distributed systems, embedded platforms, ubiquitous/pervasive computing environments, sensor networks, supercomputing infrastructure, and scalable computing methodologies. These interests drive his extensive publication record and leadership in advancing computational theory and applications. Dr. Yang has received numerous accolades including: Honorable Mention, PROSE Awards for "Mobile Intelligence" (2010) Best Paper Award at IEEE AINA-06 (2006) Best Paper Nomination Distinguished Achievement Awards (2005, 2011) Canada Foundation for Innovation Award (2003) Multiple Best Paper Awards at international conferences His professional service includes chairing the IEEE Technical Committee of Scalable Computing (2008-2011), leading the IEEE Task Force on Ubiquitous Computing and Intelligence (2009-present), and serving on Compute Canada's National Resource Allocation Committee (2009-present). As editor-in-chief for multiple journals and author/editor of over 25 books, he has shaped scholarly discourse while delivering approximately 20 keynote addresses globally.

Marco AldinucciView profile

Professor

• Parallel Computing
• High-Performance Computing (HPC)
• Cloud Engineering
• +7 more

Marco Aldinucci is a Full Professor and Head of the Parallel Computing group at the University of Torino's Computer Science Department. He leads the HPC Key Technologies and Tools (HPC-KTT) national lab under CINI, involving 38 Italian universities. His expertise spans parallel programming models, HPC systems, federated learning, and energy-efficient computing. Aldinucci has secured over €10M in EU research funding, contributed to frameworks like Fastflow and Streamflow, and pioneered initiatives like the HPC4AI lab and the CINI HPC-KTT lab. His research focuses on advancing exascale computing, cloud-HPC integration, and AI-driven medical solutions. Notable projects include the Gaia AVU-GSR solver for exascale systems and the DeepHealth Toolkit for medical AI. He has held governance roles in EuroHPC and chairs the Observatory on Trends and Applications of Supercomputing in Italy. Aldinucci’s publications (150+) address parallel algorithms, distributed learning, and sustainable HPC infrastructure. His work has been recognized with awards from HPC Advisory Council, NVIDIA, IBM, and Autodesk. Current initiatives include the Software & Integration lab at the Italian National HPC Centre (ICSC) and leadership in the OpenScience working group at Torino. His advising includes Iacopo Colonelli, whose thesis won CINI’s 2023 best award. He actively engages in EU projects, workflow systems, and standards for hybrid computing environments. Aldinucci’s labs and collaborations drive innovations in HPC portability, energy efficiency, and AI scalability.

• 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.

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.

• Computational Materials Science
• Tribology
• Surface and Interface Physics
• +3 more

Maria Clelia Righi is Full Professor at the Department of Physics and Astronomy “Augusto Righi” of the University of Bologna, Italy. She also serves as Visiting Professor at Imperial College London and acts as scientific advisor to TotalEnergies. Her research centres on computational methods for predicting materials behaviour, with a focus on surface and interface phenomena and tribology. Education 2004 – PhD in Physics, University of Modena and Reggio Emilia 1998 – Laurea Degree in Physics (Cum Laude), University of Modena and Reggio Emilia Research Interests Maria Clelia Righi develops and applies ab-initio and multiscale computational techniques to understand how mechanical stress activates chemical reactions at interfaces and to design advanced lubricants and low-friction materials. Her group pioneered first-principles tribochemistry modelling and collaborates with leading experimental laboratories and multinational companies. Major funded initiatives include the ERC Consolidator Grant “SLIDE” (2020-2025) and the HORIZON-RIA project STORMING, where she coordinates the modelling work-package on CO₂-free methane cracking reactors. Scientific Awards & Distinctions ERC Consolidator Grant 2019 – SLIDE PRACE and DEISA supercomputing allocations (multiple) Marie Curie Individual Fellowship PATCHES – supervisor Supervision & Grants She has supervised 13 PhD students, 19 MSc students, 10 post-doctoral researchers and 2 industry-seconded researchers. Her group has secured funding from the EU (ERC, H2020, Horizon Europe, PRACE), Italian regional authorities and industry contracts with Toyota Central R&D Labs and Total Marketing Services. Editorial & Professional Service Member of the editorial boards of Scientific Reports , Lubricants , Lubrication Science and Coatings .

• High Performance Computing
• Grid Computing
• Cloud Computing
• +7 more

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.

Ritamaria BucciarelliView profile

Adjunct Professor

• Natural Language Processing
• Computational Linguistics
• Digital Humanities
• +3 more

Ritamaria Bucciarelli is an Adjunct Professor at Ca' Foscari University of Venice, affiliated with the Department of Comparative Linguistic and Cultural Studies (DSLCC) and the Research Institute for Digital and Cultural Heritage. She holds a PhD in Language Sciences (2008) and multiple advanced degrees in Literature, Pedagogy, and Natural Sciences. Her research focuses on Natural Language Processing (NLP), Digital Intelligence, and computational linguistics, with projects involving quantum physics applications and digital text analysis. She has taught Italian linguistics and language courses internationally, including at the University of Pisa and LUISS Guido Carli. Her work includes developing tools like Acro Word and Type Race for automated text generation and translation. Awards include the ESB Level 3 English certification with Distinction (2019) and recognition as top-performing in an online teaching course (2002). She has led research teams in Italy and France, collaborating on projects such as sentiment analysis and digital text cloud workshops. Bucciarelli also served as a MIUR inspector for secondary schools and has extensive experience in blended learning and e-learning platform management.

Loris BelcastroView profile

Researcher

• cloud computing
• edge computing
• big data
• +4 more

Loris Belcastro, PhD, is a researcher in computer engineering at the University of Calabria, Italy, affiliated with the Department of Electronics, Computer Science and System Sciences (DIMES). He earned his PhD in Information and Communication Engineering from the same institution and serves on editorial boards for journals like the Journal of Supercomputing and SN Computer Science. In 2024, he obtained the Italian National Academic Qualification as Associate Professor in Computer Engineering. His research focuses on cloud and edge computing big data analytics machine learning social media analysis high performance computing parallel/distributed data analysis His recent publications highlight trends in edge-cloud continuum integration, distributed machine learning, and scalable big data analysis. He has contributed to frameworks for urban mobility prediction, industrial IoT applications, and HPC optimization through libraries like ParSoDA-Py. He has served as guest editor for journals such as Future Generation Computer Systems and Sensors, reflecting his expertise in distributed systems and data analysis.

Francesco Di RenzoView profile

Associate Professor

• Theoretical Physics
• Quantum Field Theory
• Lattice Gauge Theory
• +6 more

Francesco Di Renzo is Associate Professor of Theoretical Physics at the University of Parma's Department of Mathematical, Physical and Computer Sciences. Appointed faculty in 2001 and promoted to associate professor in 2016, he holds concurrent research appointments with INFN and coordinates the €4M EuroPLEx H2020 network. His academic background includes: Laurea (Master's equivalent) in Physics with honors, University of Parma PhD in Physics, University of Parma Di Renzo's research centers on lattice formulations of quantum field theories with emphasis on fundamental interactions. He pioneers computational approaches to critical phenomena using Lee-Yang zero analysis and thimble regularization to address sign problems in QCD. His work integrates high-performance computing architectures with emerging AI techniques for non-perturbative physics simulations. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Application of multipoint Padé approximants to locate QCD critical points through Lee-Yang edge singularities, (2) Development of Numerical Stochastic Perturbation Theory (NSPT) for O(N) sigma models at large N, and (3) Cross-disciplinary fusion of machine learning with lattice field theory for phase transition analysis. He currently supervises three PhD students and has served on the Doctoral Studies Committee since 2000. His grant leadership includes EuroPLEx (H2020 scientific coordinator), STRONGnet (FP7 Parma unit lead), and multiple PRIN projects where he coordinated research units. As an INFN-appointed researcher, he directs the QCDLAT initiative. Di Renzo actively contributes to international infrastructure through the International Lattice Data Grid (where he serves as Italian representative and former chairman) and the INFN SUMA supercomputing project. He previously led the AuroScience initiative and currently shapes UNIPR's high-performance computing strategy as committee member.

• Optimization Techniques
• Machine Learning
• High-Performance Computing (HPC)
• +3 more

Andrea Borghesi is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering (DISI), University of Bologna, Italy. His research focuses on optimization techniques, Machine Learning, and energy-efficient computing in High-Performance Computing (HPC) systems. He holds an M.S. (2013) and Ph.D. (2017) in Computer Engineering from the University of Bologna, and has been awarded the Italian National Scientific Qualification for Associate Professor in Computer Engineering (2018-2020). **Employment History**: 2020–Present: Executive Scientific Representative at ALMA-AI (Inter-departments Center for Artificial Intelligence, University of Bologna) 2019–Present: Assistant Professor (RTDA) at DISI, University of Bologna 2018–2019: Postdoctoral Fellow at DISI 2017–2018: Research Intern at University of Bologna’s Department of Electrical, Electronic and Information Engineering **Research Interests**: Borghesi’s work addresses complex systems, predictive maintenance, and energy efficiency. Key areas include HPC scheduling, AI-driven monitoring, and interdisciplinary AI applications. His projects span energy-aware scheduling, federated learning for anomaly detection, and AI fairness in healthcare and education. **Grants & Projects**: Lead or contributed to EU-funded initiatives such as AEQUITAS (AI fairness), StairwAI (AI accessibility), and OPRECOMP (transprecision computing), alongside national projects like vertOpt (AI application matching) and EXADATA (exascale monitoring). **Labs & Collaborations**: Active in the Inter-departments Center for Artificial Intelligence (ALMA-AI) and collaborated with institutions like CINECA and industry partners such as ENI and YANMAR. His work integrates AI, optimization, and simulation to solve real-world challenges in computing and healthcare.

Biagio CosenzaView profile

Associate Professor

• High-performance computing
• Compiler technology
• Software optimization
• +7 more

Biagio Cosenza is an Associate Professor at the Department of Computer Science, University of Salerno, Italy. He leads research in high-performance computing, compiler technology, and software optimization. Previously, he was a Senior Researcher at TU Berlin (2015-2019) and a Post-Doctoral Researcher at the University of Innsbruck, Austria (2011-2015). His educational background includes: Ph.D. from University of Salerno (2011), supervised by Prof. Vittorio Scarano Dr. Cosenza's research focuses on creating efficient programming models for heterogeneous computing systems. His work spans compiler technology, automatic performance tuning, and energy-efficient computing approaches. He has made significant contributions to SYCL-based programming models and MPI implementations, with emphasis on portability across diverse hardware platforms including GPUs and accelerators. His research often bridges theoretical computer science with practical applications in scientific computing and bioinformatics. His recent publications demonstrate a strong trend toward heterogeneous and distributed computing, with particular emphasis on energy efficiency, performance portability, and SYCL-based programming models. Many papers focus on applications in drug discovery and scientific simulations, showing how his theoretical work translates to real-world scientific problems. Dr. Cosenza has received several prestigious recognitions: Best Paper Award at the 14th BenchCouncil International Symposium (2022) Elevated to Senior Member of the IEEE (2022) Recognized as ACM Senior Member (2022) He leads multiple significant research projects including the PRIN 2022 project "LibreRT" and the EuroHPC project "LIGATE." His work has secured substantial funding from sources including the German Research Foundation (DFG), the Italian Ministry of Education, University and Research, and EuroHPC. He actively mentors students and collaborators on advanced topics in high-performance computing. Dr. Cosenza is a member of the ISIS Lab at the University of Salerno and contributes to open standards through his work with the Khronos Group and SYCL Working Group. His research group develops tools like CELERITY, a C++ SYCL-based programming model for accelerator clusters, and EMPI, an enhanced message passing interface in modern C++.

• Theoretical Physics
• Spin Glass Theory
• Statistical Mechanics
• +3 more

Andrea Maiorano serves as an Associate Professor at the Department of Biotechnology, Chemistry and Pharmacy at the University of Siena. His academic appointments include teaching Physics courses for the Pharmaceutical Chemistry and Technology program (5-year cycle) and Chemical Sciences (1st cycle degree), with teaching responsibilities extending through the 2025/2026 academic year. His research focuses on theoretical aspects of spin glass systems, specializing in non-equilibrium dynamics, memory effects, and critical phenomena in disordered magnetic systems. Maiorano's work bridges theoretical physics with computational approaches, examining complex behaviors like temperature chaos, multifractality, and aging dynamics in spin glasses through extensive numerical simulations. Analysis of his 15 most recent publications reveals a consistent focus on spin glass theory, with particular emphasis on memory effects, length scale dependencies, and non-equilibrium dynamics. His research demonstrates strong integration between theoretical modeling, computational simulations, and experimental validation across multiple physical systems. His scientific contributions include significant work on: Memory and rejuvenation phenomena in spin glasses Temperature chaos in off-equilibrium dynamics Scaling laws for spin-glass responses Mpemba effect as a persistent memory phenomenon Strong ergodicity breaking in aging systems Maiorano maintains active research collaborations with international physics groups, as evidenced by his co-authorship on numerous publications in high-impact journals including Physical Review Letters and Nature Physics. His computational work leverages specialized hardware like the Janus supercomputer architecture designed for spin-glass simulations.

Riccardo CantiniView profile

Assistant Professor

• Deep Learning
• Large Language Models
• Sustainable AI
• +3 more

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).