Find Academic Supervisors

• Computer Networks
• Edge Computing
• Internet of Things (IoT)
• +5 more

Paolo Bellavista is a Full Professor of Distributed and Mobile Systems at the Department of Computer Science - Science and Engineering, Alma Mater Studiorum – University of Bologna. His research focuses on edge computing, federated learning, IoT, digital twins, and blockchain applications. With over 140 journal articles and 200+ conference papers, his work appears in top venues such as ACM Computing Surveys and IEEE Transactions. He serves as Editor-in-Chief of the MDPI Computers Journal and holds editorial roles in IEEE Communications Surveys&Tutorials and ACM Computing Surveys. His contributions span network optimization, cybersecurity, and smart cities, with notable achievements in citation metrics (h-index 46 on Google Scholar). Research interests include distributed systems, vehicular networks, AI-driven security, and QoS-aware edge infrastructures. He has organized major conferences like IEEE Mobile Cloud and ACM ICDCN. Current projects explore federated learning unlearning, digital twin entanglement, and blockchain-based data spaces. His work bridges theoretical foundations with real-world deployments in smart manufacturing and healthcare. Editorial Roles : MDPI Computers (Editor-in-Chief), IEEE Communications Surveys&Tutorials (Associate Editor), ACM Computing Surveys (Editor) Conference Leadership : Technical Program Chair for IEEE Mobile Cloud 2015, General Co-Chair for SCNS 2018 Key Contributions : Over 140 journal papers, 15 special issues as guest editor, and 200+ conference publications His lab develops middleware for edge-cloud continuum applications and collaborates internationally on projects like InAbled Cities. Office hours are held weekly at the University of Bologna’s DISI department.

• Decentralized Machine Learning
• Federated Learning
• Industry 4.0
• +3 more

Alessio Mora is a postdoctoral researcher at the Department of Computer Science and Engineering (DISI), University of Bologna, specializing in decentralized learning techniques such as Federated Learning. His work focuses on communication efficiency, heterogeneous data handling, and distributed AI systems. PhD in Computer Science and Engineering, University of Bologna (2023) Master in Computer Engineering, University of Bologna (2019) Bachelor in Computer Engineering, University of Bologna (2016) His research spans decentralized learning, federated learning, and their applications in Industry 4.0, IoT, and edge computing. Key themes include model optimization, communication efficiency, and heterogeneous data distribution in cross-device settings. Recent publications highlight trends in federated learning infrastructure, including unlearning methods, knowledge distillation, and sparse compression techniques. His work integrates theoretical analysis with practical implementations in industrial and IoT contexts. Summer of Reproducibility Award – Flower Labs (2023) Awarded $2,000 USD for implementing and reproducing the FedMLB baseline in Flower, advancing reproducibility in federated learning. Mora has contributed to European projects like OntoTrans and SimDOME, focusing on ontology-based systems for materials modeling and industrial data translation. His technical expertise includes Docker, Stardog triplestore, FastAPI, and Python wrappers for chemical simulation tools.

• Aerospace control systems
• Autonomous and assisted vehicles
• Biomedical engineering
• +8 more

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.

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.

• Statistics
• Statistical Inference
• Time-Series Analysis
• +3 more

Emanuele Taufer is a Full Professor of Statistics at the Department of Economics and Management of the University of Trento. His academic career includes roles as Vice Director of the Department of Computer Science and Business Studies and Faculty Delegate for International Relations. He holds a Ph.D. in Statistics from Cardiff University, an M.Sc. in Mathematical Statistics from George Washington University, and a Laurea in Economics from the University of Trento. His research focuses on statistical inference, stochastic processes, goodness-of-fit tests, and applications in ESG analysis. Notable contributions include work on exponentiality testing, graphical models, and financial dependence modeling. He has been recognized for his 2002 paper on mean residual life characterization at the SIS2002 conference. Recent research trends emphasize methodological advancements in ESG performance measurement, sparse network estimation for heavy-tailed data, and generalized precision matrices for financial risk modeling. His work spans theoretical statistics, applied econometrics, and interdisciplinary topics like environmental governance. Education: Ph.D. in Statistics, Cardiff University (UK) M.Sc. in Mathematical Statistics, George Washington University (USA) Laurea in Economics, University of Trento (Italy) Professional Roles: Full Professor of Statistics at University of Trento (2003–present) Associate Professor (2003–2003), Assistant Professor (1996–2002) Awards: 2002 SIS2002 Recognition for innovative statistical testing methodology Key Research Themes: Stochastic processes and estimation ESG methodology and financial reporting High-dimensional data analysis Goodness-of-fit tests and tail index estimation

• Intra-Body networks
• 5G Radio Technologies
• Cognitive Radio Networks
• +3 more

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.

• Numerical Analysis
• Inverse Problems
• Microwave Imaging
• +3 more

Claudio Estatico is a Full Professor at the Department of Mathematics (DIMA) at the University of Genoa, Italy. His academic activities include teaching courses such as Mathematical Analysis 1B for Electrical Engineering and Numerical Analysis for graduate programs in Mathematics. His research focuses on inverse problems, numerical analysis, and microwave imaging applications. Recent publications highlight advancements in: Microwave inversion for medical diagnostics (stroke detection) Data-driven approaches for plant diagnostics Regularization techniques in Banach spaces Subsoil electromagnetic data inversion He actively collaborates with researchers like Alessandro Fedeli, Andrea Randazzo, and Giuseppe Rodriguez, with recent outputs spanning both theoretical and applied domains of computational mathematics.

Stefano QuerView profile

Associate Professor

• Computer Science
• Electrical Engineering
• Formal Methods
• +7 more

Stefano Quer is an Associate Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He holds a PhD in Electronic Engineering from the same institution and has been affiliated with DAUIN since 1996. Researcher (1996-2000) Associate Professor (2000-present) Visiting Faculty at UC Berkeley (1994-1995) Research Interests His work spans Formal Verification BDD/SAT Techniques Embedded Systems Hardware/Software Co-Verification Parallel Computing with applications in VLSI CAD, industrial IoT, and energy-efficient systems. Recent articles focus on GPU-accelerated graph algorithms, wireless sensor calibration, and AI-driven test optimization. Scientific Awards Best Paper Award, IEEE EURO-DAC'94 Academic Contributions Supervised PhD students Lorenzo Cardone and Andrea Calabrese Member of DATE, ICSOFT Technical Program Committees Topical Advisor for Sensors MDPI 60+ publications in IEEE/ACM venues

• Complex networks
• Multi-agent systems
• Nonlinear control
• +5 more

Anton Proskurnikov is an Associate Professor in the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. His academic appointment spans the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. Based in office L.240, he maintains an active research program focused on control theory with applications to complex networked systems. Dr. Proskurnikov's research centers on complex networks, multi-agent systems, nonlinear control, and social network analysis, with significant contributions to understanding opinion dynamics and consensus mechanisms. His work bridges theoretical control engineering with practical applications in social dynamics, earning recognition through prestigious awards including the O. Hugo Schuck Best Paper Award (2023). His expertise spans control theory, optimization, and network analysis with implications for sustainable infrastructure and smart cities. His publication trajectory reveals a sophisticated evolution from foundational control theory toward increasingly complex applications in social dynamics and networked systems, with recent emphasis on bounded confidence models, higher-order interactions in social dynamics, and quantum optimization for complex system design. This interdisciplinary approach connects traditional control engineering with emerging challenges in social network analysis. Dr. Proskurnikov has received multiple significant awards: O. Hugo Schuck Best Paper Award (2023) from the American Automatic Control Council EPEPS Best Paper Award (2020) from EPEPS Conference Annual Reviews in Control Paper Prize (2020) from IFAC As an academic advisor, Dr. Proskurnikov currently supervises PhD student Jinze Wu in Electrical, Electronics, and Communications Engineering. His research is supported by the National Research PRIN project "Higher-order interactions in social dynamics with application to monetary networks" (2023-2025), for which he serves as Scientific Manager. He contributes to editorial boards of leading journals including IEEE TRANSACTIONS ON AUTOMATIC CONTROL and INTERNATIONAL JOURNAL OF CONTROL, reflecting his standing in the control theory community. Dr. Proskurnikov is an active member of the Automatica research group within the Department of Electronics and Telecommunications, focusing on systems, automation, and control. His collaborative work extends internationally to institutions including TU Delft, St. Petersburg National Research University, and the University of Groningen, demonstrating his strong international research presence and interdisciplinary approach to complex networked systems.

• Numerical Analysis
• Machine Learning
• Computational Mathematics
• +4 more

Elena Toscano is a researcher in the Department of Mathematics and Computer Science at the University of Palermo. She specializes in numerical analysis, machine learning, and computational mathematics, with a focus on mesh-free methods like Smoothed Particle Hydrodynamics (SPH) and applications to physics, engineering, and interdisciplinary fields. Teaching: Numerical Analysis (Master's in Informatics and Mathematics, 2025/2026) Research Areas: Signal/image processing, SPH consistency restoration, genetic algorithms for tomography, and mathematical-literary collaborations (e.g., Oulipo). Her publications span computational physics, machine learning, and mathematical modeling, emphasizing numerical stability and interdisciplinary innovation.

Werner NuttView profile

Professor

• Data Management
• Knowledge Representation
• Intelligent Information Extraction
• +3 more

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 .

• Numerical Analysis
• Optimal Control
• Finite Element Methods
• +9 more

Enrico Bertolazzi is an Associate Professor in Numerical Analysis at the University of Trento's Department of Industrial Engineering. He specializes in numerical methods for optimal control, finite element methods, sparse matrix computations, and symbolic computation. His work integrates advanced programming skills in C/C++, MATLAB, and other languages to develop computational tools for engineering and mathematical challenges. Bertolazzi's research addresses complex systems such as vehicle dynamics, trajectory optimization, and real-time simulations, with applications in automotive engineering, renewable energy, and robotics. Research Interests: Numerical Optimal Control and Trajectory Planning Finite Element/Finite Volume Methods for PDEs Sparse Matrix Algorithms Clothoid Curve Modeling Symbolic-Numeric Hybrid Techniques Vehicle Dynamics and Autonomous Systems Publications: Recent work focuses on real-time tire models, DAE index reduction, and autonomous vehicle path planning. His contributions span algorithm development, computational toolboxes (e.g., TrussMe-Fem), and interdisciplinary applications combining mathematics with engineering. Grants & Advising: Advises on optimal control, robotics, and simulation projects. Active in developing educational software like the virtual racing car championship platform. Labs/Teams: Involved in research groups focused on intelligent vehicles, computational mechanics, and numerical methods for industrial applications.

Gianluigi PillonettoView profile

Assistant Professor

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +3 more

Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.

Mattia ZorziView profile

Associate Professor

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +8 more

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

Yashar DeldjooView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +7 more

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.