جست‌وجوی استاد و پژوهشگر

Francesco Picarielloمشاهده پروفایل

پژوهشگر

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +۷ مورد دیگر

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Aerospace control systems
• Autonomous and assisted vehicles
• Biomedical engineering
• +۸ مورد دیگر

Carlo Novara is a Full Professor at Politecnico di Torino, affiliated with the Department of Electronics and Telecommunications (DET) and the Interdepartmental Center CARS@PoliTO - Center for Automotive Research and Sustainable Mobility. He serves as a member of the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. His research interests span aerospace control systems, autonomous and assisted vehicles, biomedical engineering, design of experiments, energy system control and optimization, model predictive control, modeling and simulation, set membership estimation, statistical analysis, system identification, machine learning, complex networks and systems, prediction and estimation, and quantum optimization. His work focuses on nonlinear and LPV system identification, filtering/estimation, time series prediction, nonlinear control, predictive control, data-driven methods, set membership methods, sparse methods, and nonlinear optimization with applications in automotive, aerospace, biomedical, and energy domains. His recent publications demonstrate strong trends in nonlinear model predictive control, physics-based system identification, and space applications. Many papers focus on computational efficiency for real-time control, with increasing emphasis on quantum optimization techniques and space mission applications like the LISA mission. His work bridges theoretical control methods with practical applications in automotive, aerospace, and energy systems. Professional Recognition: Member of IEEE TC on System Identification and Adaptive Control Member of IFAC TC on Modelling, Identification and Signal Processing Founding member of IEEE-CSS TC on Medical and Healthcare Systems Professor Novara has supervised numerous PhD students including Lucrezia Lovaglio, Giovanni Marinello, Francesco Cerrito, Sabrina Savino, Cesare Donati, and Mattia Boggio. His research has been supported by significant grants including the CNMS - Spoke 2 Sustainable Mobility Center (2022-2025), PRYSTINE ECSEL project (2018-2021), and multiple commercial research contracts with ESA and other aerospace organizations. He leads research in the Automatica research group at DET, focusing on advanced control algorithms for aerospace applications, data-driven control of autonomous vehicles and biomedical systems, quantum optimization for complex system design, and modeling and optimization of networked systems.

• Intra-Body networks
• 5G Radio Technologies
• Cognitive Radio Networks
• +۳ مورد دیگر

Anna Vizziello serves as Assistant Professor at the Department of Industrial and Information Engineering, University of Pavia, specializing in intra-body networks, 5G radio technologies, and cognitive radio systems. Selected as an EU Commission expert since 2013, she actively referees research proposals and contributes to global telecommunications standards through IEEE committees. Her educational foundation includes a Laurea in Electronic Engineering and Ph.D. in Electronics and Computer Science, both from the University of Pavia. International research experience spans Northeastern University (2023, 2016, 2011), Georgia Tech's Broadband Wireless Lab (2009-2010), and UPC Barcelona (2009-2010), building on earlier work at EUCENTRE on emergency communications. Dr. Vizziello's research program bridges theoretical innovation with practical implementation. Her intra-body communication testbed—requiring only two PCs, a cable, and electrodes—enables accessible experimentation in neural interfaces and implantable devices. In 5G/mmWave systems, she pioneered Kalman-based hybrid precoding techniques that optimize spectral efficiency while reducing hardware complexity. Current investigations integrate neuromorphic computing with wireless networks to achieve unprecedented energy efficiency in biomedical telemetry. Recent publications reveal converging trends across biomedical wireless systems (galvanic coupling for EMG transmission), AI-enhanced communications (spiking neural networks for data compression), and environment-aware networking (weather-impacted LoRaWAN forecasting). These works demonstrate her signature approach: solving real-world constraints through cross-disciplinary innovation. Her recognition includes: N2Women Rising Star in Networking (2018) ACM NanoCom Best Paper Award (2021) Top-Cited Internet Technology Letters Paper (2019-2020) Outstanding Research Visitor at Northeastern University (2015-2016) As N2Women Award Co-chair (2021) and TPC member for 40+ conferences (IEEE ICC, GLOBECOM), she actively shapes the field. Her IEEE senior membership spans WIE and Green Communications committees, while tutorial invitations (IEEE MELECON, LATINCOM, CCNC) highlight her thought leadership in intra-body networks. Though specific grants aren't detailed, her extensive conference leadership and award record indicate substantial research funding. Collaborative frameworks include Northeastern University's GENESYS Laboratory and Georgia Tech's Broadband Wireless Networking Lab, where she develops open-source testbeds shared via IEEE and Code Ocean. Future work targets neural communication interfaces and beyond-6G satellite-terrestrial integration.

Werner Nuttمشاهده پروفایل

استاد

• Data Management
• Knowledge Representation
• Intelligent Information Extraction
• +۳ مورد دیگر

Werner Nutt is a Professor at the Free University of Bozen-Bolzano , Italy, affiliated with the Faculty of Computer Science since 2005. He previously held academic positions as a Reader at Heriot-Watt University (2000-2005), Visiting Professor at Hebrew University of Jerusalem, and Research Scientist at DFKI (1992-2000). Current Role: Professor, Free University of Bozen-Bolzano Past Roles: Reader (Heriot-Watt), Visiting Professor (Hebrew University), Research Scientist (DFKI) Research Interests focus on Data Management , Knowledge Representation , and Intelligent Information Extraction , with emphasis on modeling Construction Processes and ensuring Data Quality . His work bridges Semantic Web technologies with Business Process Management , notably through the COCkPiT project (2017-present) for construction process optimization. Key Contributions include foundational work on Query Completeness in databases, SPARQL Reasoning , and Semantic Diagnostics . He has published extensively in venues like ISWC , BPM , and CIKM , with an h-index of 35 on Google Scholar. His 154+ publications span topics from Probabilistic XML to Construction Process Modeling .

Yashar Deldjooمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

• Chaos theory
• Compressed sensing
• Electromagnetic interference
• +۱۴ مورد دیگر

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.

Christian Micheloniمشاهده پروفایل

دانشیار

• Pattern Recognition
• Active Vision
• Neural Networks
• +۳ مورد دیگر

Christian Micheloni is an Associate Professor at the University of Udine's Department of Mathematics and Computer Science. He holds a Laurea (cum Laude) and Ph.D. in Computer Science from the University of Udine (2002 and 2006 respectively). His research focuses on Active Vision, Pattern Recognition, and Resource Aware Networks. He has been involved in European projects like MUSAS (2008-2010) and HAMLet (2006-2008), and collaborates with international research centers and industry. Key research areas include person re-identification using neural networks, trajectory analysis, and camera network optimization. He has received the Best Paper Award at the International Conference on Distributed Smart Cameras (2014) and serves as an editor for the IEEE Journal of Selected Topics in Signal Processing. His work emphasizes real-time surveillance systems and adaptive sensor networks to manage computational and power constraints. Micheloni teaches courses like Artificial Vision and advises post-doctoral researchers. He maintains an active lab (AVires Lab) and offers post-doctoral positions. His publications span journals like IEEE Transactions on Pattern Analysis and Machine Intelligence, and conferences such as CVPR and ICCV.

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).

Gabriel E. Hineمشاهده پروفایل

پژوهشگر

• Biometrics
• Signal Processing
• Information Security
• +۱۳ مورد دیگر

Gabriel E. Hine is a Postdoc researcher at the Biometric Systems and Multimedia Forensics Lab within the Department of Engineering at Roma Tre University, Italy. He is actively engaged in research at the intersection of signal processing, biometrics, and information security, and contributes to teaching in advanced signal processing and biometric systems. Bachelor’s Degree in Electronic Engineering, Roma Tre University (2013, cum laude) Master’s Degree in Information and Communication Technology Engineering, Roma Tre University (2015, cum laude) PhD in Applied Electronics, Roma Tre University (2018) Research collaboration at Telefonica I+D, Barcelona (2016) Research involvement in EU H2020 ENCASE and PRIN 2011/2012 projects His research focuses on biometric cryptosystems, template protection, and privacy-preserving biometric recognition, with significant contributions to fingerprint, palmprint, EEG, and signature-based systems. He has also conducted influential work on online social media dynamics, particularly analyzing 4chan’s /pol/ forum. The analysis of his recent publications reveals a strong trend in developing secure and unlinkable biometric templates using deep learning, sparse signal representations, and zero-leakage frameworks. His work spans both theoretical formulation and experimental validation on real-world data, particularly in finger-vein and palm-vein recognition. He also explores novel biometric modalities such as in-air 3D signatures and resting-state EEG. Best Paper Runner-Up, ICWSM-17 (2017) Selected as 'Best of Arxiv' paper Widely covered in Nature , MIT Technology Review , The Independent , Vice , and Italian media Hine has served as a lecturer and teaching assistant for courses including 'Biometric Systems' and 'Signal Theory' at Roma Tre University. He has led a didactic laboratory on biometric systems, guiding students in implementing and analyzing biometric recognition solutions. His research is supported by major grants such as EU H2020 ENCASE and PRIN 2011/2012. He collaborates extensively with Prof. Patrizio Campisi and other researchers in the BioMedia4n6 lab. He is a core member of the BioMedia4n6 research group, which specializes in biometrics, data hiding, and blind deconvolution, applying advanced signal processing techniques to multimedia forensics and security challenges.

Luca Cosmoمشاهده پروفایل

دانشیار

• Shape Analysis
• Geometric Deep Learning
• Spectral Geometry
• +۳ مورد دیگر

Luca Cosmo is an Associate Professor at Ca’ Foscari University of Venice, specializing in Shape Analysis, Geometric Deep Learning, and Spectral Geometry. His research focuses on 3D shape analysis, spectral methods, and generative models for computer vision tasks. He has contributed to projects like the Average Mixing Kernel Signature (AMKS) and isospectralization techniques, advancing robust shape representation and correspondence. Previously, he held postdoctoral positions at the Geometric Deep Learning group under Prof. Michael Bronstein and collaborated with the spin-off DigitalViews Srl on industrial projects for quality control. He actively serves on program committees for top conferences (CVPR, ICCV, ECCV) and journals (IJCV, PR). His work bridges spectral geometry with machine learning, addressing challenges in partial shape matching, adversarial attacks on deformable shapes, and latent space preservation in generative models. Key trends in his publications include advancements in 3D shape generation (e.g., UniMoGen, 3D-WAG), spectral methods for subgraph learning, and quantum-inspired approaches to shape analysis. He has organized tutorials on shape analysis and contributed benchmark datasets like SHREC’16/17. His research emphasizes practical applications in computer graphics, robotics, and industrial automation. Luca’s advising and grants involve collaborations with institutions like Oxford University (Antonio Norelli) and industry partners. His lab, GLADIA, focuses on geometric deep learning and generative models. Current work includes universal motion generation, high-fidelity 3D reconstruction, and scalable generative models for large datasets.

Laura Palagiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Mixed Integer Nonlinear Programming
• Semidefinite Programming
• +۷ مورد دیگر

Laura Palagi is a Full Professor at Sapienza University of Rome, Department of Computer, Automation, and Management Engineering "Antonio Ruberti". She has been with the institution since 1996, progressing from Researcher to Associate Professor in 2005, and Full Professor since March 2020. She currently serves as Coordinator of the PhD program in Automatic Control, Bioengineering and Operations Research (ABRO) since February 2024 and is the GEP committee representative at the DIAG Department. Her research focuses on developing and analyzing methods for solving nonlinear optimization problems with continuous and discrete variables. Key research themes include decomposition methods for training Support Vector Machines and Deep Networks, methods for Mixed Integer Nonlinear Programming (MINLP), Semidefinite Programming (SDP), standard quadratic problems (StQP), and convex quadratic constrained problems. She also develops predictive machine learning models for healthcare applications and surrogate models for engineering. Her recent publications span optimization methods, machine learning applications in healthcare, sustainability assessment, and network design. Her work demonstrates a strong integration of theoretical optimization with practical applications across multiple domains. Continuous Optimization Awards 2020 Member Best Paper Award from Journal of Global Optimization Honorable Mention from Marguerite Frank Award jury for Best EJCO Paper 2024 EURO Doctoral Dissertation Award Professor Palagi teaches multiple courses including Optimization and Decision Science, Laboratory of Decision Science, Optimization Methods for Machine Learning, and Optimization for Medical programs. She serves as thesis advisor for numerous Bachelor's and Master's students in Management Engineering, Mechanical Engineering, and Data Science programs.

Giovanni Trappoliniمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +۹ مورد دیگر

Giovanni Trappolini is an Assistant Professor in the Department of Computer, Control, and Management Engineering at Sapienza University of Rome, where he conducts research in the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He earned his PhD in Machine Learning from Sapienza in 2022, focusing on Geometric Deep Learning under Prof. Emanuele Rodolà. Trappolini has collaborated with leading institutions such as Stanford, Technion, Meta, Amazon, TII, and the University of Pisa, contributing to cutting-edge research in AI and information retrieval. His research interests include machine learning, deep learning, geometric deep learning, multimodal AI, information retrieval, and large language models. He has pioneered work on Retrieval-Augmented Generation (RAG), Neural Databases, and the development of Fauno, the leading Italian LLM. His teaching experience includes courses on Python for Data Science, Advanced Data Mining, and Statistical Learning at both Sapienza and Luiss Guido Carli. Trappolini's recent publications span top-tier venues like SIGIR, NeurIPS, ECCV, and IEEE Transactions, with a focus on graph neural networks, federated learning, anomaly detection in 5G networks, and multimodal systems. His work shows a strong trend toward robust, privacy-preserving, and multimodal AI systems with real-world applications in language, vision, and network security. Sapienza honor graduate IELTS 8.0 He has advised several teaching roles and delivered courses on Python, data science, and statistical learning. His research is supported through collaborations with industry leaders like Meta and Amazon, and he leads innovative projects such as Fauno and Neural Databases. Trappolini is actively involved in the AI research community and continues to push the boundaries of language and retrieval systems. He is a core member of the RSTLess Lab and has been instrumental in advancing geometric and multimodal deep learning at Sapienza University. His work bridges theoretical innovation with practical implementation, particularly in the domains of Italian language modeling and secure, efficient information retrieval systems.

Marco Frascaمشاهده پروفایل

دانشیار

• Artificial Intelligence in Biology & Medicine
• Deep Learning
• Deep Neural Network Compression
• +۷ مورد دیگر

Marco Frasca is an Associate Professor in the Department of Computer Science at the University of Milan. He is a member of the AnacletoLab (Computational Biology and Bioinformatics Lab) and holds a Ph.D. in Computer Science from the University of Milan. His research focuses on machine learning applications in bioinformatics, including artificial intelligence for medicine, deep learning, and neural network compression. He has held visiting positions at institutions such as the University of Toronto and Johannes Gutenberg University. Education: Ph.D. in Computer Science (2012), University of Milan M.Sc. in Computer Science (2005), University of Salerno Research Interests: Frasca's work bridges machine learning and computational biology, emphasizing methods for imbalanced data, hierarchical classification, and biomolecular network analysis. He develops algorithms for gene function prediction, disease-gene prioritization, and compressed deep learning models. His tools, such as COSNet and UNIPred-Web, are widely used in bioinformatics. Grants & Projects: Leader of the OPTIMA-NASH project (Pfizer-funded), integrating AI for NASH management PRIN Project 2017 (Italian Ministry of Education) on data structures and algorithms Labs & Teams: Frasca leads the AnacletoLab, focusing on AI-driven solutions for biology and medicine. The lab collaborates internationally on projects like CAFA3 and the EPIGEN flagship.

• Machine Learning
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Claudio Gallicchio is an Associate Professor of Deep Learning at the Department of Computer Science, University of Pisa, Italy. His research focuses on Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems, with specialization in Dynamical Neural Networks, Reservoir Computing, and Graph Learning. He actively contributes to IEEE Task Forces on Reservoir Computing and Randomized Neural Networks, and is a member of several AI societies including AAAI and ENNS. He holds a Ph.D. in Computer Science from the University of Pisa. His work emphasizes addressing challenges in graph neural networks—such as oversquashing—and advancing energy-efficient neural architectures using memristive systems. He supervises students on topics like Recurrent Neural Networks, Neural ODEs, and Deep Learning for Digital Humanities. Gallicchio's recent publications explore Reservoir Computing applications in industrial processes, non-dissipative graph networks, and federated learning. His research bridges theoretical foundations (e.g., Hamiltonian architectural bias) with practical implementations (e.g., memristor-based models). He has pioneered frameworks like GRAMA for adaptive graph time-series analysis and the TEACHING platform for human-centric autonomous systems.