Find Academic Supervisors

• Artificial Intelligence
• Robotics
• Sensitivity Informatics
• +7 more

Professor Shohei Kato holds a primary appointment at Nagoya Institute of Technology's Graduate School of Engineering (Omohi College) and serves as a Visiting Researcher at the National Center for Geriatrics and Gerontology. His career includes prior roles as Associate Professor at Nagoya Institute of Technology (2002–2015), Lecturer at Toyota National College of Technology (1999–2002), and Research Assistant (1998–1999). He earned his Doctor of Engineering from Nagoya Institute of Technology, where he also completed undergraduate studies in Electrical and Computer Engineering. Kato's research integrates artificial intelligence, robotics, and affective computing, with applications spanning healthcare, optimization, and human-machine interaction. Key themes include kansei (sensitivity) robotics, computational intelligence, dementia screening via speech/fNIRS analysis, and multi-agent systems. His work emphasizes adaptive algorithms, emotion modeling, and real-world AI deployment. Recent publications focus on sim-to-real transfer learning, reinforcement learning for multi-agent cooperation, and non-invasive dementia diagnosis. A consistent trend involves applying particle swarm optimization and deep learning to industrial and biomedical challenges, such as shield tunneling automation and EEG-based emotion recognition. Awards & Honors: Minister of Education Commendation for Science and Technology (2015) IEEE Outstanding Paper Awards (GCCE 2017, 2018) 日本感性工学会論文賞 (2012) Multiple Workshop on Informatics (WiNF) prizes for student collaboration (2011–2020) Kato leads externally funded projects on dementia screening, affective robotics, and optimization algorithms. He directs the Kato Lab (katolab.nitech.ac.jp), collaborating with entities like Shimizu Corporation on AI-driven construction planning. His team explores human-robot symbiosis, BCI interfaces for Duchenne muscular dystrophy patients, and therapy robot design.

Alessio OrsinoView profile

Research Fellow

• Big Data Analysis
• Distributed Computing
• Edge-Cloud Computing
• +5 more

Alessio Orsino is a Research Fellow at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) at the University of Calabria. His academic journey includes a Ph.D. in Information and Communication Technologies (ICT) awarded in January 2025, a Master’s in Computer Engineering (Big Data Analytics and Data Science) from 2020, and a Bachelor’s in Computer Engineering from 2018, all from the University of Calabria. He also served as a visiting researcher at the University of Cambridge’s Department of Computer Science and Technology (July–December 2023). His research focuses on big data analysis, distributed computing, machine learning, and edge-cloud integration. He contributes to projects like ASPIDE (Exascale programming models) and INSIDER (cloud-edge IoT deployment optimization), funded by EU Horizon 2020 and Italy’s MIUR. He is a contract professor at DIMES and a member of the Scalable Computing and Cloud Laboratory (SCALab). Orsino’s work bridges theoretical and applied computing, emphasizing efficient machine learning on resource-constrained systems and real-time adaptive models for edge environments. His publications address edge-cloud continuum challenges, adversarial robustness in AI, and scalable big data frameworks. Key grants include Horizon 2020 (ASPIDE) and PRIN 2022 (INSIDER), supporting his interdisciplinary research in hybrid cloud-edge infrastructures and data-intensive applications. He has authored/co-authored a book on big data programming and collaborates internationally through visiting positions and lab engagements.

• Information Processing Systems
• Neural Networks
• Embedded Systems
• +3 more

Musci Myrtle is a Researcher at the Department of Industrial and Information Engineering, University of Pavia. Their work focuses on information processing systems, neural networks, and embedded systems applications in healthcare and cultural heritage preservation. They have contributed to 3D modeling of historical artifacts and developed neural network architectures for wearable devices. Research interests include interdisciplinary topics such as digital heritage documentation, real-time health monitoring systems, and scalable neural network architectures. Publications span conference proceedings, book chapters, and peer-reviewed journals in neural networks and information systems. Teaching activities are documented but lack specific details in the provided materials. No awards or grants are explicitly mentioned.

Giuliano GrossiView profile

Associate Professor

• Sparse representation models
• Signal processing
• Face recognition
• +5 more

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

• Cloud Computing
• Fog Computing
• SDN Microservices
• +7 more

Giuseppe Di Modica is an Associate Professor at the Department of Computer Science - Science and Engineering, University of Bologna. His research focuses on cloud computing, edge/fog systems, IoT, and digital twins for industrial applications. He has contributed to frameworks like IoTwins and H2F, addressing challenges in distributed computing, microservice orchestration, and latency optimization in SDN architectures. His work spans containerized application management, geo-distributed big data processing, and security in serverless environments. Key research themes include: Cloud-edge continuum orchestration IoT service placement in fog environments SDN microservice architecture design Lightweight virtualization for Industry 4.0 Validation of performance models for microservices His publications emphasize practical implementations, such as the Torch orchestrator for multi-cloud deployments, and frameworks for fault-aware cloud provisioning. Ongoing work explores digital twin platforms for manufacturing and smart facility management, leveraging edge-cloud synergy and real-time data analytics. He holds a permanent academic position with no part-time designation noted. Contact: giuseppe.dimodica@unibo.it, +39 051 20 9 3866.

• Computer architectures and Computer aided design
• Data science
• Computer vision and AI
• +3 more

Walter Gallego Gomez is a PhD Student in Computer and Systems Engineering (39th cycle, 2023-2026) and an External Lecturer/Teaching Assistant at the Department of Control and Computer Science (DAUIN) of the Polytechnic University of Turin. His academic and research journey is supported by an EBRAINS-Italy research grant. Bachelor's in Electronic Engineering (University of Antioquia, 2015) Master's in Computer Engineering (Polytechnic University of Turin, 2018) His research intersects life sciences and computer science, focusing on bioinformatics algorithms, neuromorphic hardware (Loihi2/Lava), and their implementation. He collaborates with the Electronic Design Automation (EDA) group and external entities like Candiolo Oncology Center and Telluride Neuromorphic Cognition Engineering workshop. Teaching roles include System-on-chip architecture (2024/25) and Applied AI and machine learning (2023/24-2024/25) as a course collaborator. Recent publications highlight trends in somatic structural variant detection and neuromorphic ecosystem benchmarking. His work spans automotive embedded systems (Marelli, 2018-2021), academic research assistantship (2021-2023), and interdisciplinary collaborations.

• Computer architectures
• Computer-aided design
• Data science
• +7 more

Robert Alexander Limas Sierra is a Lecturer at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, Italy. He is also a PhD student in Computer and Systems Engineering, 38th cycle (2022-2025), affiliated with the CAD research group and an IEEE student member. B.S./M.Sc. in Electronic Engineering (2019/2022) - Universidad Pedagógica y Tecnológica de Colombia His research focuses on computer architectures, AI/ML, GPU computing, and hardware reliability . Key themes include neural network robustness, tensor core optimization, and fault tolerance in AI accelerators. Recent publications address self-testing libraries for GPUs, CNN error mitigation, and reliability implications of edge computing. Teaching activities include collaboration in Computer Sciences and Automotive Engineering courses (2023/24 and 2024/25 academic years).

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++.

Vincenzo BonniciView profile

Associate Professor

• Bioinformatics
• Artificial Intelligence
• Computational Biology
• +3 more

Vincenzo Bonnici is an Associate Professor in Informatics at the Department of Mathematical, Physical and Computer Sciences , University of Parma, Italy. His academic career includes a PhD in Computer Science from the University of Verona (2015), preceded by a master’s degree from the University of Catania (2011). He has held research positions at prestigious institutions, including the Institute for Genomics and Bioinformatics (IGB) , University of California, Irvine (2013–2014). Education BSc/MSc in Computer Science from University of Catania (2008/2011) PhD in Computer Science from University of Verona (2015) His research focuses on bioinformatics and computational biology , with a strong emphasis on subgraph matching algorithms for biomedical graphs, genomic sequence analysis using information theory, and parallel computing for biological networks. His work spans pangenomics, phylogenomics, and non-coding RNA studies, with applications in GPU and SMP architectures. The article trends reflect his core expertise in graph algorithms and computational genomics. Key contributions include MULTI-GRAPHMATCH (2025) for multigraph analysis, ARC-MATCH (2024) for edge domain-based graph querying, and foundational work on pangenome discovery (e.g., PANDELOS series, 2023). Parallel computing and information theory are recurring themes across his publications. Scientific awards include the ICPR 2014 international graph-matching contest and a best poster award at the Jacob T. Schwartz International School for Scientific Research. He has served as a speaker at 12 international conferences. Teaching includes courses on Artificial Intelligence Algorithms (2025/2026), Artificial Intelligence Laboratory , and Software Engineering at the University of Parma. His work integrates algorithmic innovation with biological data analysis.

• Battery Management Systems
• Electrochemical Impedance Spectroscopy
• Machine Learning Applications
• +7 more

Alessio De Angelis is a researcher affiliated with the University of Perugia, focusing on advanced measurement systems, battery technology, and localization techniques. His work spans disciplines such as electrical engineering, machine learning, and IoT applications. University: University of Perugia Email: alessio.deangelis@unipg.it His research interests include: Battery management and state-of-charge estimation Uncertainty quantification in AI-based systems Ultra-wideband (UWB) and magnetic localization Wireless sensor networks for environmental monitoring Signal processing for quantized and constrained data IoT applications in structural and health monitoring Recent publications highlight his expertise in battery diagnostics, localization algorithms, and AI-driven measurement systems. Key trends involve integrating machine learning with electrochemical impedance spectroscopy (EIS) and optimizing low-complexity sensor architectures.

Stefano CaselliView profile

Professor

• Autonomous Robotics
• Real-Time Systems
• Parallel and Distributed Computing
• +4 more

Stefano Caselli is a Full Professor of Computer Engineering at the School of Engineering , University of Parma , Italy. He teaches courses in operating systems, real-time systems, digital design, and robotics. His academic journey includes a Laurea in Electronic Engineering (1982) and a Ph.D. in Computer and Electronic Engineering (1987) from the University of Bologna, followed by a visiting scholar position at the University of Florida's Machine Intelligence Laboratory (1989-1990). Current Role: Full Professor, Department of Information Engineering Leadership: Former Chairman of Electronic, Computer, and Telecommunications Engineering degree programs (2000-2006), currently Vice-Chairman of the Ph.D. school in Information Technology and director of a French-Italian double-degree exchange program. Research Focus : His work spans autonomous robotic systems , real-time architectures , human-robot interaction , and edge-to-cloud computing . He has led EU-funded projects like VAALID (2015-2020) and regional initiatives such as AERTECH (2015-2020), emphasizing robotics in industrial automation and rehabilitation. Publications & Collaborations : With over 100 papers in top journals and conferences (e.g., IEEE ICRA, IROS), he explores robotics, precision agriculture, and sensor integration. His Robotics Laboratory (co-founded in 1992) collaborates with local companies on industrial automation solutions. Teaching & Administration : He has served on scientific committees for IEEE ICRA and IROS conferences and is a member of IEEE and ACM . Office hours are conducted via Teams during the pandemic, with physical meetings resuming in Building 1, University of Parma.

Michele TomaiuoloView profile

Associate Professor

• Social Media Analysis
• Peer-to-Peer Networks
• Security and Trust Management
• +5 more

Michele Tomaiuolo is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. He has been actively teaching undergraduate and graduate courses in computer science and engineering since at least 2014, with current teaching assignments for the 2025/2026 academic year. Dr. Tomaiuolo holds a Master's degree in Computer Engineering and a PhD in Information Technology, both from the University of Parma. His educational background has provided the foundation for his extensive career in academic research and teaching in the field of computer science. Professor Tomaiuolo's research spans several interconnected domains within computer science and information systems. His primary focus is on social media analysis and peer-to-peer social networks, with particular attention to security and trust management. His work also encompasses multi-agent systems, semantic web technologies, rule-based systems, and peer-to-peer networking architectures. More recently, his research has expanded into sentiment analysis applications for digital libraries and educational contexts, as well as fine-grained agent-based modeling for epidemiological simulations. His interdisciplinary approach bridges theoretical computer science with practical applications in information systems and social computing. An analysis of Professor Tomaiuolo's recent publications (2020-2024) reveals several key trends in his research. There is a strong emphasis on social network analysis, particularly in the context of sentiment analysis and user behavior. His work frequently employs agent-based modeling techniques to address complex problems ranging from pandemic spread prediction to digital library user experience. The integration of artificial intelligence methods with traditional computer science approaches is evident across his publications, with applications spanning digital libraries, educational technology, and social media platforms. His research demonstrates a consistent focus on practical implementations of theoretical concepts, often addressing real-world challenges through computational solutions. Professor Tomaiuolo has participated in various research projects, including the EU-funded @lis TechNet, Agentcities, Collaborator, and Comma projects, and the nationally funded Anemone project. These collaborative efforts have provided the foundation for much of his published research. His teaching portfolio is extensive, covering fundamental computer science concepts, object-oriented programming, software engineering, computer networks, mobile code, and security. Current courses for the 2025/2026 academic year include Fundamentals of Computer Science, Computer Science and Programming Laboratory, and Paradigms and Languages for Data Analysis across various engineering programs. Professor Tomaiuolo is actively involved in the academic community, serving as a reference professor and tutor for undergraduate engineering programs at the University of Parma. His work with the CSBNO Consortium demonstrates his commitment to applying computational methods to enhance library services and user experiences.

Tommaso CucinottaView profile

Associate Professor

• Cloud Computing
• Real-Time Systems
• Artificial Intelligence
• +7 more

Tommaso Cucinotta is an Associate Professor at the Real-Time Systems Laboratory (ReTiS) within the TECIP Institute of Scuola Superiore Sant'Anna, Pisa, Italy. He earned a MSc and PhD in Computer Engineering from University of Pisa and Scuola Superiore Sant'Anna, respectively. His career spans academic and industrial roles, including researcher positions at Alcatel-Lucent Bell Labs (2012-2014) and Software Development Engineer at Amazon DynamoDB (2014-2016). He coordinates real-time and embedded systems research at ReTiS since 2019. Born in 1974, Potenza, Italy MSc in Computer Engineering, University of Pisa (2000) with 110 cum laude PhD in Computer Engineering, Scuola Superiore Sant'Anna (2004) His research focuses on real-time systems in cloud environments, including adaptive resource management, AI-driven performance monitoring, secure computing, and scalable NoSQL databases. He explores operating system innovations for many-core architectures, network function virtualization (NFV) optimization, and kernel-level enhancements for latency control. His work integrates formal methods with practical implementations, such as autonomic QoS control and high-performance container communication frameworks. Recent publications analyze predictive elasticity in cloud infrastructures, real-time DAG optimization on heterogeneous platforms, and AI applications for system-level performance tuning. He actively contributes to open-source tools like ARSim and AQuoSA, while mentoring MSc thesis projects on topics like Kubernetes optimization, fault-tolerant replication logs, and machine unlearning techniques for LLMs. Collaborations with industry leaders (Ericsson, Red Hat, Vodafone) bridge academic research with real-world scalability challenges. Scientific awards include the Best Paper Award at CLOSER 2020 for his work on high-performance inter-container communication frameworks. He participates in program committees of major conferences and contributes to the evolution of Linux real-time scheduling mechanisms through projects like SCHED_DEADLINE enhancements for multimedia applications.

• Physics Education
• Optics
• Fluid Dynamics
• +6 more

Luigi Gratton is a Visiting Professor at the Department of Physics, University of Trento. His work focuses on innovative educational approaches in physics, particularly through low-cost experimental setups and digital technology integration . Research Interests: Physics Education Quantum and Wave Optics Fluid Dynamics Climate Change Studies Human Perception in Physics Key Publication Trends: Development of smartphone-based spectrometers and diffraction experiments Classical analogs to quantum phenomena Home laboratory methodologies during distance learning FFT applications in introductory physics Analogous demonstrations for complex concepts Gratton's experiments often combine computational analysis (e.g., Tracker app, FFT) with physical setups using everyday materials. His collaborations with Stefano Oss, Pasquale Onorato, and Alessandro Salmoiraghi highlight interdisciplinary team efforts.

Emilio CrucianiView profile

Assistant Professor

• Algorithms
• Data Mining
• Social Networks
• +9 more

Emilio Cruciani is a Tenure Track Assistant Professor in Computer Science at the European University of Rome, affiliated with the Department of Human Sciences. He holds a PhD in Computer Science from the Gran Sasso Science Institute (2019) and has conducted research at universities in Germany, France, and Austria. His research focuses on algorithms, data mining, and social network dynamics. Current work investigates local network interactions for graph clustering, stochastic processes on networks, and scalable algorithms for large datasets. He has contributed to Green AI through dynamic model selection for energy efficiency. Co-PI on two Facebook Research-funded projects Active in program committees for algorithms and artificial intelligence conferences Over 30 publications in international conferences and journals Publications span network dynamics, election control, software testing, and hypergraph convergence. His work bridges theoretical computer science with applied problems in social influence and biomedical informatics.