Find Academic Supervisors

Marta CatilloView profile

Researcher

• Cybersecurity
• Machine Learning
• Intrusion Detection Systems
• +3 more

Marta Catillo is a Researcher at the Department of Engineering (DING) of the University of Sannio (UNISANNIO) . She specializes in Cybersecurity , with focus on Machine Learning applications for intrusion detection , IoT security , and cloud auto-scaling mechanisms . Her research addresses challenges in Denial of Service (DoS) mitigation , anomaly detection , and deep learning architectures for security. Teaching: [803004] PROGRAMMING 1 for Electronic and Biomedical Engineering students (2025 cohort) Contact: Office hours: Thursdays 3-5 PM, Room 23, Bosco Lucarelli Palace Research trends: From 2019-2025 publications, her work spans adversarial attack resistance , collective anomaly detection , outlier-aware architectures , and empirical analysis of defense mechanisms , with recurring collaborations with Antonio Pecchia , Umberto Villano , and Massimiliano Rak . Key methodologies include deep autoencoders , hybrid detection systems , and measurement-based security evaluation . Technical Contributions: Developed the ZED-IDS framework for zero-day threat detection, MultiCIDS for multivariate time series intrusion detection, and DEFEDGE for edge-cloud security testing.

Roberto MenichettiView profile

Researcher

• Theoretical Physics
• Computational Physics
• Statistical Mechanics
• +7 more

Roberto Menichetti is a theoretical physicist at the University of Trento , affiliated with the Department of Physics and the VARIAMOLS research group . His work integrates computational physics, statistical mechanics, and machine learning to model biomolecules and soft matter systems. Prior roles include postdoctoral research at the Max Planck Institute for Polymer Research (2016-2018) and academic training at the University of Rome 'La Sapienza'. PhD in Physics (La Sapienza, 2016) BSc/MSc in Physics (La Sapienza, 2012) His research spans: Coarse-Graining Methods for polymers, colloids, and biomolecules High-throughput Molecular Dynamics Simulations in drug-membrane permeability studies Integration of Machine Learning with physical models for multi-scale analysis Applications in Biophysics (e.g., chromatin condensates, protein dynamics) Article trends highlight his focus on computational frameworks like EXCOGITO and theoretical investigations of non-equilibrium systems and allosteric regulation . He actively develops tools for systematic model reduction and conformational sampling in biomolecular systems. Contact: roberto.menichetti@unitn.it

Francesco PicarielloView profile

Researcher

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +7 more

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.

Giovanni AlbertiView profile

Professor

• Partial Differential Equations
• Inverse Problems
• Applied Harmonic Analysis
• +3 more

Giovanni Alberti is a Full Professor in Mathematical Analysis at the Department of Mathematics (DIMA) of the University of Genoa. He earned his D.Phil. at the University of Oxford and completed postdoctoral positions at École Normale Supérieure (Paris) and ETH Zurich. His research focuses on partial differential equations, applied harmonic analysis, inverse problems, and machine learning. University of Genoa MaLGa Center (Machine Learning Genoa) Mathematical Institute, Oxford Maths Department, ETH Zurich His work bridges mathematical analysis with computational methods, particularly in inverse problems , compressed sensing , and machine learning . He has developed algorithms for real-time geotechnical predictions, sparse optimization for scatterer localization, and continuous generative models. Recent publications emphasize physics-data-driven integration and low-dimensional manifolds. He received the Gioacchino Iapichino Prize (2017), Eurasian Association on Inverse Problems Young Scientist Award (2018), and an ERC Starting Grant (2021). He serves on editorial boards for journals including Inverse Problems and SIAM Journal on Imaging Sciences.

Peppino FazioView profile

Associate Professor

• Telecommunications
• Wireless Networks
• Vehicular Networks
• +9 more

Peppino Fazio is an Associate Professor at the Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice. He holds a degree in Computer Engineering (2004) and a PhD from the University of Calabria (2008). His research focuses on telecommunications, wireless networks, and quantum key distribution, with contributions to vehicular networks, cybersecurity, and IoT systems. Key career milestones include post-doctoral research in Spain (2008), involvement in EU projects (e.g., ORACOLO), and leadership in StartCup-winning ventures like SPITCH (2014). He has authored over 130 publications, with recent work addressing quantum networks, deep learning for intrusion detection, and multi-layer robotic swarm management. Received StartCup Calabria 2012 and Lamarck Award 2014. Co-founder of SPITCH and developer of the Intelligent Remote Controller (IRC). His research spans theoretical and applied domains, including network emulation, mobility prediction in 5G/6G systems, and medical neuroscience modeling through network operators.

• 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.

• Probability
• Mathematical Statistics
• Entropy Measures
• +3 more

Francesco Buono is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) within Politecnico di Torino . His research focuses on probability theory, mathematical statistics, and entropy-based measures, with a particular emphasis on reliability engineering and stochastic modeling. He has published extensively on topics such as extropy, Kullback-Leibler divergence, and generalized entropy in applications to system reliability and ranked set sampling. His recent publications (2023–2025) highlight collaborations with researchers like Franco Pellerey, Jorge Navarro, and Maria Longobardi. Key trends include advancements in stochastic orders , predictive modeling , and information-theoretic measures applied to failure analysis and statistical inference. Notable journals include Entropy , Communications in Statistics , and Journal of Applied Probability .

Pasquale DaponteView profile

Professor

• Electrical Measurements
• Electronic Measurements
• Signal Processing
• +5 more

Pasquale Daponte is a Full Professor in the Department of Engineering at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His research focuses on measurement systems, signal processing, and instrumentation with particular expertise in analog-to-digital conversion technologies, compressed sensing applications, and unmanned aerial vehicle systems. Professor Daponte's research interests span several key areas in electrical measurements and instrumentation: Dynamic characterization methods for analog-to-digital and digital-to-analog converters Algorithms for numerical processing of measurement signals Architectures and software for distributed measurement systems Distance learning systems in engineering education Applications of compressed sensing in various measurement contexts UAV-based measurement systems His publication record demonstrates consistent contributions to measurement science, particularly in converter testing methodologies and standards development. Professor Daponte has been actively involved in IEEE standardization efforts, contributing to key documents on analog-to-digital and digital-to-analog converter testing. His recent work shows a growing emphasis on compressed sensing applications across multiple domains including spectrum monitoring, ECG signal processing, and radio frequency localization. Professor Daponte maintains an active research group with frequent collaborations with colleagues including Luca De Vito, Sergio Rapuano, and Francesco Picariello. His work bridges theoretical measurement principles with practical engineering applications across biomedical, communications, and industrial domains.

• Electrical and Electronic Measurement
• Compressive Sampling
• Data Acquisition Systems
• +5 more

Ioan Tudosa is a Research Fellow at the Department of Engineering, University of Sannio, specializing in electrical/electronic measurements and signal processing. His research focuses on developing advanced measurement systems using compressive sampling techniques, with applications spanning biomedical instrumentation, IoT security, and precision metrology. Research Focus Tudosa's primary research investigates: Novel compressive sampling architectures for data acquisition systems High-accuracy phase measurement instruments (collaborating with NIST on a $1M project) IoT security solutions through hardware measurement techniques Biomedical monitoring systems for ECG and pulse wave analysis Machine learning applications in measurement science His work bridges theoretical signal processing with practical instrumentation design for industrial and medical applications. Publication Trends Tudosa's recent publications demonstrate three key trends: Advancements in compressive sensing for spectrum monitoring and biomedical signal acquisition Integration of machine learning with measurement systems for anomaly detection Development of IoT-enabled monitoring platforms for structural health and industrial safety His research shows consistent focus on improving measurement accuracy while reducing system complexity and power consumption. Research Infrastructure Tudosa collaborates with the National Institute of Standards and Technology (NIST) and leads projects developing specialized testbeds for: High-frequency phase measurement Wireless spectrum monitoring Structural health monitoring using IoT networks Wearable biomedical sensors

• Electrical Measurements
• Electronic Measurements
• Data Acquisition Systems
• +9 more

Eulalia Balestrieri is an Associate Professor in the Department of Engineering at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). She teaches courses including Data Acquisition Systems for Biomedical Applications and Fundamentals of Measurement across various engineering programs, with office hours held Tuesdays from 10:00 to 12:00 at the Bosco Lucarelli Palace. Her research spans several key areas in electrical and electronic engineering: Measurement theory and instrumentation Analog-to-Digital and Digital-to-Analog converter technologies Compressive sampling and signal processing techniques Biomedical measurement systems, particularly for ECG monitoring RF emitter localization and spectrum analysis Biomedical image analysis including morphometric parameter measurements Professor Balestrieri's recent publications demonstrate a strong focus on applying advanced measurement techniques to biomedical applications and communication systems. Her work on compressive sampling for ECG monitoring represents innovative approaches to reducing data requirements in healthcare technology. She has made significant contributions to IEEE standards for measurement devices, establishing herself as an authority in measurement methodology. Her 2023 research on fish blood cell analysis shows expansion into biomedical image processing applications. Her extensive collaborative research shows strong partnerships with colleagues including De Vito, Daponte, Picariello, Rapuano, and Tudosa, resulting in numerous joint publications across multiple research areas. The 2025 publication on cable diagnosis using machine learning demonstrates her ongoing innovation in measurement techniques.

Simone FaroView profile

Associate Professor

• Algorithmic solutions for text-processing
• Natural language processing
• Algorithmics in quantum domain
• +12 more

Simone Faro is an Associate Professor at the University of Catania , affiliated with the Department of Mathematics and Computer Science . He has held this position since 2015, after serving as a University Researcher from 2006 to 2015. Teaches Algorithms and Laboratory (Bachelor's L31) and Quantum Computer Programming (Master's LM18) Also teaches Computer Science (Bachelor's L20) and previously held Computational Linguistics (Master's LM49) at the Department of Humanities Research Focus : Algorithmic solutions for text-processing and natural language processing Combinatorial algorithms on graphs and networks Privacy-preserving text processing Bit-parallel automata simulations Quantum domain algorithm design Research Projects : Co-Principal Investigator for STORAGE (2020), University of Catania Responsible for Scientific Degrees Plan (PLS 2019/21) Component in Godscapes project (2020) and COMPACT project (2015) Principal Investigator for multiple string matching and secure text processing projects Editorial Roles : Guest Editor for Algorithms journal (MDPI) special issues Editor for String Processing and Combinatorial Algorithms (Springer Nature) Member of Le Matematiche editorial team Contact: faro@dmi.unict.it | simone.faro@unict.it

Matteo SantacesariaView profile

Associate Professor

• Inverse Problems
• Mathematical Analysis
• Medical Imaging
• +3 more

Matteo Santacesaria is an Associate Professor at the Department of Mathematics (DIMA) of the University of Genoa. His research focuses on inverse problems, mathematical analysis, and their applications in medical imaging and machine learning. He teaches courses such as Mathematics for Architectural Sciences, Mathematical Analysis I, and Company Internship for Mathematics students. His work bridges theoretical mathematics with practical applications, including compressed sensing, MRI and CT reconstruction, and deep learning techniques. Recent projects involve developing algorithms for artifact correction in medical imaging and sparsity-promoting regularizers for inverse problems. He actively contributes to international conferences like SSVM and collaborates on manifold learning and variational methods. His research emphasizes interdisciplinary solutions for imaging challenges and optimization in inverse problem frameworks.

Sergio RapuanoView profile

Professor

• Instrumentation and Measurement
• Analog-to-Digital Conversion
• Biomedical Measurement Systems
• +2 more

Sergio Rapuano is a Full Professor in the Department of Engineering at the University of Sannio, Italy, specializing in Instrumentation and Measurement. With approximately 270 publications including journal papers, conference proceedings, book chapters, and IEEE standards, he coordinates nationally and internationally funded research projects focused on measurement systems and signal processing. His research spans analog-to-digital conversion characterization, biomedical measurement systems, distributed measurement networks, and telecommunications instrumentation. Recent work shows a pronounced shift toward compressed sensing applications for cable diagnostics, spectrum monitoring, and biomedical signal processing, reflecting his focus on efficient data acquisition in constrained environments. He also contributes significantly to distance learning methodologies in electrical measurement education. Prof. Rapuano actively shapes the field through IEEE Technical Committee 10 (Waveform Generation, Measurement, and Analysis), where he drives standardization efforts and conference organization for the Instrumentation and Measurement Society. His leadership in waveform measurement standards and traceability frameworks demonstrates his commitment to advancing measurement science globally.

• Electrical and Electronic Measurements
• Signal Processing
• Compressed Sensing
• +5 more

Luca De Vito is an Associate Professor in the Department of Engineering (DING) at the University of Sannio, Italy, specializing in Electrical and Electronic Measurements (ING-INF/07). He maintains an active research profile with over 250 publications spanning more than two decades, demonstrating sustained contribution to the field of measurement science and signal processing. His research focuses on: Advanced measurement techniques using compressed sensing Analog-to-Digital and Digital-to-Analog Converter testing methodologies Signal processing for telecommunications and biomedical applications Spectrum monitoring and cognitive radio systems Measurement uncertainty analysis De Vito's work shows a clear evolution from fundamental measurement standards development to innovative application of compressed sensing techniques across diverse domains. His recent research (2020-2024) demonstrates a sophisticated integration of compressed sensing with practical measurement challenges, significantly reducing testing time while maintaining accuracy in areas ranging from DAC nonlinearity testing to ECG signal processing and structural health monitoring. He has made substantial contributions to IEEE standards, particularly in converter testing methodologies. Throughout his career, De Vito has maintained a productive research collaboration with a core group including Pasquale Daponte, Sergio Rapuano, and Francesco Picariello. His work bridges theoretical measurement principles with practical engineering applications, making significant contributions to both academic knowledge and industrial measurement practices.

Giulio Ermanno PIBIRIView profile

Associate Professor

• Computer Science
• Bioinformatics
• Algorithms
• +5 more

Giulio Ermanno PIBIRI is an Associate Professor at the Department of Environmental Sciences, Computer Science and Statistics of Ca' Foscari University of Venice. His primary research focuses on algorithms, data structures, and their applications in bioinformatics, genomics, and information retrieval. He specializes in developing efficient methods for handling large-scale genomic data and optimizing storage and querying of massive datasets. His work includes advancements in minimal perfect hashing, k-mer indexing, and compression techniques for de Bruijn graphs. Key research areas include: Algorithm design for bioinformatics (e.g., sequence analysis, genome assembly) Data compression and indexing (inverted indexes, variable-byte codes) Efficient storage and retrieval of genomic and textual data Secure machine learning (verifiable tree ensembles) Recent publications highlight contributions to: Meta-colored de Bruijn graphs for genomic data representation Efficient k-mer hashing and sampling algorithms Secure and robust machine learning models Teaching includes courses on computer programming and foundational computer science topics for engineering students.