جستجوی استاد راهنمای دانشگاهی

Salvatore Ivan Trapasso is a Fixed-term Assistant Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA) , Polytechnic University of Turin , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . His research focuses on Applied Harmonic Analysis , Fourier Analysis , Machine Learning , and Quantum Theory , with expertise in Mathematical Analysis (MATH-03/A) and Theoretical PDEs (PE1_11). Education : Implied PhD in Mathematics. Research Areas : Phase Space Analysis, Time-Frequency Methods, and Applications to Quantum Mechanics. His recent publications investigate phase space techniques for Feynman Path Integrals , Twisted Laplacian , Compressed Sensing , and Stability of Scattering Transforms . His work bridges Harmonic Analysis with Machine Learning and Quantum Dynamics . Notable scientific awards include the Axioms Young Investigator Award (2022) , Best Paper Award (ICGF 2020) , and the Quality Award 2019 from Polytechnic University of Turin. He serves on the Editorial Board of Advances in Operator Theory and as Associate Editor for University Texts in the Mathematical Sciences . Teaching roles include Lecturer for Mathematical Principles in the College of Architecture and Design , Collaborator for Mathematical Analysis I/II in Biomedical and Aerospace Engineering, and Contributor to advanced mathematical methods in Computer Science Engineering.

• Applied harmonic analysis
• Fourier analysis
• Machine learning
• +۵ مورد دیگر

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.

• Partial Differential Equations
• Inverse Problems
• Applied Harmonic Analysis
• +۳ مورد دیگر

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.

• Digital Signal Processing
• Image Processing
• Biometrics
• +۱۲ مورد دیگر

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.

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +۳ مورد دیگر

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.

• Inverse Problems
• Mathematical Analysis
• Medical Imaging
• +۳ مورد دیگر

Anna Volpara is a Research Fellow at the Department of Mathematics (DIMA) of the University of Genova. Her work focuses on numerical analysis and solar imaging techniques, particularly inverse problems in high-energy solar observations. Primary affiliation: Department of Mathematics - DIMA, University of Genova Academic rank: Research Fellow Her research interests include: Inverse problems in astrophysics Hard X-ray observation techniques Fourier-based image reconstruction Kernel methods for solar imaging Recent publications (2023-2025) demonstrate expertise in computational methods for solar flare analysis, STIX imaging, and multi-scale data processing. No scientific awards were mentioned in the available information.

• Inverse Problems
• Numerical Analysis
• Solar Imaging
• +۳ مورد دیگر

Federica Porta is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her academic career focuses on numerical analysis, optimization, and stochastic gradient methods, particularly in machine learning and image restoration. Her research interests include: Numerical Analysis and Statistics for Computer Engineering Numerical Optimization for Artificial Intelligence Stochastic Gradient Descent with Variance Control Deep Image Prior Frameworks Regularization Techniques for Biomedical Imaging Federica's recent publications (2021–2025) demonstrate expertise in hybrid gradient projection methods, adaptive learning rate selection, and deep learning applications for image segmentation and classification. She collaborates extensively with researchers like Giorgia Franchini, Valeria Ruggiero, and Luca Zanni, applying these methods to both convex and non-convex optimization problems. She teaches courses such as Numerical Analysis and Statistics for Computer Engineering, Numerical Analysis for Mathematics, and Numerical Optimization for Artificial Intelligence. Her teaching emphasizes MATLAB/Python implementation of numerical methods, convergence properties, and computational complexity analysis. Contact: federica.porta@unimore.it | Office: Mathematics Building, Via Campi 213/b

• Numerical Analysis
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Simone Fiori is an Associate Professor at the University of Ancona and Marche Polytechnic (UNIVPM), affiliated with the Department of Information Engineering under the Faculty of Engineering. His research focuses on control systems, dynamical systems on manifolds, robotics, and applications of differential geometry to engineering problems. His work emphasizes the use of Lie groups, Riemannian manifolds, and geometric control theory to model and control complex systems such as spacecraft, submersibles, and drones. Recent studies include automated rendezvous for spacecraft, neuroevolutionary controllers for quadcopters, and synchronization of dynamical systems on manifolds. Publications highlight advancements in mathematical modeling, optimization techniques under constraints, and statistical methods for circuit identification. His research bridges theoretical foundations (e.g., Euler-Poincaré reduction) with practical applications like brain-computer interfaces and blind deconvolution algorithms. Prof. Fiori’s contact information includes his email s.fiori@univpm.it and office hours on Thursdays 15:00-18:00 at the Department of Information Engineering (Q165).

• Control Systems
• Dynamical Systems
• Applied Mathematics
• +۴ مورد دیگر

Alexander Fuller Viguerie is a Researcher (Ricercatore Legge 240/10 a tempo determinato) at the Department of Pure and Applied Sciences (DiSPeA) at the University of Urbino Carlo Bo. His position involves both teaching responsibilities in mathematical courses and conducting advanced research primarily focused on numerical methods and mathematical modeling. He teaches courses including Logic, Algebra and Geometry; Numerical Methods for Linear Algebra and Functional Analysis; and Statistical Processing of Experimental Data across various programs including Informatics, Digital Innovation, and Biotechnology. Viguerie's research spans multiple interdisciplinary domains with an emphasis on numerical analysis and computational methods. His primary research interests include mathematical epidemiology (particularly modeling of infectious diseases like COVID-19), computational fluid dynamics, partial differential equations, and dynamic mode decomposition techniques. His work demonstrates a strong focus on developing and applying advanced numerical methods to solve complex problems in epidemiology, fluid mechanics, and biomedical applications. He has developed compartmental models with diffusion components for spatially resolved epidemic simulations, created novel approaches using delay differential equations for disease modeling, and applied dynamic mode decomposition to accelerate simulations of cancer growth models. His publication record shows a significant shift toward mathematical epidemiology during the pandemic years, with numerous high-impact papers analyzing the spatiotemporal spread of COVID-19 across different countries. Viguerie has developed modified SEIRD models with dynamic parameterization capabilities, enabling more accurate forecasting of pandemic trajectories. His work extends beyond epidemiology to include applications in computational fluid dynamics, additive manufacturing, and biomedical modeling, demonstrating versatility across multiple application domains while maintaining a strong methodological foundation in numerical analysis. Viguerie maintains an extensive international research network, with collaborations spanning Europe, Asia, North America, and South America. His research shows particularly strong connections with institutions in Italy, the United States, Brazil, Germany, and Singapore. This global collaboration network enables him to conduct cross-country comparative studies, as evidenced by his work analyzing the spread of COVID-19 in Italy, the USA, and Brazil. His research methodology frequently combines theoretical mathematical analysis with practical numerical implementation, resulting in tools that have potential applications in public health decision-making and engineering design processes.

• Numerical Analysis
• Computational Fluid Dynamics
• Mathematical Epidemiology
• +۴ مورد دیگر