Find Academic Supervisors

• Empirical Asset Pricing
• Financial Derivatives
• Risk Aversion Estimation
• +3 more

Maria Kosolapova is a Researcher at the Faculty of Economics and Business Administration, Free University of Bozen-Bolzano. Her research focuses on Empirical Asset Pricing and Financial Derivatives, with notable contributions in estimating risk aversion using option prices and analyzing market expectations during the early phase of the COVID-19 pandemic. She has co-authored influential studies in journals like Quantitative Finance and Economics Letters . Her academic activities include teaching courses such as Applied Statistics for Accounting and Finance (Master’s level) and Economic Econometrics (Doctoral program). Her work bridges theoretical econometric methods with practical applications in financial markets, particularly in understanding how crises impact risk perceptions and market behaviors.

Gabriele SANTINView profile

Researcher

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +5 more

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

• Artificial Intelligence
• Bioinformatics
• Machine Learning
• +7 more

Enrico Blanzieri is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science. His research focuses on artificial intelligence, bioinformatics, quantum computing, machine learning, and computational biology. He actively contributes to interdisciplinary projects such as Vitis OneGenE for gene network analysis and quantum machine learning pipelines. His teaching includes courses like Algorithms for Bioinformatics, Quantum Machine Learning, and Data Mining. Blanzieri's work bridges theoretical foundations and practical applications, with notable contributions in quantum annealing-based algorithms, gene network expansion using distributed computing, and socially-competent artificial agents. His research integrates computational methods with biological systems, addressing challenges in precision agriculture and systems biology. His recent publications emphasize quantum machine learning advancements, causal role attribution in gene networks, and ethical AI frameworks for language models. Collaborative projects include the GENE@HOME volunteer computing initiative for bioinformatics tasks. Blanzieri also engages in educational activities, supervising courses that blend theoretical computer science with real-world applications.

• Algebraic Number Theory
• Analytic Number Theory
• Cryptanalysis
• +8 more

Carlo Sanna is an Associate Professor at the Department of Mathematical Sciences (DISMA) of Politecnico di Torino, Italy. His research focuses on algebraic and analytic number theory, cryptography, and their intersections, particularly in post-quantum and public-key cryptographic systems. Research Interests : Algebraic Number Theory, Analytic Number Theory, Cryptanalysis, Post-Quantum Cryptography, Public-Key Cryptography, Discrete Mathematics. Teaching : He teaches Linear Recurrences (PhD level) and Advanced Cryptography (Master's level) at Politecnico di Torino, while also lecturing Linear Algebra and Geometry for Aerospace and Management Engineering. Publications : His recent work spans Fibonacci partitions, Lucas sequences, post-quantum cryptographic algorithms, and properties of linear recurrences. Supervised PhD Students : Leonardo Errati (Post-Quantum Cryptography, 2025) Giulia Salvatori (2024) Federico Accossato and Enrico Guglielmino (2022) Giuliano Romeo (2021-2025, p-adic continued fractions) Research Projects : Algebraic Methods in Cryptanalysis (2025-2027, Scientific Director) QUBIP (Quantum-oriented browser updates, 2023-2026, Scientific Head) SERICS Spoke 5 (Cybersecurity, 2023-2025, Scientific Manager) Email : carlo.sanna@polito.it

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +4 more

Sonia Mazzucchi is a Full Professor in the Department of Mathematics at the University of Trento, specializing in probability theory, stochastic processes, and mathematical physics. Her research focuses on Feynman path integrals, quantum mechanics, and operator theory, with recent extensions into quantum information and photonics applications. She teaches core courses including Probability Calculus II, Mathematics and Statistics II, Quantum Information, and Stochastic Processes. Her research interests bridge abstract mathematical theory with practical quantum technologies. She investigates stochastic processes for modeling quantum systems, develops rigorous mathematical frameworks for path integrals on Lie groups, and applies probability theory to quantum random number generation and LiDAR systems. Her work combines functional analysis, measure theory, and differential equations to solve problems in quantum mechanics and information science. Recent publications (2022-2025) reveal a strong interdisciplinary trajectory merging mathematical physics with quantum engineering. Key trends include quantum random number generators using single-photon entanglement, SPAD-based LiDAR innovations for photon flux measurement, and advanced treatments of path integrals on Riemannian manifolds. Her work demonstrates consistent progression from foundational mathematical theory to quantum technology applications. No scientific awards are documented in the available information. Details regarding graduate student supervision and research grants are not specified in the source materials. Her academic activities center on teaching core mathematics courses and publishing in high-impact journals spanning mathematical physics and quantum information science.

• Chemometrics
• Analytical Chemistry
• Spectroscopy
• +4 more

Nicola Cavallini is a Fixed-term Assistant Professor at the Department of Applied Science and Technology (DISAT) , Politecnico di Torino , and a member of the College of Chemical and Materials Engineering . He holds a Scientific Branch CHEM-06/A - Chemical Foundations of Technologies within Area 0003 - Chemical Sciences . Since 2020, he has been teaching Principles of Chemometrics and Practical Design of Experiments to PhD and undergraduate students in Chemical Engineering. 2025/26 : Course Lecturer for Principles of Chemometrics 2024/25 : Course Lecturer for Principles of Chemometrics 2023/24 : Course Lecturer for Principles of Chemometrics 2022/23 : Course Collaborator for Principles of Chemometrics 2021/22 : Course Collaborator for Principles of Chemometrics 2020/21 : Course Collaborator for Principles of Chemometrics His research focuses on Chemometrics , Analytical Chemistry , and Applied Spectroscopy , particularly in food authentication and materials characterization. He has developed methods for ethanol quantification in wine using NMR, rice variety classification via hyperspectral imaging, and fermentation monitoring with portable NIR sensors. Collaborative projects include polymer electrolyte optimization and textile waste valorization in biotechnological processes. Recent publications highlight his work on: Food Fraud Detection (Mechanically Separated Meat in processed products) Advanced Spectroscopic Techniques (NMR and NIR applications) Algorithm Development for FESEM image analysis Sustainable Material Processing (Polymer blends, textile waste) Plant-Microbe Interactions (β-ionone's role in mycorrhization)

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.

David TewodroseView profile

Professor

• Geometric Analysis
• Metric Measure Spaces
• Ricci Curvature
• +4 more

David Tewodrose is a Professor in the Department of Mathematics and Data Science at the Vrije Universiteit Brussel (Belgium). His research focuses on geometric analysis, particularly studying metric measure spaces obtained as Gromov-Hausdorff limits of Riemannian manifolds under curvature constraints. He leads the Odysseus Programme titled Geometric and Analytic Properties of Metric Measure Spaces with Spectral Curvature Constraints, with Applications to Manifold Learning , funded by FWO (2023–2028). This program explores geometric evolution problems, spectral optimization, and manifold learning algorithms. He teaches differential geometry and measure theory in the VUB Bachelor program, and Riemannian geometry in the VUB-UAntwerpen Master program. Previously, he instructed analysis courses in the International Bachelor Ygrec program and supervised student blog projects on societal data science issues. His research team includes PhD candidates Nicolo' Cont and Manuel Dias , along with postdoctoral researcher Susovan Pal . His work bridges geometric analysis with applications in data science, emphasizing curvature constraints and spectral properties.

• Partial Differential Equations
• Fluid Dynamics
• Calculus of Variations
• +3 more

Maria Colombo is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL). Her research focuses on nonlinear partial differential equations (PDEs), particularly in fluid dynamics, calculus of variations, and optimal transport. She investigates fundamental questions related to existence, uniqueness, and regularity of solutions in contexts such as Euler and Navier-Stokes equations, nonlocal conservation laws, and branched transportation networks. Her work spans theoretical analysis of PDEs, including studies on non-uniqueness phenomena in transport equations, stability of optimal traffic plans, and singular limits of nonlocal models. She also explores geometric aspects of variational problems, such as free boundary regularity and epiperimetric inequalities. Colombo's contributions bridge pure mathematics and applied analysis, addressing challenges in both classical and modern settings like semiclassical limits in quantum mechanics. Her research often involves collaboration with leading mathematicians in PDEs and analysis, resulting in over 50 publications in top-tier journals. While no specific awards are listed, her prolific output reflects recognition in her field. She maintains an active presence in academic communities through conference organization and speaking engagements.

Andrea TorselloView profile

Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Andrea Torsello is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on Computer Vision, Machine Learning, and Artificial Intelligence with applications in quantum computation, graph theory, and 3D modeling. He is actively involved in teaching courses on quantum computation, artificial intelligence foundations, and computer science fundamentals at both undergraduate and graduate levels. Prof. Torsello's research interests include graph neural networks, quantum machine learning, and structural pattern recognition. His work often bridges theoretical computer science with practical applications in cultural heritage digitization and environmental monitoring. His recent articles highlight advancements in graph generation via spectral diffusion, quantum lens-based 3D shape analysis, and interpretable graph neural networks. He serves on the editorial boards of journals like Pattern Recognition and has contributed to projects funded by the EU’s H2020 program, including the VEiL initiative for visualizing engineered landscapes. His teaching spans topics such as quantum computation, advanced algorithms, and machine learning across multiple engineering programs.

Domenico TortorellaView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Reservoir Computing
• +6 more

Domenico Tortorella is an Assistant Professor (RTD-A) in the Department of Computer Science at the University of Pisa, Italy. He earned a PhD in Computer Science (cum laude) in 2024, an MSc in Computer Science (cum laude) in 2020, and a BSc in Computer Engineering (cum laude) in 2017. His research focuses on graph neural networks , reservoir computing , and deep learning on graphs , particularly addressing challenges like heterophily, over-squashing, and structural encoding. He contributes to conferences such as ICANN, ESANN, and NeurIPS, and is part of the Computational Intelligence and Machine Learning (CIML) research group. Research Trends: His recent publications emphasize Graph topology encoding (e.g., Randomized Ising Models) Efficiency in graph kernels and neural networks Explainable AI for deep graph models Stability analysis in echo state networks Handling heterophilic graphs via reservoir computing Temporal resolution in recurrent architectures Grants & Service: He received funding from the Future AI Research (FAIR) project (2024–2025) and served on program committees for ICANN, NeurIPS workshops, and GbRPR. He was Vice-Chair of the IEEE Student Branch of Pisa (2023–2024) and holds memberships in IEEE, ACM, ENNS, and IAPR's CVPL.

Nicoletta NocetiView profile

Associate Professor

• Computer Science
• Robotics
• Machine Learning
• +7 more

Nicoletta Noceti is an Associate Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) of the University of Genoa. Her academic roles include teaching courses such as Computer Vision, Deep Learning, and Machine Learning across undergraduate and master's programs in Robotics Engineering and Computer Science. She also contributes to innovative education initiatives like the 'Smart rogaining' model for computer science orientation. Her research focuses on interdisciplinary areas spanning computer vision, human-centered artificial intelligence, and robotics. Key themes include human motion analysis, causal discovery, and the application of AI in healthcare and social interaction. Recent work emphasizes disentangled representations for microscopy images, scene-unbiased action recognition, and understanding engagement dynamics in virtual teams of older adults. She also explores uncertainty-aware systems for head pose estimation and kinematic primitives in action similarity judgments. Her research frequently intersects with robotics, cognitive science, and biomedical engineering, addressing challenges such as causal inference in nonlinear models and embodied interaction design for robots. These efforts aim to bridge computational methods with human perceptual and motor systems, with applications in assistive technologies and social robotics.

Fabio PrevidiView profile

Professor

• Control Systems
• Mechatronics
• Aerospace Engineering
• +5 more

Fabio Previdi is a Full Professor in Control Systems at the Department of Management, Computer and Industrial Engineering, University of Bergamo. His career spans roles from Research Assistant at the University of Glasgow (1999-2000) to Associate Professor (2006-2017) before assuming his current position. He holds a PhD in Control Systems from Politecnico di Milano (1999), MSc in Electronic Engineering (1993) and Physics (2008) from Politecnico di Milano and University of Milan. Research focuses on control systems, mechatronics, and aerospace applications. Key areas include electromechanical actuators, fault detection, system identification, and biomedical engineering. Over 150 peer-reviewed papers highlight contributions to topics like robust control, machine learning for industrial systems, and pandemic modeling. Publications (2023-2020) address cutting-edge topics such as jamming detection filters for aircraft actuators, agent-based pandemic simulations, and kernel-based system identification. Awards: Not explicitly mentioned but notable contributions recognized through extensive publication record.

• Machine learning
• Numerical analysis
• Numerical methods
• +10 more

Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.