Find Academic Supervisors

• Computer Engineering
• Embedded Systems
• System-on-Chip (SoC) Design
• +3 more

Giorgio Insinga is a Research Fellow at the Department of Control and Computer Science (DAUIN) of Politecnico di Torino. He serves as an External lecturer and teaching assistant, contributing to courses like Computer System Architectures and Computer Engineering since 2022/23. Research Interests: His work focuses on embedded memory testing, diagnosis, and reliability in automotive System-on-Chips (SoCs). Key areas include fault detection methodologies, diagnostic data compression strategies, and hybrid ADC designs for on-chip measurements. He collaborates with the CAD - Electronic CAD & Reliability Group (DAUIN). Scientific Contributions: Recent publications address test architectures for large-scale memory banks, low-area ADC implementations, and irradiation-based memory parameter extraction. His research bridges hardware reliability with practical automotive applications. National patent for 'Electronic board quality analysis software' Teaching Roles: Collaborated in courses related to computer architectures and system design since 2022, emphasizing practical implementation and testing techniques.

Francesco GUARNERAView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Deep Learning
• +3 more

Francesco GUARNERA is a Researcher at the Department of Mathematics and Computer Science of the University of Catania. He earned his B.Sc. (2009) and M.Sc. (2018) in Computer Science from the University of Catania (summa cum laude), followed by a Ph.D. in Mathematics and Computer Science (2022) from the same university, with research conducted at the University of Hertfordshire, UK. Research Focus: Computer Vision, Artificial Intelligence, and Deep Learning applied to forensic and medical imaging. Key Projects: JPEG compression trace analysis, document authentication via fingerprint extraction, medical image classification/segmentation (MRI, CT-scans), and Agent-Based Modeling for drug evaluation. Collaborations: Rizzoli Orthopedic Institute, iCTLab, COMBINE Group, Garibaldi Hospital, and CoEHAR. His publications span journals like MDPI Journal of Imaging , Elsevier JVCI , and IEEE Access , with topics covering Deepfake detection, pollen classification, and forensic methodologies. He co-supervised multiple M.Sc. theses and contributed to international summer schools (ICVSS, IFOSS) as an organizer and participant.

Riccardo RenzulliView profile

Researcher

• Object-Centric Representation Learning
• Medical Image Analysis
• Computer Vision
• +5 more

Riccardo Renzulli is a Researcher at the Department of Computer Science, University of Turin, focusing on object-centric representation learning, medical image analysis, and AI-based computer vision applications. His research emphasizes capsule networks, deep learning models for hierarchical relationships, and applications in healthcare and aerial/satellite imagery. Education: MSc and BSc in Computer Science from University of Turin (2018 and 2015). Previous research with Prof. Valentina Gliozzi explored description logics and non-monotonic reasoning. Professional experience includes a 2022 post at Aalto University (supervised by Prof. Ville Kyrki and Francesco Verdoja) and roles at Addfor and Machine Learning Reply as a deep learning scientist. Research interests span concept learning, few-shot learning, interpretability, and medical imaging. Notable work includes visual localization systems for UAVs, AI-assisted diagnosis for COVID-19 via CXR analysis, and lung nodule segmentation using DeepHealth Toolkit. He contributed to the UniToChest dataset for cancerous nodule detection. His recent publications (2022-2025) address efficient neural architectures, medical imaging applications, and 3D scene modeling. Collaborations include EIDOSLAB, with research emphasizing scalable compression, entropy-based pruning, and ensemble methods for neural networks.

Juan David Guerrero BalagueraView profile

Research Fellow

• AI Accelerators
• GPU Reliability
• Fault Tolerance
• +7 more

Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.

• Machine Learning
• Electronic Design Automation
• Reliability Engineering
• +7 more

Nicolò Bellarmino is a Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, where he also serves as an External Lecturer and Teaching Assistant. His work is centered on machine learning applications in electronic design automation, particularly in microcontroller performance screening and reliability assessment of deep learning hardware. His research interests include machine learning for embedded systems, feature selection, neural network testing, and reliability engineering. He employs techniques such as evolutionary algorithms, transfer learning, and fault injection to develop efficient and robust methodologies for semiconductor testing and DNN accelerator validation. The recent publications highlight a strong focus on data-efficient and automated approaches for performance prediction and reliability assessment, leveraging foundation models, unsupervised learning, and hierarchical modeling. These works span journals and top-tier conferences in computer-aided design, electronics, and machine learning. He has no listed scientific awards in the provided text. Bellarmino actively contributes to teaching in the Computer Engineering and Aerospace Engineering programs, collaborating on courses such as Systems Programming, System and Device Programming, and Future of Work. He is a member of the CAD - Electronic CAD & Reliability Group (DAUIN), contributing to cutting-edge research in EDA and hardware reliability. No grants or advising roles are mentioned.

• Fluid Machinery
• Thermal and Hydraulic Machines
• Internal Combustion Engines
• +3 more

Oscar Vento serves as a Fixed-term Assistant Professor in the Department of Energy (DENERG) at Politecnico di Torino, Italy, within the College of Mechanical, Aerospace, and Automotive Engineering. His academic responsibilities span teaching Computational Heat and Mass Transfer at the PhD level in Energetics and Fluid Machines across Bachelor's and Master's programs in Mechanical Engineering since the 2019/20 academic year through current 2025/26 offerings. Research Focus: Dr. Vento's work centers on fluid machinery and thermal systems, with emphasis on internal combustion engines, fuel injection technologies, and sustainable energy solutions. His expertise includes Gasoline Direct Injection (GDI) systems, cavitation phenomena, thermal-hydraulic performance analysis, and real-time control methodologies for fuel systems. Research aligns with UN Sustainable Development Goals 7 (Clean Energy), 9 (Innovation), 11 (Sustainable Cities), and 12 (Responsible Consumption). Publication Trends: Recent publications (2022-2025) demonstrate consistent focus on experimental and numerical investigations of fuel injection dynamics, with increasing emphasis on alternative fuels (ammonia), neural network applications, and transient flow measurement techniques. Research bridges fundamental fluid mechanics with practical automotive engineering challenges, particularly in emission reduction and energy efficiency. Advising and Intellectual Property: Co-supervises PhD candidate Carmelo Baronetto (Energetics program, 39th cycle, 2023-present) Holds national patent: System and method for measuring injected flow rate using a neural network (with Ferrari and Novara) Cod holds national/international patents: Control of the quantity injected into internal combustion engines (with Ferrari, Novara, Violante, Zhang) Research Context: Collaborates extensively within Alessandro Ferrari's research group, with publications spanning ENERGY, FUEL, ENERGIES, and Journal of Fluid Mechanics. Work addresses critical challenges in sustainable transportation through advanced diagnostics, control systems, and alternative fuel combustion research.

ALESSIO VITAView profile

Researcher

• Composite Materials
• Additive Manufacturing
• Sustainability Assessment
• +5 more

Alessio Vita is a Researcher at the Department of Industrial Engineering and Mathematical Sciences (DIISM) within the College of Engineering at Marche Polytechnic University. His work focuses on composite materials, additive manufacturing, and sustainability assessment. Role: Researcher Location: Via Brecce Bianche 12, 60131 Ancona Office Hours: Wednesdays (13:30–14:30) in Pesaro; Tuesdays (12:00–14:00) in Fermo Research spans mechanical properties of 3D-printed composites, environmental sustainability, and cost optimization in manufacturing. His work emphasizes life cycle assessment (LCA), carbon fiber reinforced polymers (CFRP), and innovative printing methods for composite materials. Recent publications highlight advancements in 3D printing technologies, thermal annealing effects, hygrothermal durability of composites, and eco-friendly production techniques. Key trends include sustainable industrial practices and multi-criteria optimization models. Contact: alessio.vita@univpm.it

Andrea SciarroneView profile

Associate Professor

• Internet of Things (IoT)
• Wireless Communications
• Artificial Intelligence in Engineering
• +7 more

Andrea Sciarrone is an Associate Professor at the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa. He teaches courses on IoT applications, applied telecommunications, and Python programming for engineering disciplines. His research focuses on the integration of IoT and AI in structural health monitoring, wireless surveillance, and biomedical diagnostics. Key areas include edge computing, microwave imaging for stroke detection, and wearable sensors for rehabilitation. He has developed innovative systems for crowd monitoring, drone-based object tracking, and contactless patient surveillance using Wi-Fi signals. The recent articles highlight trends in AI-driven IoT optimization, wireless security, and biomedical engineering. His work bridges telecommunications theory with practical implementations in infrastructure monitoring and healthcare applications. He actively contributes to the development of eHealth systems, leveraging IoT for home rehabilitation and neurological symptom detection. His technical expertise spans compressive sensing, fingerprint-based positioning, and electromagnetic imaging techniques.

Mihai Teodor LazarescuView profile

Associate Professor

• Artificial intelligence
• Capacitive sensors
• Embedded software
• +10 more

Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)

• Circuit modeling
• Circuit simulation
• Kriging
• +9 more

Paolo Manfredi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He actively contributes to the EMC Group (Electromagnetic Compatibility) and serves as an Associate Editor for journals including IEEE Journal on Multiscale and Multiphysics Computational Techniques and International Journal of Circuit Theory and Applications. His research focuses on uncertainty quantification in circuits, surrogate modeling , machine learning applications, and signal integrity analysis . He leads projects on compact dynamical modeling of complex systems and stochastic analysis of interconnects. Recent publications highlight advancements in active learning for PCB line uncertainty quantification, connector degradation impacts on microwave signals , and SPICE-compliant IC model compression , reflecting his expertise at the intersection of electrical engineering and data science . Scientific Awards: Best Paper Award EPEPS (2010, 2013) Premio Optime (2010) URSI Young Scientist Award (2011) Honorable Mention - IMS (2011) He supervises PhD students in projects spanning 5G/6G metasurfaces , power cable corrosion assessment , and neural network applications in circuit design.

Antonio FrisoliView profile

Professor

• collaborative robotics
• rehabilitation robotics
• wearable robotics
• +5 more

Antonio Frisoli is a Full Professor of Engineering Mechanics and Robotics at Sant'Anna School of Advanced Studies, where he leads the Human-Robot Interaction area at the Institute of Mechanical Intelligence. He serves as President of Artes4.0, the Italian National Competence Center on Robotics and Industry 4.0, and holds editorial roles including Associate Editor for IEEE Robotics Automation Magazine. His educational background includes: PhD with honors in Industrial and Information Engineering from Scuola Superiore Sant'Anna (2002) MSc in Mechanical Engineering, minor Robotics, from University of Pisa (1998) MSc in Industrial Engineering from Scuola Superiore Sant'Anna (1998) as an honor student Professor Frisoli's research spans collaborative robotics, rehabilitation robotics, wearable robotics and exoskeletons, multimodal interaction and haptics, wearable devices, and virtual reality. His work leverages expertise in human perception biomechanics to develop advanced haptic devices and robotic systems for rehabilitation, teleoperation, and virtual environments, with significant focus on clinical applications for stroke rehabilitation and orthopedic recovery. Analysis of his recent publications reveals strong emphasis on haptic interface innovation, exoskeleton optimization, and human-robot collaboration. Key trends include wearable haptic gloves for remote interaction, musculoskeletal modeling for rehabilitation exosuits, running efficiency enhancement through hip exoskeletons, and tele-echography systems resilient to time delays, demonstrating consistent translation of theoretical research into practical applications. Professor Frisoli actively drives technology transfer through co-founding two spin-off ventures: Wearable Robotics srl: A market leader in exoskeleton technology Next Generation Robotics srl: Specializing in robotic solutions for railway systems He directs the Human-Robot Interaction research group within PERCRO (Perceptual Robotics Laboratory), which maintains deep expertise in psychophysics of human perception and biomechanics. The group develops force feedback exoskeletons, large workspace haptic devices, and virtual reality systems for applications ranging from telepresence to clinical rehabilitation, with ongoing projects enhancing stroke therapy through robotic supplementation of traditional one-to-one treatment.

• Regenerative Medicine
• Neuroscience
• Anatomy
• +3 more

Andrea Sbarbati is a Full Professor at the University of Verona's Department of Neurological, Biomedical and Movement Sciences, specializing in Human Anatomy. He holds leadership roles in department governance, including Department Director and membership in multiple academic committees such as Research and Internationalization. His teaching spans over 165 modules across Medicine, Surgery, Physiotherapy, and specialized postgraduate programs, focusing on Human Anatomy I and II, Bioimaging in Translational Research, and related clinical disciplines. Research interests include regenerative medicine, nanomedicine, neuroanatomy, and advanced imaging techniques. Ongoing projects involve biomaterials for tissue repair, CFTR modulation in viral infections, and novel therapies for skin aging and scars. His work bridges basic science with clinical applications, particularly in stem cell therapy and anatomical imaging. Key research tools include ultrastructural analysis, spectroscopy, and preclinical MRI. He collaborates with centers like the Brain Bank and the WHO Collaborating Centre for Mental Health. His lab focuses on morphology and cell/tissue biology, applying microscopy and multimodal imaging. No scientific awards are explicitly mentioned, but his extensive publications reflect impactful contributions to regenerative medicine and biomedical research. Advising and grants include roles in student mentorship and projects like the Verona Nanomedicine Initiative. His labs, including the Tissue Engineering and Neuroimaging groups, drive interdisciplinary innovation. Clinical interests span aesthetic medicine, burn scar treatment, and anatomical education.

• Neuroimaging
• Neuroscience
• Cognitive Science
• +3 more

Jorge Jovicich is a Full Professor in the Department of Psychology and Cognitive Science at the University of Trento, Italy. He holds concurrent roles at the Center for Mind/Brain Sciences (CIMeC) and the Centre for Medical Sciences (CISMED). With expertise in functional neuroimaging and MRI technology, he leads the MRI lab and Methods Group at CIMeC since 2006. His academic career spans postdoctoral research at institutions like MIT, Caltech, and Harvard Medical School, alongside roles as a lecturer in neuroscience and medical imaging. Education includes a PhD in Physics from the Max-Planck Institute and University of Leipzig, an MSc in Medical Imaging from the University of Aberdeen, and a Licenciatura en Física from the Universidad Nacional de Córdoba. His research focuses on brain connectivity, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and advancing MRI techniques for clinical applications. He has contributed to projects like the ISMRM reproducibility challenge and developed tools such as ReStNeuMap for functional MRI network analysis. Teaching spans global institutions including Harvard, MIT, and the Instituto Balseiro in Argentina. His work emphasizes translational neuroimaging, bridging basic science and clinical practice. Key interests include biomarker development for neurodegenerative diseases and optimizing neuroimaging protocols for accuracy and reproducibility. Recent research trends include exploring temporal resolution in fMRI, thalamic nuclei segmentation, and microstructural pathways in cognitive impairment. Collaborative efforts aim to standardize neuroimaging practices and improve diagnostic accuracy through advanced MRI methods.

• Computational Mathematics
• Numerical Analysis
• Isogeometric Analysis
• +3 more

Mattia Tani is a researcher at the Department of Mathematics, University of Pavia. He is affiliated with the Scientific Computing research group focusing on Numerical Methods and Applications. His work centers on developing advanced numerical techniques for computational mechanics, particularly Isogeometric Analysis (IGA), space-time methods, and efficient solvers. Tani’s research emphasizes computational efficiency through low-rank approximations, preconditioning strategies, and parallelization techniques. His academic contributions include pioneering work on matrix-free methods, Tucker tensor-based solvers, and domain decomposition approaches for multi-patch geometries. Notable methodologies include the IETI-DP method for discontinuous Galerkin discretizations and weighted quadrature rules for hierarchical B-splines. Tani also explores applications in parabolic problems, Schrödinger equations, and thermal engineering simulations. Publications highlight a focus on bridging theoretical numerical analysis with practical computational tools, with emphasis on scalability and robustness for large-scale problems. His work is disseminated across top journals and conferences, reflecting interdisciplinary collaboration between applied mathematics and engineering.

• Sparse optimization
• Machine learning
• Convex and nonconvex optimization
• +7 more

Sophie Fosson is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, Italy. She is a member of the System Identification & Control (SIC) research group and teaches courses such as Automatic Control and Modeling and Control of Cyberphysical Systems at both the master's and doctoral levels. Education: PhD in Mathematics for Industrial Technologies, Scuola Normale Superiore di Pisa Master’s in Mathematical Engineering, Politecnico di Torino Her research focuses on sparse optimization, machine learning, system identification, and control theory . She integrates mathematical modeling with practical applications in cyber-physical systems and neural networks. Her work emphasizes efficient and robust optimization techniques for deep learning and control systems. The recent publications highlight a strong trend in optimization of neural networks , particularly through sparse training, binary and ternary quantization, self-supervised learning, and fault tolerance . Her research bridges control theory with machine learning, applying mathematical rigor to modern AI challenges in safety-critical and embedded environments. Scientific Awards: No awards explicitly mentioned. She supervises several PhD students including Alice Re, Rosario Milazzo, and Simone Pirrera , and has been involved in research projects such as CRISP and collaborations with Centre Tecnològic de Telecomunicacions de Catalunya . She has held postdoctoral positions at DISMA and DET (Politecnico di Torino) and worked as a researcher at Istituto Superiore Mario Boella. Laboratories and Research Groups: System Identification & Control (SIC), DAUIN, Politecnico di Torin