Find Academic Supervisors

Giuseppe Bruno Averta is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Polytechnic University of Turin, and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is affiliated with the College of Computer, Film and Mechatronics Engineering and contributes to national and international research in artificial intelligence and robotics. Averta has held a Visiting Researcher position at the Massachusetts Institute of Technology (MIT) from January to June 2019. His research interests include Computer Vision, Deep Learning, Robotics, Neural Architecture Search, Egocentric Vision, Embodied Intelligence (Edge/Tiny ML), and Human-Robot Collaboration . His work is aligned with ERC sectors in Artificial Intelligence, Machine Learning, and Robotics, and contributes to UN SDGs such as Good Health and Well-being, Industry Innovation and Infrastructure, and Responsible Consumption and Production. The recent publication trends highlight his focus on vision-language models (e.g., CLIP), egocentric action recognition, efficient neural architectures (e.g., BiSeNet, MaskFormer), and robust deep learning. His research bridges theoretical advances with practical robotics applications, including grasping and manipulation. Scientific Awards and Recognitions: Georges Giralt PhD Award (euRobotics AISBL, 2021) Wiley Best Reviewer (Wiley, Italy, 2021) Best Paper Award, ICUMT 2015 (2017) Fellow, ELLIS Network of Excellence (2022–) Fellow, DAAD AInet (2022–) DAAD AInet Fellowship Advising and Grants : Averta supervises multiple PhD students in the Artificial Intelligence and Computer and Systems Engineering doctoral programs at Politecnico di Torino. He is involved in teaching at both the master’s and doctoral levels, including courses on Robot Learning and Machine Learning and Deep Learning. He is also a co-inventor on a national and international patent for a method and algorithm for the automatic design of neural networks through machine learning, indicating active research funding and innovation. Labs and Research Groups : He is a member of the SmartData@PoliTO center and contributes to research in the VANDAL PoliTO lab (as indicated by his student Davide Buoso). His work is deeply integrated with teams working on egocentric vision, embodied AI, and neural architecture search.

• Computer Vision
• Deep Learning
• Robotics
• +9 more

Pietro Manzoni is a Professor of Computer Engineering at the Polytechnic University of Valencia (UPV), Spain. He holds a Master's from the University of Milan (1989) and a Ph.D. from Politecnico di Milano (1995). His research focuses on IoT, edge computing, and wireless networks, with emphasis on TinyML, LPWAN, and edge-cloud systems. He coordinates the Computer Networks Research Group (GRC) and is active in IEEE committees. Education includes a Master's in Computer Science (Università degli Studi di Milano, 1989) and a Ph.D. in Computer Science (Politecnico di Milano, 1995). He interned at Bellcore Labs (USA, 1992–1993) and ICSI (USA, 1994). Research interests span IoT applications, resource-constrained devices, and distributed systems. His work prioritizes empirical validation through prototypes. Teaching includes courses on Networks and Security, Intelligent IoT Systems, and IoT fundamentals in Spanish programs. Publications emphasize IoT protocols, UAV swarms, and TinyML. No scientific awards listed, but over 130 theses advised. Coordinates GRC projects and contributes to editorial boards and conferences.

• Internet of Things (IoT)
• Mobile Wireless Networks
• LPWAN Technologies
• +7 more

Ivano Bilenchi is a postdoctoral researcher at the Polytechnic University of Bari's Information Systems Laboratory (SisInf Lab). He holds a Master's in Computer Science Engineering (2020) and a Ph.D. in Electrical and Information Engineering (2024) from the same institution. His research focuses on AI, Semantic Web technologies, edge computing, and IoT applications, with notable contributions to embedded OWL reasoners and cloud-edge intelligence frameworks. He teaches courses such as Formal Languages and Compilers, Secure Programming, and Information Systems Security. His work bridges academic research with practical applications, including iCleaner (iOS system cleaner), Tiny-ME (Semantic Web reasoner), and AI-LMD (fleet optimization tool). He actively participates in conferences like ICWE and I-CiTies, and has contributed to initiatives like the sustainable development project HowtUyoga. His awards include a First Prize at the Sustainable Development Festival (2018). Research highlights include developing Cowl (lightweight OWL library for edge devices) and proposing innovative architectures for cloud-edge AI in sensor networks. Collaborations span semantic blockchain marketplaces (RideMATCHain) and UAV autonomy using knowledge representation.

• Artificial Intelligence
• Semantic Web
• Internet of Things (IoT)
• +4 more

Fabio Pareschi is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino, where he conducts research in circuit architectures, embedded systems, and signal processing with applications in security, AI, and power electronics. He is affiliated with the VLSILAB research group and leads multiple high-impact research projects. Research Interests: Chaos theory and true random number generation for cryptographic applications Compressed sensing for secure and efficient signal acquisition EMI reduction techniques in DC-DC power converters Tiny machine learning and low-power embedded systems Circuit design for IoT and biomedical applications The recent articles highlight a strong trend in integrating compressed sensing with encryption, leveraging chaos-based randomness for security, and optimizing power electronics for EMI reduction. His work bridges theoretical foundations with practical hardware implementations in microelectronics and embedded systems. Scientific Awards: Best Student Paper Award (IEEE, 2005) Best Paper Award (IEEE, 2005) IEEE PRIME Gold Leaf Certificate (2019) BioCAS Transactions Best Paper Award (2019) Best Student Paper Award at EMCCompo (IEEE, 2019) Advising and Grants: He supervises multiple PhD students in the Electrical, Electronics, and Communications Engineering program. He is the Scientific Director of the CESOIA project (Non-EU International Research, 2025–2028) on low-complexity AI models, and leads the ECS4DRES project (EU-funded, 2024–2027) on resilient energy systems. He also heads a commercial research project on high-performance DC-DC converters (2022–2025). His editorial roles include Associate Editor for IEEE Transactions on Circuits and Systems and guest editorships in multiple IEEE journals. Labs and Teams: He is a key member of the VLSILAB Group (DET), which focuses on VLSI systems, embedded signal processing, and secure hardware design.

• Chaos theory
• Compressed sensing
• Electromagnetic interference
• +14 more

Luciano Prono serves as a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino, where he is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic appointment falls under Scientific Disciplinary Sector IINF-01/A - Electronics within Area 0009 - Industrial and Information Engineering. Dr. Prono's research spans multiple cutting-edge domains in AI and signal processing, with particular expertise in neuromorphic computing, edge AI implementation, biomedical signal processing, and IoT systems. His work bridges theoretical AI concepts with practical hardware implementations, focusing on efficient neural network architectures that can operate effectively on resource-constrained devices. His publication record demonstrates a strong trajectory in developing novel neural network paradigms, particularly centered around Multiply-And-Max/Min (MAM) neurons that enable aggressive pruning while maintaining performance. These publications span top-tier venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and major IEEE conferences like ISCAS and CVPRW. The research shows a clear progression from theoretical foundations of novel neural architectures to practical implementations in robotics, biomedical applications, and edge computing scenarios. Dr. Prono actively supervises PhD research, currently guiding Lorenzo Nikiforos and Elisabetta Spinazzola in the 40th cycle of the Electrical, Electronics and Communications Engineering PhD program. His teaching responsibilities include Cloud Computing and Data Center Design Lab for the Communications Engineering Master's program and Applied Electronics for the Engineering Physics Bachelor's program. His research aligns with multiple ERC sectors including artificial intelligence systems (PE6_7), machine learning applications (PE6_11), communication networks (PE7_8), and signal processing (PE7_7), demonstrating the interdisciplinary nature of his work that bridges computer science, electrical engineering, and biomedical applications.

• Artificial intelligence
• Neuromorphic computing
• Edge computing
• +7 more

Paolo Gastaldo serves as Associate Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa's School of Engineering. His academic appointment focuses on electronic engineering (IINF-01/A classification) with teaching responsibilities spanning undergraduate and graduate programs including Electronic Engineering and Information Technologies. His research centers on machine learning for embedded systems, specializing in neural architecture search techniques optimized for resource-constrained environments. Key investigation areas include hardware-aware AI deployment on microcontrollers, energy-efficient traffic classification, and affordance segmentation for wearable robotics. His work bridges theoretical machine learning with practical hardware constraints, targeting applications in IoT security, edge computing, and smart wearable systems. Analysis of his recent publications reveals a strong trend toward Pareto-optimal solutions balancing computational efficiency and accuracy in neural network design. His research demonstrates particular expertise in adapting deep learning models for microcontroller deployment while maintaining cybersecurity functionality. The publications consistently address the challenge of implementing complex AI tasks within strict memory and power limitations of embedded devices. Gastaldo maintains active research supervision with regular publication output, primarily through the University of Genoa's institutional repository (IRIS). His work shows strong interdisciplinary connections between computer science, electrical engineering, and robotics applications.

• Machine Learning
• Embedded Systems
• Neural Architecture Search
• +4 more

Rodolfo Zunino serves as a Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa. His teaching portfolio includes core electronics courses across multiple degree programs including Master's in Electronic Engineering and undergraduate programs in Electronics Engineering and Information Technology. His research focuses on the intersection of embedded systems and neural networks, with particular emphasis on hardware-aware optimization for resource-constrained environments. Key areas include traffic classification for cybersecurity, affordance segmentation for robotics, energy-efficient structural health monitoring, and in-sensor computing architectures. His work consistently addresses the Pareto optimization between computational efficiency and accuracy in embedded AI applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward hardware-software co-design for neural networks, with increasing specialization in wearable robotics and structural monitoring systems. The research demonstrates consistent innovation in neural architecture search techniques specifically tailored for microcontrollers and sensor-embedded systems. Professor Zunino maintains active research supervision with multiple advisees (though specific names aren't listed in available materials) and collaborates on projects involving hardware implementation of neural networks for security and monitoring applications. His work receives consistent publication in relevant engineering venues. His laboratory activities center around embedded electronic systems development, with focus areas in neural network acceleration for microcontrollers, sensor data processing, and real-time system implementation for robotics and infrastructure monitoring applications.

• Embedded Systems
• Neural Networks
• Hardware-Aware Neural Architecture Search
• +7 more

Michele Ruta serves as Full Professor in the Department of Electrical and Information Engineering at Polytechnic University of Bari, specializing in pervasive computing systems and semantic technologies. His academic journey began with a Master's in Electronics Engineering from the same institution in 2002, followed by a Ph.D. in Computer Science in 2007. His research spans pervasive computing, ubiquitous web systems, knowledge representation, and wireless ad-hoc networks, with recent focus on blockchain integration for IoT, cloud-edge intelligence, and semantic web applications. Key contributions include the COWL ontology framework and Tiny-ME reasoning engine for resource-constrained devices. Analysis of his 15 most recent publications reveals strong emphasis on semantic technologies for smart mobility (2025), AI-driven logistics optimization (2025), and edge-based reasoning systems (2024). His work consistently bridges theoretical knowledge representation with practical applications in smart cities, healthcare, and industrial IoT. As editorial board member for relevant journals and Program Committee participant in international conferences, he actively shapes research directions in his fields. His technical leadership extends to national and international research projects focusing on semantic web infrastructure and IoT integration. Professor Ruta maintains active contributions through his personal research platform and IRIS publication repository, with 178 publications demonstrating sustained scholarly output since 2007. His laboratory work focuses on practical implementations of semantic reasoning for cyber-physical systems, particularly in cloud-edge continuum architectures.

• Pervasive Computing
• Ubiquitous Web
• Knowledge Representation Systems
• +7 more

Emanuele Lattanzi is an Associate Professor in the Department of Pure and Applied Sciences (DiSPeA) at the University of Urbino Carlo Bo, specializing in Information Processing Systems (IINF-05/A). His research focuses on human activity recognition, wireless sensor networks, IoT systems, and artificial intelligence applications. He obtained his Laurea degree in 2001 and PhD in 2003, followed by a visiting scholar position at Pennsylvania State University's Department of Computer Science and Engineering. His educational background forms the foundation for his current research in sensor-based AI systems. Lattanzi's research interests center on developing energy-efficient solutions for human activity recognition using wearable sensors, with particular emphasis on optimizing machine learning models for resource-constrained IoT devices. His work bridges theoretical AI advancements with practical implementation challenges in real-world sensor deployments. Analysis of his 2025 publications reveals a strong thematic focus on human activity recognition, with recurring exploration of energy-accuracy tradeoffs, novel neural network architectures (including transformers and Siamese networks), and self-supervised learning techniques specifically adapted for inertial sensor data. His research demonstrates consistent collaboration with multiple co-authors across international conferences. As an educator, Lattanzi teaches core computer science courses including Operating Systems (12 credits) and IoT Programming (6 credits) across multiple Informatics degree programs. His teaching spans from 2021/2022 through the current 2025/2026 academic year, demonstrating sustained contribution to the department's curriculum in both foundational systems programming and emerging IoT technologies.

• Human Activity Recognition
• Wireless Sensor Networks
• Internet of Things (IoT)
• +1 more

Riccardo Berta is an Associate Professor at the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa. His research focuses on TinyML, deep learning on microcontrollers, and embedded systems for automotive and IoT applications. Current Affiliation: Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN), University of Genoa Academic Role: Associate Professor, Electronics (IINF-01/A) His research interests include: Tiny Machine Learning (TinyML) for resource-constrained devices Deep Reinforcement Learning in automated driving and gaming Edge Computing and Structural Health Monitoring IoT frameworks for data management and serious games Recent work explores the application of neural networks, adversarial training, and synthetic dataset generation in automotive scenarios. He is a member of the Scientific Council of the University Service Center for Simulation and Advanced Training (SIMAV) and the Department Board of DITEN.

• Tiny Machine Learning
• Reinforcement Learning
• Deep Learning on Microcontrollers
• +5 more

Yuri Borgianni is an Associate Professor at the Faculty of Engineering, Free University of Bozen-Bolzano (unibz), Italy. He teaches courses including Technical Drawing, Reverse Engineering & Rapid Prototyping, and Design Creativity. His research focuses on engineering design, product development, and sustainable innovation. Borgianni has established himself as a prominent researcher in design methodology, with particular expertise in TRIZ (Theory of Inventive Problem Solving) and Additive Manufacturing applications. Dr. Borgianni earned his Master's Degree in Mechanical Engineering from the University of Florence in 2005, followed by a Ph.D. in Industrial Engineering from the same institution in 2014. His academic journey has led him to specialize in the intersection of design creativity, sustainable development, and advanced manufacturing technologies, positioning him at the forefront of innovative research in engineering design. Professor Borgianni's research interests span value innovation, ideation within engineering design, creative product development, industrial process re-engineering, and computer-aided innovation. He has made significant contributions to understanding how sustainability principles can be effectively integrated into product design processes. His work often combines theoretical frameworks with practical applications, particularly exploring the potential of Additive Manufacturing for sustainable production. Borgianni has also investigated user experience aspects in design evaluation through advanced methodologies like eye-tracking and virtual reality. His recent publications reveal a strong focus on sustainable design education, manufacturing process selection using machine learning, circular economy implementation in product development, and the application of TRIZ for environmental problem-solving. Borgianni's work demonstrates a consistent trend toward integrating digital technologies with sustainable design principles, with increasing attention to circular economy metrics and the human factors involved in sustainable product evaluation. While specific scientific awards are not prominently mentioned in the available information, Professor Borgianni's extensive publication record (over 160 publications) and his role as editor for special issues on design processes indicate significant recognition within his field. His work has been cited over 50,000 times according to ResearchGate metrics, reflecting substantial impact in engineering design research. Professor Borgianni actively supervises research projects and mentors students in the Faculty of Engineering at unibz. His research has been supported by various grants focused on sustainable product development, additive manufacturing applications, and design methodology innovation. He collaborates extensively with researchers across Europe, particularly in Italy, Germany, and the UK, contributing to international projects that bridge academic research with industrial applications in sustainable manufacturing. At the Free University of Bozen-Bolzano, Borgianni is involved in research initiatives related to sustainable engineering design, working with interdisciplinary teams that combine expertise in mechanical engineering, computer science, and environmental science. His work with the university's engineering faculty contributes to the institution's focus on innovative, sustainable technologies and their practical implementation in industry.

• Engineering Design
• Product Development
• Design Creativity
• +7 more

Floriano Scioscia is an Associate Professor at the Polytechnic University of Bari, Italy, specializing in Information Processing Systems (SSD ING-INF/05). His research bridges Semantic Web technologies, Knowledge Representation, and Artificial Intelligence to develop innovative solutions for Internet of Things, Blockchain, and smart city infrastructures. His primary research domains include Semantic Web, Knowledge Representation and Reasoning, Internet of Things, Blockchain, Edge Computing, and Artificial Intelligence. He applies these to solve critical problems in smart mobility (e.g., blockchain-enhanced ridesharing), last-mile logistics optimization, energy infrastructure management, and healthcare decision support. His work consistently focuses on creating lightweight, scalable frameworks like Tiny-ME for resource-constrained environments and semantic-enhanced platforms for real-world system integration. Analysis of his 2023-2025 publications reveals a dominant trend toward edge-centric semantic technologies. Key themes include OWL reasoning on constrained devices (Tiny-ME Wasm), blockchain integration for transparent service platforms (RideMATCHain), and AI-driven optimization for logistics/smart cities. His work demonstrates exceptional cross-domain applicability, with consistent emphasis on explainability, scalability, and practical implementation in transportation, energy, and healthcare infrastructure. The publications show strong synergy between theoretical knowledge representation advances and deployable system architectures.

• Semantic Web
• Knowledge Representation and Reasoning
• Artificial Intelligence
• +5 more