جست‌وجوی استاد و پژوهشگر

• Data Mining
• Process Mining
• Knowledge Representation
• +۵ مورد دیگر

Antonella Guzzo is an Assistant Professor (ING-INF/05) at the Faculty of Engineering, University of Calabria, affiliated with the Department of Electronic Informatics and Systems (DEIS). Her research spans Data Mining, Process Mining, Federated Learning, and Knowledge Representation, with applications in Cyber Security and Bioinformatics. Laurea (2000) and Ph.D. (2004) in System Engineering and Computer Science from University of Calabria Her research integrates advanced techniques like graph mining, outlier detection, and AI-driven process discovery. She contributes to projects like TOCAI.it, PROMIS, and OpenKnowTech, focusing on knowledge-oriented technologies and logistics optimization. Recent publications highlight Federated Learning for healthcare, Green AI, and IoT security. She is a member of the IEEE Task Force on Process Mining and serves as a reviewer for journals such as Data and Knowledge Engineering. Antonella collaborates with labs including the Artificial Intelligence and Data Science Laboratory and Cyber Security Laboratory at DIMES.

• Multimedia Security
• Multimedia Forensics
• Adversarial Machine Learning
• +۷ مورد دیگر

Tiziano Bianchi is a Full Professor in the Department of Electronics and Telecommunications at Politecnico di Torino (Polytechnic of Turin), Italy. He serves as the Course Coordinator for the Master's Degree in ICT Engineering for Smart Societies and is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Previously, he was an Assistant Professor at the same institution starting in 2012, having joined from the University of Florence where he worked as a Research Assistant from 2005 to 2012. Dr. Bianchi's research focuses on multimedia security technologies, with particular expertise in multimedia forensics, signal processing in the encrypted domain, and security aspects of compressed sensing. His work spans adversarial machine learning, image and video forensics, deep learning for video compression, secure authentication based on deep learning, and satellite image compression. He has made significant contributions to privacy-preserving data processing through compressed sensing techniques. His recent publications demonstrate a strong emphasis on applying deep learning to solve complex problems in image processing, security, and data compression. The research shows a clear trajectory toward efficient, secure, and privacy-preserving processing techniques, particularly for resource-constrained environments like wearable devices and satellite systems. Among his notable recognitions are the Journal of Visual Communication and Image Representation Best Editor of 2022 Award, The SIGMM Test of Time Paper Honorable Mention in Multimedia Security and Privacy, and the 2019 Best Paper Award from IEEE MultiMedia. He has also received multiple editorial awards for his work as Senior Area Editor for the Elsevier Journal of Visual Communication and Image Representation and Associate Editor for the IEEE Transactions on Information Forensics and Security. Dr. Bianchi is the co-founder of ToothPic, a startup offering breakthrough technologies for camera identification, and has co-invented four patents related to cryptographic key obfuscation, user authentication, and camera recognition techniques. He has authored over 100 papers in international journals and conference proceedings, demonstrating sustained scholarly productivity throughout his career.

• Artificial intelligence
• Neuromorphic computing
• Edge computing
• +۷ مورد دیگر

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.

Riccardo Marinمشاهده پروفایل

پژوهشگر

• Spectral Shape Analysis
• Geometric Deep Learning
• Virtual Humans
• +۳ مورد دیگر

Riccardo Marin is a Post-Doctoral Researcher at the University of Tuebingen, supported by the Humboldt Post-Doctoral Fellowship. He is part of the Real-Virtual Humans group led by Prof. Gerard Pons-Moll. Previously, he conducted post-doctoral research at Sapienza University of Rome in the GLADIA group under Prof. Emanuele Rodolà, contributing to the SPECGEO ERC project. He earned his Ph.D. in Computer Science from the University of Verona under Prof. Umberto Castellani, winning the Best PhD Thesis Award by EG-Italy. He holds a Master's degree in Computer Science and Engineering from the University of Verona (2017). His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and Virtual Humans, with contributions to top-tier conferences and journals (NeurIPS, IJCV, CVPR). Key achievements include a Best Student Paper Award at 3DV 2020 and five Outstanding Reviewer Awards. He actively serves as a reviewer for leading journals and conferences (PAMI, TCVG, CVPR, NeurIPS) and has organized events like Siggraph RCD and 3DV 2018. Collaborations include institutions like École Polytechnique, Max-Planck Institute, INRIA Strasbourg, UCL, and the University of Milan. His work is supported by grants from Google and the European Research Council (ERC). His research bridges geometry processing with machine learning, emphasizing spectral methods and shape correspondence. Notable projects include MoMaS (real-time procedural texturing), FARM (3D human body registration), and contributions to latent space analysis for shape modeling. He maintains an active GitHub profile with repositories like NICP and InstantRecoveryFromSpectrum .

Domenico Ursinoمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Domenico Ursino is a Full Professor at the Polytechnic University of Marche (UNIVPM) in the Department of Information Engineering . His research focuses on advanced topics in Computer Science , Artificial Intelligence , and Social Network Analysis , including Vision Transformers, complex network modeling, and data lake processing. Research Interests : Machine Learning, Social Network Analysis, Deep Learning, Complex Networks, Explainable AI, Graph Neural Networks, and Neuroscience. Notable Trends : Recent work emphasizes token pruning in Vision Transformers, user influence dynamics in social platforms, and multilayer network frameworks for XAI and social media analysis. Contact: d.ursino@univpm.it

Enrico Masalaمشاهده پروفایل

دانشیار

• Multimedia Communications
• Video Coding
• Digital Image Processing
• +۷ مورد دیگر

Enrico Masala is Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, where he also serves as Deputy Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the Internet Media Group (IMG) and the Interdepartmental Center for Service Robotics (PIC4SeR), and actively contributes to multimedia systems research and education. His research interests include multimedia communications, video coding, digital image processing, and media quality assessment, with a strong focus on AI and machine learning applications. His work spans algorithm development for quality optimization in multimedia transmission over packet networks, including wireless and cloud-based systems. The recent publications highlight a strong trend in leveraging deep learning and AI to model human perception in multimedia quality assessment. Topics include artificial observers, modeling of subjective scoring behavior, and individualized quality prediction—indicating a shift toward more personalized and human-aligned evaluation frameworks in multimedia systems. Elsevier Audioslides competition (2013) ScienceDirect TOP25 Hottest Articles (2012) Fellow - IEEE (2014–) IEEE Senior Member Enrico Masala has served as Scientific Manager for multiple commercial research projects, including the Radio 3.0 streaming platform and V-POP multimedia transmission project. He has also acted as evaluator for CHIST-ERA and the European Commission. He teaches core courses such as Internet Video Streaming and Web Applications , and has delivered international lectures and keynote talks, including at BUPT in China and CONIELECOMP in Mexico. He leads the JEG-Hybrid project within the Video Quality Experts Group (VQEG) and is active in international research networks related to next-generation multimedia networking and cloud-based media delivery.

• Biophotonics
• Fiber Lasers
• Solid-state Lasers
• +۷ مورد دیگر

Stefano Taccheo is an Associate Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino. He is a member of the Interdepartmental Center Photonext and serves as the Scientific Contact of the European Association Photonics21. His professional activities span across multiple institutions including Swansea University where he was Associate Professor from 2008-2019 and co-founded the Centre for Cosmetic & Curative applications of Compound Semiconductor Tech (C4ST). Dr. Taccheo's research focuses on biophotonics and tumor diagnosis, fiber lasers and solid-state lasers, quantum cascade lasers for medical applications, and wide-bandwidth active materials and devices. His work bridges the gap between fundamental laser physics and practical medical applications, with particular emphasis on cancer detection and environmental monitoring. His recent publications demonstrate a clear trend toward advanced modeling of doped glass systems for laser applications, with increasing integration of machine learning techniques for optical system analysis. The research spans from fundamental laser physics to biomedical applications, showing a consistent trajectory of applying photonics to solve real-world medical and environmental challenges. Philip Morris Award for Scientific and Technological Research, Italy (1992) Steering Committee member - Photonics21, European (2019-) Steering Committee member - Photonics21, European (2011-2014) Dr. Taccheo currently supervises PhD student Riccardo Ballarini in the Electrical, Electronics, and Communications Engineering program. His research is supported by significant collaborations including the CIBioF agreement between Politecnico di Torino and the University of Turin for the study of pharmacological and oncological biomarkers with photonic techniques (2025-2029). He has held program chair positions for multiple Advanced Solid-State Lasers conferences and Fiber Lasers and Glass Photonics events. As a key member of the Photonext Interdepartmental Center on Applied Photonics at PoliTO, Dr. Taccheo contributes to interdisciplinary research teams working at the intersection of photonics, medicine, and environmental science. His leadership in the European Photonics21 platform demonstrates his significant role in shaping photonics research directions at the European level.

Laura Falaschettiمشاهده پروفایل

پژوهشگر

• Embedded Systems
• Neural Networks
• Biomedical Engineering
• +۳ مورد دیگر

Prof. Laura Falaschetti is a Researcher at the Department of Information Engineering, Polytechnic University of Marche (Ancona, Italy). Her work focuses on embedded systems, neural networks, biomedical engineering, and signal processing. She develops lightweight machine learning models for resource-constrained devices, with applications in healthcare monitoring, environmental sensing, and wearable technology. Key projects include real-time gesture recognition systems, EEG-based disease classification, and low-power IoT devices for disaster early warning. Her research integrates hardware-software co-design (e.g., QEMU/GHDL) and emphasizes practical deployment of AI algorithms on microcontrollers. She holds office hours every Friday 10:00-13:00 at Ufficio docente Q165 DII/Microsoft Teams. Contact: l.falaschetti@staff.univpm.it Publications highlight contributions to embedded vision systems, wearable sensor networks, and multimodal signal fusion for clinical applications. Her work bridges theoretical machine learning with practical embedded system constraints, addressing challenges in energy efficiency, real-time processing, and medical accuracy.

• Adversarial Machine Learning
• Explainability
• Cybersecurity
• +۳ مورد دیگر

Maura Pintor is an Assistant Professor at the University of Cagliari, Italy. Her research focuses on adversarial machine learning, explainability methods, and cybersecurity applications. She holds an MSc in Telecommunications Engineering (2018) and a PhD in Electronic and Computer Engineering (2022), both from the University of Cagliari. Her work emphasizes robustness in AI systems, including defenses against adversarial examples, secure model updates, and dataset development for testing adversarial robustness. She has contributed to frameworks like SecML for explainable machine learning and led projects such as RAID and Attackbench for evaluating adversarial defenses. Her research spans neural network pruning, over-parameterization effects, and query-efficient adversarial attacks on phishing detectors. She is affiliated with the PRALab, where she explores AI security in autonomous systems and malware detection. Key areas include continual learning for malware detection, adversarial attacks on deep reinforcement learning, and model security in edge computing environments. She actively publishes in top venues like the ACM Workshop on Artificial Intelligence and Security (AISec).

Gigliola Vagliniمشاهده پروفایل

استاد

• Distributed Systems Verification
• Model Checking
• Machine Learning Applications
• +۲ مورد دیگر

Gigliola Vaglini is Full Professor of Computer Engineering at the University of Pisa's Department of Information Engineering. She teaches Software Systems Engineering and Databases, with research focusing on formal specification and verification of concurrent/distributed systems. Her work integrates model checking methodologies with machine learning techniques for industrial anomaly detection and mobile security applications. She has participated in multiple research projects funded by the Italian Ministry of University and Research, National Research Council, and European Community. Research Focus: Vaglini's work bridges theoretical computer science and practical applications, developing verification frameworks for complex systems while creating AI solutions for real-world problems. Her current investigations span swarm intelligence coordination, structural monitoring via digital twins, and multimodal deep learning architectures. Recent publications demonstrate cross-disciplinary approaches combining formal methods with machine learning, particularly in environmental monitoring, structural engineering, and neural architecture design. This reflects a consistent pattern of translating theoretical computer science concepts into applied technological solutions.

Enzo Tartaglioneمشاهده پروفایل

دانشیار

• Deep Learning
• Model Compression
• Pruning
• +۶ مورد دیگر

Enzo Tartaglione serves as Associate Professor at Télécom Paris, Institut Polytechnique de Paris, holding a Hi!Paris chair and contributing as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems. His academic journey spans multiple institutions across Europe and the US, reflecting a strong interdisciplinary foundation. Educational milestones include: MS in Electronic Engineering, Politecnico di Torino (2015, cum laude) MS in Electrical and Computer Engineering, University of Illinois at Chicago (2015, magna cum laude) MS in Electronics, Politecnico di Milano (2016, cum laude) PhD in Physics, Politecnico di Torino (2019, cum laude), thesis: 'From Statistical Physics to Algorithms in Deep Neural Systems' His research centers on efficient deep learning , with pioneering work in model compression, neural pruning, and debiasing techniques. He actively develops methods for privacy-aware learning and green AI, targeting real-world deployment constraints in computer vision and medical imaging applications. His approach bridges theoretical physics with practical AI optimization. Recent publications (2024-2025) demonstrate consistent focus on computational efficiency, with 60% of works addressing model compression for vision tasks, 25% on bias mitigation, and emerging contributions in privacy preservation. Key venues include ICCV, CVPR, and IEEE Transactions, reflecting strong industry-academia impact. Scientific recognition includes: Finalist for Multimedia Rising Star Award (2025) He mentors 10 active PhD candidates across compression, debiasing, and on-device learning domains, having previously guided 3 PhD graduates and 17+ Master's researchers. Research funding includes the Hi!Paris GIFFAI project (2025) for frugal AI and ANR's BANERA initiative (2024) on bias-aware architecture search. His group operates within Télécom Paris' joint laboratory, driving the Frugal AI initiative through collaborations with ELLIS Society partners and industry stakeholders focused on sustainable deep learning deployment.

• Edge Computing
• Fog Computing
• Distributed Algorithms
• +۴ مورد دیگر

Roberto Beraldi is an Associate Professor at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome since 2002. He leads the Edge Computing Lab, which focuses on distributed algorithms, resource sharing, and scheduling policies for edge and fog environments. His research interests include edge computing, fog computing, and past work on mobile ad hoc networks (MANETs) and peer-to-peer systems. He co-founded Ubiquitous Srl, a startup spun off from Sapienza University. Research & Grants: Current PI of the Italian PRIN 2022 grant 'EV2 Edge Vision against Varroa' and former PI of the FogAware project. Active in EU-funded research initiatives such as BLEND and ReSIST. His lab uses Raspberry Pi clusters and Oculus Quest devices for experiments in edge-assisted VR and distributed computing. Teaching & Advising: Supervises MSc and PhD theses on edge computing applications, including AR/VR integration and real-time task scheduling. Recent advisees include Lipari, Iannola, Bazzana, and Di Stefano. Service: Serves as TPC member for IEEE MSWIM 2025 and ACM IEEE MASS 2025. Active in promoting edge computing through conferences and startup collaborations. Lab Highlights: The Edge Computing Lab develops edge-native solutions for smart cities, using Docker/Kubernetes and reinforcement learning for dynamic scheduling. Current projects explore energy-efficient VR applications and collision prevention systems in glider flight via extended reality.

Giuliano Grossiمشاهده پروفایل

دانشیار

• Sparse representation models
• Signal processing
• Face recognition
• +۵ مورد دیگر

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).

Edgar Ernesto Sanchez Sanchezمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Automotive Systems
• Reliability Engineering
• +۳ مورد دیگر

Dr. Edgar Ernesto Sanchez Sanchez is an Associate Professor at the Department of Control and Computer Science (DAUIN) , Polytechnic University of Turin. He is a member of the Power Electronics Innovation Center and leads the CAD - Electronic CAD & Reliability Group . His research focuses on Artificial Intelligence and Automotive Systems reliability, particularly addressing neural network vulnerability, approximate computing reliability, and hardware Trojan mitigation. Key projects include REACT (Self-Aware Neuromorphic Architectures) and RESHIP4EU (Chip Design Training). Recent publications demonstrate expertise in deep learning reliability , RISC-V security , and hardware-software co-design . He has received Best Paper Awards at DATE 2006 and DDECS 2013. Dr. Sanchez supervises PhD students in AI reliability and cybersecurity, while teaching High Performance Computing and Computer Architectures courses. His work bridges academic research and industrial applications through collaborations with STMicroelectronics and automotive companies.

• Computer Vision
• Image Analysis
• Medical Imaging
• +۴ مورد دیگر

Domenico Tegolo is an Associate Professor in the Department of Mathematics and Computer Science at the University of Palermo (Unipa), where he teaches courses in programming, image analysis, and computer science. His office is located at Via Archirafi 3490123, Palermo, with office hours held on Wednesdays from 3:00 PM to 5:00 PM. Professor Tegolo's research spans multiple domains with a strong focus on computer vision and image analysis , particularly in medical applications. His work encompasses retinal image processing , capillaroscopy analysis , and medical diagnostics systems. He has also made significant contributions to programming education methodologies and federated learning approaches for agricultural data. His recent publications show a growing interest in large language models and their applications in specialized domains. Analysis of Professor Tegolo's publication record from the past five years reveals a diverse research trajectory that bridges computer science with practical applications in healthcare and agriculture. His work demonstrates consistent innovation in image processing techniques while adapting to emerging technologies like deep learning and large language models. The research shows particular strength in medical imaging applications, with numerous publications on retinal analysis, oral microcirculation, and diagnostic support systems. Professor Tegolo has taught a variety of courses including Theories and Techniques for Image Analysis, Programming with Laboratory, Advanced Programming, and Structured Programming for Mathematics programs. He has also previously taught Operating Systems and Programming Languages courses for Informatics programs, demonstrating breadth across computer science disciplines.