Find Academic Supervisors

• Quantitative Finance
• Risk Management
• Operational Research
• +6 more

Sandra Paterlini is a Full Professor in the Department of Economics and Management at the University of Trento, Italy. She holds academic roles including Co-Chair of the ERCIM Working Group on Optimization Heuristics and Vice-Chair of the IEEE Task Force on Portfolio Optimization. Her career includes visiting positions at institutions such as the University of Minnesota and Ludwig-Maximilians-Universität München. She earned a PhD in Computational Methods for Financial and Economic Decisions from the University of Bergamo, an MSc in Financial Mathematics from the University of Warwick, and a Laurea in Economics from the University of Modena and Reggio E. Her research focuses on quantitative finance, risk management, portfolio optimization, and network analysis, with applications to ESG, systemic risk, and financial stability. Key research contributions include methodologies for sparse graphical modeling, systemic risk analysis, and ESG scoring frameworks. She has received multiple awards for research excellence and serves on editorial boards of journals like Computational Statistics & Data Analysis and Frontiers in Applied Mathematics and Statistics . Her work bridges academia and policy, with contributions to the European Central Bank’s Financial Stability Directorate and involvement in global conferences on computational finance and econometrics.

• Structural Health Monitoring
• Seismic Analysis
• Bayesian Calibration
• +14 more

Gaetano Miraglia is a Fixed-term Assistant Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, where he conducts research in structural health monitoring, seismic analysis, and computational modeling. He is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Centre, contributing to interdisciplinary efforts in risk mitigation and infrastructure resilience. His work spans both theoretical and applied domains, with strong emphasis on heritage preservation and sustainable urban development. His research interests include Bayesian calibration of nonlinear models, hybrid simulation, peridynamics, masonry structures, and the integration of satellite interferometric (InSAR) data with in-situ measurements for structural monitoring. He applies advanced computational and machine learning techniques to improve the accuracy and reliability of structural assessments, particularly in historical and monumental buildings. His work supports UN Sustainable Development Goals 9, 11, and 13. His recent publications demonstrate a consistent focus on data fusion, digital twinning, domain adaptation, and real-time damage detection. He frequently collaborates with researchers such as Rosario Ceravolo and Erica Lenticchia, publishing in high-impact journals like Computer-Aided Civil and Infrastructure Engineering , Structures , and Scientific Reports , as well as at major conferences including EWSHM, SAHC, and EVACES. His research is applied in projects such as the monitoring of the Vicoforte Sanctuary and the development of the CAMELOT and HY-LEARN toolboxes. Research Projects: MONITORAGGIO VICOFORTE (2024–2026) – Member of Research Group CAMELOT – PoC Transition (2023–2024) – Member of Research Group HY-LEARN – Model Calibration via Hybrid Simulation and ML (2022–2024) – Scientific Manager (PNRR Mission 4) He teaches in various programs, including as a course collaborator in PhD, Master’s, and Bachelor’s level courses such as Earthquake Engineering , Structural Consolidation , and Seismic Risk of Cultural Heritage . He is also an inventor on national and international patents and software related to the CAMELOT toolbox, highlighting the translational impact of his research. He has no listed scientific awards or formal advisees in the provided text.

• Antennas
• Applied Electromagnetics
• Computational Electromagnetics
• +2 more

Giuseppe Vecchi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , Italy. He leads the Applied Electromagnetics research group and contributes to projects in computational electromagnetics, metamaterials, and biomedical applications of electromagnetic fields. He has been a IEEE Fellow since 2010 and serves on PhD college committees for Electrical, Electronic, and Communications Engineering. Research Interests : Antennas, Applied and Computational Electromagnetics, Metamaterials, Microwave Imaging for medical applications, Nuclear Fusion Reactor Physics. Scientific Leadership : Principal Investigator for projects like METEOR, MTSA, and RESOLVED-K, focusing on terahertz generation, metasurface antennas, and real-time temperature mapping in hyperthermia. Awards : IEEE Fellow (2010), recognizing his contributions to electromagnetic simulations and antenna design. Students : Supervises PhD candidates in advanced antenna engineering, computational electromagnetics, and biomedical applications, including Owais Khan, Francesco Lattanzio, and Sara Paknezhad Panahi. Patents : Holds multiple patents in antenna diagnostics, encrypted metasurface antennas, and microwave soil disinfection systems.

• Numerical Linear Algebra
• Numerical Nonlinear Algebra
• Parallel Computing
• +7 more

Prof. Zhong-Zhi Bai is a Professor of Computational Mathematics at the Institute of Computational Mathematics and Scientific/Engineering Computing, part of the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing. He is based at the State Key Laboratory of Scientific/Engineering Computing, a leading institution in applied mathematics and high-performance computing in China. His research spans a broad range of topics in computational mathematics, with a strong emphasis on numerical linear and nonlinear algebra , parallel computations , numerical optimization , and sparse matrix techniques . He has made significant contributions to the development and analysis of preconditioners and numerical methods for differential-algebraic equations and partial differential equations . His work is foundational in advancing iterative solvers and large-scale scientific simulations. The available publications, including the notable book Milestones in Matrix Computation (2007), reflect a long-standing engagement with matrix computation and numerical algorithms. The research trends indicate a deep focus on theoretical and practical aspects of numerical methods, particularly in the context of high-performance and scientific computing environments. Prof. Bai serves on the editorial boards of several leading journals in the field, including: Applied Numerical Mathematics Communications on Applied Mathematics and Computation Computational and Applied Mathematics East Asian Journal on Applied Mathematics Electronic Transactions on Numerical Analysis Journal of Computational and Applied Mathematics Journal of Mathematical Study Journal of Scientific Computing Numerical Algorithms Numerical Linear Algebra with Applications He has advised students and mentored researchers, though specific names are not listed. His work is supported by institutional affiliations and likely national research grants, given his position in a State Key Laboratory. He has developed software tools and delivered lecture series, contributing to both research and education in computational mathematics. Prof. Bai is actively involved in the numerical linear algebra community and maintains a comprehensive academic presence, including publications, editorial service, and software contributions. He leads or participates in research teams focused on advancing computational methods within the State Key Laboratory framework.

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +7 more

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

• Numerical Analysis
• Partial Differential Equations
• Integral Equations
• +3 more

Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.

• Machine Learning
• Signal Processing
• Telecommunications
• +3 more

Andrea Migliorati is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He specializes in telecommunications and industrial/information engineering (IINF-03/A - Area 0009), with a focus on machine learning applications in signal processing and neural network optimization. Academic Role: Assistant Professor Department: Electronics and Telecommunications Research Branch: Telecommunications (IINF-03/A) University: Politecnico di Torino Migliorati's research explores neural network sparsification, quantization techniques, and robust training methods for secure systems. His work bridges telecommunications engineering with modern machine learning, targeting applications in wearable biometrics and edge computing. Key trends include: Advancing sparse trainable neural networks through concave regularization Developing ternary/binary quantization approaches for model compression Enhancing gait identification systems for wearable devices Improving adversarial robustness via Gaussian class-conditional training

Alex WeissensteinerView profile

Professor

• Quantitative Finance
• Financial Engineering
• Asset Allocation
• +4 more

Alex Weissensteiner is a Full Professor of Quantitative Finance and Rector at the Free University of Bozen-Bolzano (unibz). He previously held academic positions at Leopold Franzens University in Innsbruck, the University of Liechtenstein, and served as Professor of Financial Engineering at the Technical University of Denmark from 2013–2015. At unibz, he held leadership roles including Director of the Bachelor's Degree in Economics and Management (2015–2020) and Pro-Rector for Studies (2020–2024) before becoming Rector in 2024. His research focuses on Life-cycle asset allocation Parameter uncertainty in financial models Scenario generation for investment decisions Asset-liability management Market microstructure dynamics Information economics in financial markets Recent publications emphasize portfolio optimization under uncertainty, option-implied risk analysis, and agricultural risk management. His work combines theoretical finance with empirical validation, often applying quantitative methods to banking, insurance, and pension systems. Scientific recognition includes EU grants for "Understanding Pensions in Europe" (2016) and "Understanding Saving in Europe" (2019) Regular contributions to leading journals like Journal of Banking & Finance and Quantitative Finance Invited presentations at major finance conferences (Jackson Hole, AFA, DGF) As Rector, he maintains active research collaborations with scholars including Mogens Steffensen (University of Copenhagen), N. Branger, T. Dangl, and L. Garlappi. He serves on the editorial board of Risks journal and has consulted for provincial education policy bodies.

Diego Regruto TomalinoView profile

Associate Professor

• System Identification
• Robust Control
• Convex Relaxation Techniques
• +5 more

Diego Regruto Tomalino is an active Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin, Italy, with contact details +39 0110907077 and diego.regruto@polito.it. He holds leadership roles in the IEEE Control Systems Society as Scientific Committee member of the Technical Committee on System Identification and Adaptive Control (2008-present) and served as its President (2013-2015). His work spans academic research, teaching, and international collaboration within control engineering. Education : Laurea in Electronic Engineering, Politecnico di Torino (2000) Ph.D. in Systems Engineering, Politecnico di Torino (2004) Research Interests : System Identification : Specializing in set-membership, sparse, and LPV techniques for nonlinear systems with applications in automotive and biomedical domains. Optimization & Control : Developing convex relaxation methods for model predictive control, robust control algorithms, and electrified vehicle propulsion systems. Control Education : Innovating through remote laboratories and industrial platforms to enhance engineering pedagogy globally. Publication Trends : Recent work (2021-2024) focuses on sparse optimization for cyber-physical security and electrified vehicles, extending set-membership identification to continuous-time systems, and educational frameworks. Dominant fields include control theory, optimization, and automotive applications with increasing emphasis on data-driven methods and sparse computing. Scientific Awards : No specific prizes, medals, or fellowships were documented in the provided texts. Advising and Grants : PhD Supervision : Alice Re (2024-present, sparse optimization for identification), Francesco Ripa (2023-present, electric vehicle propulsion management), Simone Pirrera (2021-2025, dynamical systems learning). Research Projects : Principal investigator for OpThermEV (2021-2022) on thermal management of electric vehicles, plus corporate-funded automotive research initiatives. Labs and Teams : Core member of the SIC (System Identification & Control) research group and LAB 11 at DAUIN. Leads international control education collaborations, notably a remote laboratory partnership between Asian and European institutions.

• Nonlinear Dynamics
• Structural Dynamics
• System Identification
• +3 more

Dario Anastasio is a Fixed-term Assistant Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He is affiliated with the College of Mechanical, Aerospace, and Automotive Engineering and actively contributes to both teaching and research activities in mechanical engineering. Dr. Anastasio's research focuses on several key areas within mechanical engineering and dynamics. His primary interests include: Nonlinear dynamics and structural dynamics System identification and modal analysis Energy harvesting, particularly vibration energy harvesting Dynamics of mechanical systems with nonlinear characteristics Pantograph-catenary dynamic interaction in railway systems His work spans both theoretical modeling and experimental validation, with particular emphasis on negative stiffness oscillators, railway contact line dynamics, and nonlinear system identification techniques. Dr. Anastasio applies advanced methodologies including subspace identification, Bayesian model selection, and signal processing to solve complex mechanical engineering problems related to vibration analysis and structural dynamics. Analysis of Dr. Anastasio's publications reveals a strong focus on nonlinear dynamics, particularly in mechanical systems with complex behaviors. His research consistently bridges theoretical modeling with experimental validation across multiple domains including railway systems, energy harvesting devices, and nonlinear oscillators. The publications demonstrate progression from fundamental nonlinear dynamics research toward practical applications in railway engineering and vibration-based energy harvesting systems. Dr. Anastasio has received notable recognition for his work: Quality Award 2019 conferred by Politecnico di Torino, Italy (2020) Dr. Anastasio actively contributes to the academic community through extensive teaching activities across multiple programs. He serves as a Teaching Assistant for "Dynamics and Identification of Nonlinear Systems" and as a Course Collaborator for "Dynamics of Mechanical Systems" and "Vibration Mechanics" at both Master's and Bachelor's levels. His teaching spans from 2019/20 through the upcoming 2025/26 academic year, demonstrating his ongoing commitment to mechanical engineering education. Additionally, he contributes to PhD-level instruction in "Rotordynamics of High-Speed Rotating Machinery." Dr. Anastasio is a key member of the "Dynamics of mechanical systems and identification" research group within DIMEAS. His research integrates multiple ERC sectors including Mechanical and manufacturing engineering, ODE and dynamical systems, Signal processing, and Simulation engineering and modelling. His work aligns with Sustainable Development Goals 7 (Affordable and clean energy) and 9 (Industry, Innovation, and Infrastructure).

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.

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +6 more

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

Salvatore Ivan TrapassoView profile

Assistant Professor

• Applied harmonic analysis
• Fourier analysis
• Machine learning
• +5 more

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.

Alessandro ChiusoView profile

Professor

• Control Systems
• Machine Learning
• System Identification
• +3 more

Professor Alessandro Chiuso is a Full Professor in the Department of Information Engineering at the University of Padova, Italy. His research focuses on control systems, machine learning, system identification, and their applications to neuroscience and medical imaging. He has contributed to advancing data-driven control methodologies, regularization techniques, and brain network modeling. His work bridges theoretical control systems with practical applications, including predictive control under uncertainty, nonlinear system identification, and understanding brain dynamics through fMRI and PET data. Notable areas include developing algorithms for adaptive control, optimizing brain region interactions, and analyzing metabolic-functional couplings in neurological conditions. Recent publications highlight advancements in policy gradient methods for LQR control, sparse dynamic causal models for brain aging studies, and predictive control frameworks for stochastic systems. His research emphasizes the integration of machine learning with classical control theory to solve complex real-world problems.

Gianna Del CorsoView profile

Associate Professor

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +3 more

Gianna Del Corso is an Associate Professor at the Department of Computer Science, University of Pisa. Her research interests span Quantum Computing, Numerical Linear Algebra, and Spectral Analysis. She has advised PhD students in quantum computing topics such as quantum algorithms and machine learning. Her teaching includes courses on Numerical Calculus, Parallel Scientific Computing, and Introduction to Quantum Computing, delivered across multiple academic years at the University of Pisa. She has also contributed to advanced courses in the PhD program in Computer Science. Her research focuses on quantum algorithms for machine learning, quantum walks, eigenvalue computation of structured matrices, and spectral techniques for web analysis. Recent work includes applications of quantum k-means clustering and variance estimation subroutines. Her publications span quantum computing, numerical methods, and scientific citation models. Her articles highlight contributions to PageRank computation, matrix factorization for recommendation systems, and analysis of citation-based research evaluation models. She has actively engaged in international conferences, presenting on topics such as orthogonal iterations for nonlinear eigenvalue problems and quantum hitting time algorithms. Labs/Teams: Research conducted within the Department of Computer Science at the University of Pisa, focusing on quantum computing and numerical methods.