Find Academic Supervisors

• Statistical Modeling
• Biostatistics
• Environmental Data Analysis
• +4 more

Prof. Vito Michele Rosario Muggeo is a Full Professor in the Department of Economics, Business and Statistics at the University of Palermo. His research focuses on statistical modeling, biostatistics, environmental data analysis, and regression techniques. He teaches courses such as Statistical Inference/Statistics 2 and Statistical Methods. Active in academic evaluations, his work spans over two decades with publications in statistical methodologies, public health, and environmental science. Notable contributions include advancements in segmented regression, quantile regression, and applications in healthcare and ecology. His research often involves collaboration with international journals and conferences, addressing topics like cardiovascular health in cancer survivors and pandemic modeling. He is also involved in software development for statistical analysis, contributing R packages like 'segmented' and 'quantregGrowth'. Education & Professional Experience: - Full Professor at University of Palermo (STAT-01/A) - Active in teaching and research since at least 2000, evidenced by publications from 2000s onward. Research Interests: - Development of statistical methodologies for complex data structures. - Applications in public health, oncology, and environmental studies. - Quantile regression, penalized regression, and segmented modeling techniques. Recent Article Trends: - Focus on pandemic analysis (Covid-19 monitoring), cardiovascular outcomes in leukemia survivors, and innovative statistical tools for predictive modeling. Grants & Advising: - No explicit grants listed, but extensive collaboration in research projects across disciplines. - Advises on statistical methodologies via software development (R packages).

• Battery Management Systems
• Electrochemical Impedance Spectroscopy
• Machine Learning Applications
• +7 more

Alessio De Angelis is a researcher affiliated with the University of Perugia, focusing on advanced measurement systems, battery technology, and localization techniques. His work spans disciplines such as electrical engineering, machine learning, and IoT applications. University: University of Perugia Email: alessio.deangelis@unipg.it His research interests include: Battery management and state-of-charge estimation Uncertainty quantification in AI-based systems Ultra-wideband (UWB) and magnetic localization Wireless sensor networks for environmental monitoring Signal processing for quantized and constrained data IoT applications in structural and health monitoring Recent publications highlight his expertise in battery diagnostics, localization algorithms, and AI-driven measurement systems. Key trends involve integrating machine learning with electrochemical impedance spectroscopy (EIS) and optimizing low-complexity sensor architectures.

• Complex systems
• Data analysis
• Statistical analysis
• +7 more

Anna Filomena Carbone is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where she directs the NOISELAB research laboratory. Her academic career spans over three decades since earning her PhD in Physics in 1993, with significant contributions to interdisciplinary research at the intersection of physics, data science, and complex systems. Her research interests encompass complex systems, data analysis, statistical analysis, modeling and simulation, and time series analysis, with a particular focus on noise in physical systems. Dr. Carbone has authored over 100 articles in international journals and conference proceedings, with recent publications (2023-2025) demonstrating continued productivity in areas including financial time series analysis, urban scaling laws, quantum information, and brainwave dynamics. Her work shows a clear trend toward increasingly interdisciplinary applications of statistical physics methods to real-world complex systems across multiple domains. Member of the Board of the Nonlinear and Statistical Physics (NSP Board) of the European Physical Society (EPS) since 2009 Member of Scientific Committee - European Physical Society, Belgium (2009-2015) Editorial board member for multiple journals including Physica A, European Physical Journal B, and Chaos, Solitons and Fractals Program chair for multiple International Conferences on Data Science Challenges Dr. Carbone actively supervises PhD students (including Chiara Panico in Computer and Systems Engineering) and serves as Course Instructor for Quantum Information and Quantum Computing courses. Her research projects include TED4LAT (2022-2025), FuturICT 2.0 (2017-2020), and Challenges in Data Science (2016). She maintains active collaborations with institutions including the European Commission and the Department for Digital Transformation of the Presidency of the Council of Ministers.

• Artificial Intelligence
• Computer Vision
• Domain Adaptation
• +6 more

Tatiana Tommasi is an Associate Professor at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin, actively contributing to advanced AI research and education. She serves as Deputy Coordinator of the National Doctoral College in Artificial Intelligence and Scientific Advisor for the European AI, Data, and Robotics Association (ADRA) and ELLIS Network. Her affiliations include the SmartData@PoliTO Laboratory and VANDAL Visual and Multimodal Applied Learning Laboratory. Her research focuses on algorithm fairness, computer vision, domain adaptation, autonomous driving, and robot learning. Recent work includes improving neural network reliability through fault-aware design, 3D semantic novelty detection, and visual relationship reasoning for robotic grasp planning. She has contributed to European research projects like ELSA (Secure and Safe AI) and commercial initiatives in space technologies and autonomous vehicle uncertainty quantification. Selected 2024-2025 publications demonstrate expertise in foundation models, fairness metrics, uncertainty estimation, and geometric deep learning. Her team leads commercial research projects on industrial robotics trajectory generation and satellite data analysis. She teaches core courses in Computer Engineering, Automotive Engineering, and Data Science, mentoring PhD students in Artificial Intelligence and Computer Systems Engineering. Research keywords: Artificial Intelligence, Machine Learning, Computer Vision, Domain Adaptation, Robot Learning, Transfer Learning, Fairness in AI, Uncertainty Quantification, Autonomous Driving, 3D Point Cloud Analysis, Vision Transformers, Industrial Robotics.

• Artificial Intelligence
• Fault Detection and Diagnosis
• Metamodeling
• +8 more

Nicola Pedroni is an Associate Professor at the Department of Energy (DENERG) of Politecnico di Torino, Italy. His research focuses on advanced computational methods for nuclear safety, uncertainty quantification, and artificial intelligence applications in risk assessment. He has held external academic roles at institutions including MIT, École Centrale Paris, and CEA Saclay. Research Interests: Artificial Intelligence, Monte Carlo Methods, Nuclear Safety, Uncertainty Quantification, Probabilistic Risk Assessment, Resilience Analysis His recent work spans inverse uncertainty quantification, fault detection in safety-critical systems, and metamodeling techniques for nuclear reactor analysis. He serves on editorial boards for Frontiers in Nuclear Engineering , Energies , and Proceedings of the Institution of Mechanical Engineers, Part O . Pedroni leads the ARISTOTELES project on AI and stochastic simulation for critical infrastructure resilience and contributes to the EU-funded ENDURANCE project for molten salt reactor safety. He supervises PhD student George Afful and teaches courses in nuclear plant modeling and risk analysis.

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.

Luca CosmoView profile

Associate Professor

• Shape Analysis
• Geometric Deep Learning
• Spectral Geometry
• +3 more

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.

Franco ChiaraluceView profile

Professor

• Telecommunications
• Coding Theory
• Cryptography
• +3 more

Prof. Franco Chiaraluce is a Full Professor in the Department of Information Engineering at Marche Polytechnic University (UNIVPM). His research focuses on telecommunications, coding theory, and information security, with particular emphasis on LDPC codes, post-quantum cryptography, and blockchain-based systems. University: Marche Polytechnic University (UNIVPM) Department: Department of Information Engineering Email: f.chiaraluce@univpm.it His recent work explores machine learning applications in space communications, error correction mechanisms, and secure decentralized authentication protocols. Key subfields include code-based cryptography, space telecommand systems, and blockchain integration for security. Current projects involve optimizing LDPC codes for space transmissions and analyzing cryptographic hardness assumptions related to coding theory.

Anton DignösView profile

Associate Professor

• Temporal Databases
• Time Series Data
• Data Analytics
• +3 more

Anton Dignös is a tenured Associate Professor at the Free University of Bozen-Bolzano, Faculty of Engineering, where he leads the Database Systems Research Group. He holds a PhD from the University of Zurich (2014) and has been a faculty member since 2014. His research focuses on temporal databases, time series data, and data analytics, with contributions to indexing, query optimization, and benchmarking tools like TSM-Bench and SEER. Education : BSc and MSc (Computer Science) - Free University of Bozen-Bolzano (2008, 2010) PhD (Dr. sc. in Computer Science) - University of Zurich (2014) Research Interests : Dignös specializes in temporal database systems, time series management, and efficient query processing. He develops novel indexing techniques (e.g., RD-INDEX) and tools for benchmarking time series systems (SEER, TSM-Bench). His work addresses challenges like anomaly detection, sparse data summarization, and parallel join algorithms. Awards : Best Paper Award at SSDBM 2023 Best Paper Award at ADBIS 2022 ACM Computing Reviews Best of Computing (2017) Teaching & Advising : Teaches Data Management and Applied Informatics courses at both BSc and MSc levels. Advises ~25 students (PhD/MSc/BSc) on topics like temporal databases and machine learning applications. Active in thesis supervision and course development. Labs/Teams : Leads the Database Systems Research Group, collaborating on projects like the Period Index for temporal data and VISOR for data visualization tools.

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

Giancarlo Sangalli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on Scientific Computing, particularly Numerical Methods and Applications, with a strong emphasis on Isogeometric Analysis (IGA) for solving Partial Differential Equations (PDEs). He leads the Scientific Computing group and contributes to interdisciplinary fields such as computational mechanics, biomedical engineering, and environmental modeling. His work integrates advanced numerical techniques, including high-order finite element methods, space-time formulations, and matrix-free solvers, to address challenges in computational efficiency and accuracy. Key areas of application include cardiac electrophysiology, wave propagation, and groundwater flow modeling. Sangalli has pioneered low-rank solvers, Tucker tensor-based methods, and immersed boundary techniques to enhance computational scalability. He actively publishes in top journals and conferences, with a focus on advancing IGA theory and its applications to real-world problems. His research also explores uncertainty quantification, Bayesian calibration, and nonlinear dynamics. Despite no explicitly listed awards, his extensive publication record reflects recognition in computational mathematics and engineering. Sangalli collaborates internationally and maintains a research website at https://mate.unipv.it/sangalli . His group's work is supported by projects in computational electromagnetics, structural mechanics, and fluid-structure interaction, demonstrating a commitment to bridging theoretical advancements with practical engineering solutions.

Marco CorneliView profile

Professor

• Static and dynamic network analysis
• Spectral clustering
• Stochastic block models
• +16 more

Marco Corneli is a Professor at the University of Côte d'Azur , where he focuses on developing AI models for archaeology and history. He collaborates with the CEPAM laboratory , is a member of the INRIA research team MAASAI , and part of the LJAD laboratory. His research spans network analysis, optimal transport, and 3D shape analysis. Research Interests : Static/Dynamic Network Analysis (Spectral Clustering, SBMs, HRGs) Optimal Transport for Machine Learning Image and 3D Shape Analysis Probabilistic and Deep Learning Methods Clustering in High-dimensional and Sparse Data Advising : Davide Adamo (2023 - ongoing, co-advised with E. Vila and M. Vuillien) Seydina Ousmane Niang (2023 - ongoing, co-advised with C. Bouveyron and P. Latouche) Giulia Marchello (2020-2023, co-advised with C. Bouveyron) Cédric Vincent-Cuaz (2020-2023, co-advised with R. Flamary) Dingge Liang (2019-2022, co-advised with C. Bouveyron and P. Latouche) Software Development : Co-developed CpDyna (R/C++ package for change point detection in dynamic graphs) Contributed to ordinalLBM (R package for co-clustering ordinal data) Teaching : Delivered courses in statistical learning, graph theory, and data science at University of Côte d'Azur (M2 MA IM), Université Paris 1 (Licence MIASHS), and University of Côte d'Azur (MSc Data Science).

• Evolutionary Algorithms
• Machine Learning
• Optimization
• +7 more

Giovanni Iacca is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science (DISI), where he serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. He leads the Distributed Intelligence and Optimization Lab (DIOL) and teaches courses including Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, and Optimization Techniques across multiple academic programs. PhD in Computer Science, University of Jyväskylä, Finland (2011) MSc in Computer Engineering, Technical University of Bari, Italy (2006) Professor Iacca's research focuses on the intersection of evolutionary computation, machine learning, and optimization with applications in distributed systems and robotics. His work spans from theoretical foundations of memetic computing and multi-objective optimization to practical implementations in soft robotics, embedded systems, and healthcare applications. Recent efforts emphasize interpretable AI, particularly in reinforcement learning contexts, where his team develops methods to make decision processes transparent while maintaining performance. His research bridges the gap between fundamental algorithmic development and real-world engineering challenges, with over 15 years of industrial experience in optimization applied to engineering, logistics, and scheduling. Analysis of his recent publications reveals a strong trend toward interpretable AI systems, particularly in reinforcement learning contexts, with significant contributions to federated learning optimization, evolutionary neural architecture search, and applications in healthcare scheduling. His work consistently combines evolutionary algorithms with modern machine learning techniques to solve complex optimization problems across diverse domains including soft robotics, batteryless edge computing, and supply chain management. Scientific Awards: EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) Professor Iacca actively supervises a large research group with numerous PhD students across multiple doctoral programs, including Information Engineering and Computer Science, Industrial Innovation, and the National PhD in Artificial Intelligence for Society. His lab has secured significant research funding through collaborations with industry partners and international research consortia. Recent grants support work on interpretable reinforcement learning, federated optimization, and applications of evolutionary computation in healthcare and robotics. He has also been appointed to editorial roles for prestigious journals including IEEE Transactions on Evolutionary Computation and Evolutionary Intelligence. The Distributed Intelligence and Optimization Lab (DIOL) under Professor Iacca's leadership comprises over 30 researchers including postdocs, PhD students, and master's students. The lab maintains strong international collaborations and has developed specialized expertise in evolutionary computation, interpretable AI, and optimization for embedded systems. Current projects include work on the EIC Pathfinder Challenge "Awareness Inside," development of methods for batteryless edge intelligence, and applications of evolutionary algorithms to healthcare scheduling problems.

Alessandro FelisiView profile

Research Fellow

• Mathematical Analysis
• Inverse Problems
• Compressed Sensing
• +3 more

Alessandro Felisi serves as a Research Fellow in the Department of Mathematics (DIMA) at the University of Genoa, Italy, specializing in Mathematical Analysis within the SSD code MATH-03/A framework. His institutional role focuses on advanced mathematical research with practical applications. His research program centers on Mathematical Analysis, particularly inverse problems and compressed sensing methodologies. He develops theoretical frameworks for reconstructing sparse signals from limited measurements, with direct applications in medical imaging, seismic data processing, and wireless communications. His work bridges abstract mathematical theory with computational efficiency in high-dimensional data analysis. Current publication trends reveal a concentrated focus on compressed sensing applications for inverse problems, demonstrating expertise in numerical optimization techniques and sparse representation theory. His research consistently intersects applied mathematics, computational physics, and engineering disciplines, emphasizing algorithmic solutions for ill-posed problems.

• Algorithms and Data Structures
• Big Data
• High-Performance Computing
• +12 more

Francesco Silvestri is an Associate Professor in Computer Engineering at the Department of Information Engineering , University of Padova . He has held previous roles as Assistant Professor (2016-2019), Post-Doc (2015-2016 at IT University of Copenhagen; 2009-2014 at University of Padova), and Part-Time Lecturer (2013-2014 at IT University of Copenhagen). He earned a Ph.D. in Computer Engineering from the University of Padova (2009). Research Focus: Algorithms and data structures for big data processing, high-performance computing, similarity search, graph mining, I/O-efficient algorithms, mobility algorithms, and differential privacy applications. Teaching: Courses include Big Data Computing and Algorithm Design (University of Padova), Advanced Algorithms Seminar (IT University of Copenhagen). Scientific Contributions: ICDT Best Paper Award (2023) for subgraph enumeration ACM SIGMOD Research Highlights (2020) for fair similarity search Developed patented MapReduce methods for triangle enumeration Collaborations and Supervision: Current PhD students: Mariafiore Tognon (2024), Linghan Zeng (2023), Fabrizio Boninsegna (2022). Collaborators include Ninh Pham (University of Auckland), Gianfranco Bilardi, and Rasmus Pagh. Personal Interests: Active in Dottor Clown Padova for hospital clowning, family life with wife Elisa Salvagnin and three children, and cycling advocacy in Padova.

• Control Theory
• System Identification
• Machine Learning
• +5 more

Vito Cerone is a Full Professor at the Department of Control and Computer Science (DAUIN) of the Polytechnic University of Turin, Italy. His research focuses on control theory, system identification, machine learning, and optimization, with applications in vehicle dynamics, energy systems, and robust control design. Research Interests: Control Theory, System Identification, Machine Learning, Optimization, Cyber-Physical Systems. Scientific Contributions: Recent publications address convex optimization via feedback control, errors-in-variables system identification, and state estimation under sensor attacks. He has led numerous industry-funded projects on energy management in hybrid vehicles, transmission control strategies, and sea wave energy conversion. Professional Roles: He has served as Associate Editor for IEEE Transactions on Vehicular Technology (2019-2022) and is a member of IEEE and its Control Systems Society since 2006. He has taught graduate-level courses in Automatic Control and Modern Design of Control Systems across academic years 2019-2025.