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.
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.
Alessandro Codello is a Researcher in Theoretical Physics at Ca’ Foscari University of Venice, based in the Department of Molecular Sciences and Nanosystems on the Scientific Campus. He is actively engaged in both research and teaching, delivering courses such as Physics of Complex Systems and Mathematical Methods for Physics and Engineering to master’s and undergraduate students. Research Interests His work centres on the renormalization group approach to quantum and statistical field theories, with particular emphasis on quantum gravity, conformal field theory, critical phenomena, and mathematical methods. He explores the non-perturbative structure of field theories through functional renormalization group techniques, investigates universality classes in diverse dimensions, and studies effective field theory constructions for gravitational interactions. Publication Profile From 2006 to 2023, Codello has authored or co-authored over forty peer-reviewed articles and one scientific monograph. The research spans high-energy physics, statistical mechanics and mathematical physics, with recurring themes of fixed-point structure, universality, conformal symmetry, and gravitational effective actions. Recent works address multicritical phenomena, long-range interacting systems, and the covariant formulation of effective quantum gravity. Teaching & Academic Service He teaches core and advanced physics courses in the master’s degree in Physics of Complex Systems and the bachelor’s degree in Physical Engineering . His teaching responsibilities include Engineering Physics , Mathematical Methods for Physics and Engineering , and Quantum Mechanics modules. Laboratory & Group Links His research is embedded within the Department of Molecular Sciences and Nanosystems at Ca’ Foscari, whose website is https://www.unive.it/dsmn .
Dr. Flavio Vella is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento. He holds roles on the management board of the national HPC laboratory at CINI and the Steering Committee of ICSC’s spoke4. His research focuses on parallel algorithms for emerging computing systems, machine learning systems, and quantum computing, with an emphasis on irregular computation and large-scale graph analysis. He has industrial experience at NVIDIA and Dividiti, and has contributed to EU projects like ARCHYTAS (AI acceleration) and NET4EXA (exascale networking infrastructure). Dr. Vella earned his Ph.D. from Sapienza University of Rome in 2017. His academic journey includes roles at the Free University of Bozen, CNR Italy, and ETH Zurich. He actively serves HPC communities as Artifact co-chair for PPoPP and Computing Frontiers, and as PC member for IPDPS, SC, and EuroPAR. His work has produced over 40 peer-reviewed publications, including Best Paper Awards at SC22/24 and Best PhD Paper at IPDPS17. His research themes include GPU performance optimization, quantum device reliability, and HPC/AI interconnects. Recent work explores tensor networks, physics-constrained neural networks, and exascale system engineering. Projects like ARCHYTAS (EUDF-2023) and NET4EXA (Horizon) highlight his leadership in European HPC initiatives.
Matteo Re is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on Machine Learning, Computational Biology, and Bioinformatics, with applications in genomics, drug network analysis, and semi-supervised learning. He has contributed to developing tools like RANKS for node label ranking in biological networks and HyperSMURF for predicting genetic variants. His work integrates graph-based methods, ensemble learning, and network modeling to address challenges in disease association, drug repositioning, and biomolecular data analysis. Re’s research interests include semi-supervised node classification, imbalanced data handling, and functional gene prioritization. He has collaborated on projects involving clinical phenotype prediction using patient biomolecular networks and drug therapeutic categorization. His methods emphasize scalable learning, secondary memory usage, and multi-species data fusion. Awards and grants are not explicitly mentioned, but his extensive publication record reflects strong contributions to computational biology and machine learning.
Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.
Francesco Carrabs is a Full Professor of Operations Research in the Department of Mathematics at the University of Salerno, Italy, a position he has held since 2024. Previously, he served as Associate Professor (2020-2024) and Assistant Professor (2008-2020) at the same institution, demonstrating a steady progression through the academic ranks. Dr. Carrabs earned his PhD in Computer Science from the University of Salerno in 2006 and graduated cum laude in Computer Science from the same university in 2002. His international research experience includes a visiting scholar position at the Centre de recherche sur les transports (CRT) at the University of Montréal, Canada (2004-2006) under Professors Gilbert Laporte and Jean-François Cordeau, followed by a post-doctoral fellowship at HEC Montréal (October 2006-March 2007). His research focuses on theoretical study, development and implementation of exact and heuristic approaches for optimization problems on graphs. His primary research areas include Routing problems, Variants of Spanning Tree problems, Traveling Salesman Problems, Labeled Graph problems, and Wireless Sensor Networks problems. This program has resulted in approximately 50 publications across various formats. His recent publications reveal a strong emphasis on constrained graph problems, particularly those involving conflict constraints in spanning trees and set covering problems. His methodological contributions span exact approaches like branch-and-cut algorithms and heuristic methods including GRASP and genetic algorithms, often accompanied by publicly available datasets and source code. Dr. Carrabs has participated in numerous national and international research projects and has served on scientific committees for various conferences. His teaching activities at the University of Salerno focus on Operations Research and Optimization courses within the SSD MATH-06/A classification. With an h-index of 16 and over 800 citations (excluding approximately 13% self-citations), Dr. Carrabs has established himself as a respected researcher in combinatorial optimization and operations research.
Raffaella Burioni is a Full Professor of Theoretical Physics in the Department of Mathematics, Physics and Computer Science at the University of Parma. She serves as Chair of the Non-Linear and Statistical Physics Division of the European Physical Society (EPS) and co-founded the Italian Society of Statistical Physics (SIFS), where she holds the position of Vice President. Her academic leadership includes directing the Ph.D. School in Physics and previously managing quality assurance for the Master's program in Physics (2017-2023). Her educational background includes an MS in Physics with distinction from the 2nd University of Rome and a PhD in Theoretical Physics from the University of Rome 'La Sapienza'. Postdoctoral experience spans the Theoretical Physics Laboratory of the École Normale Supérieure in Paris, the University of Milan, and the National Institute for the Physics of Matter (INFM). Prof. Burioni's research centers on equilibrium and non-equilibrium statistical physics, with deep expertise in graph theory, complex networks, random walks, and stochastic processes. She pioneers interdisciplinary applications to biological systems, neuroscience, and machine learning. Her work on rare events and anomalous diffusion has established fundamental principles like the 'single big jump' mechanism in transport phenomena. Recent investigations bridge statistical physics with neural network theory, examining kernel renormalization and feature learning in deep architectures. Analysis of her 15 most recent publications (2022-2025) reveals three dominant trends: (1) Theoretical advances in rare event statistics for jump processes and extreme value theory, (2) Network-based epidemic modeling incorporating adaptive temporal dynamics and simplicial structures, and (3) Machine learning physics connecting neural network theory to statistical mechanics through Bayesian effective actions and kernel methods. Her work consistently demonstrates how statistical physics principles solve complex problems across disciplines. Her scientific accolades include: Two-time recipient of the Enrico Persico Prize from the Accademia Nazionale dei Lincei Fellow of the Institute for Scientific Interchange (ISI) since 2014 American Physical Society Outstanding Referee (2018) Fellow of the European Centre for Living Technology (2020) As Director of the Ph.D. School in Physics, she mentors doctoral candidates while serving on editorial boards for Physical Review E, JSTAT, and Journal of Physics A. Her research is supported by international collaborations with institutions including the Kavli Institute for Theoretical Physics, Max Planck Institute, and Ben-Gurion University, evidenced by frequent keynote invitations at major conferences like StatPhys, ECCS, and APS March Meetings. Prof. Burioni leads collaborative research networks through her EPS division chairmanship and SIFS leadership, fostering cross-institutional projects on statistical physics applications. Her campus-based studies leverage Wi-Fi data from Parma University to model pedestrian dynamics and epidemic spreading in real-world constrained environments.
Pietro Rotondo is an Assistant Professor (Ricercatore a tempo determinato) at the University of Parma , Italy, within the Department of Mathematical, Physical and Computer Sciences . He is actively involved in lecturing the course Principles of Physics to first-year students of the Earth Sciences bachelor programme for the academic years 2023/2024 and 2024/2025. Education & Academic Background: While explicit educational history is not detailed in the text, his faculty rank and research output indicate advanced training in theoretical physics and statistical mechanics, likely culminating in a PhD. Research Interests: Statistical mechanics of deep learning and artificial neural networks Bayesian inference in high-dimensional, disordered systems Phase transitions and replica methods in complex systems Quantum many-body physics and cavity quantum electrodynamics Renormalization group approaches to finite-width neural networks His interdisciplinary work bridges rigorous physics techniques with modern machine-learning challenges, aiming to uncover universal laws governing learning and generalization in artificial systems. Recent Publication Trends: Over the past five years, Rotondo has concentrated on developing analytical frameworks that describe how deep neural networks learn and generalize, particularly beyond the infinite-width limit. Recurring themes include Bayesian effective actions, kernel renormalization, and the statistical mechanics of structured data. Scientific Awards & Honors: No specific awards are mentioned in the provided text. Supervision & Funding: The text does not list current PhD or Master’s students, nor does it detail specific grants or funded projects. Laboratories & Teams: No named laboratories, centers, or research groups are explicitly cited in the scraped material.
Francesco Marchetti is a fixed-term researcher (RTDa) at the Department of Mathematics "Tullio Levi-Civita" of the University of Padova. He holds a Master's degree in Mathematics and a PhD in Health Planning Sciences, both from the University of Padova (2016 and 2021 respectively). His research spans interdisciplinary areas including mathematical approximation theory, machine learning, and applications in medical imaging and space weather forecasting. Education Master in Mathematics, University of Padova (2016) PhD in Health Planning Sciences, University of Padova (2021) Research Interests Approximation theory and kernel-based methods Machine learning and deep learning applications Magnetic Particle Imaging (MPI) signal reconstruction Space weather forecasting via neural networks He actively collaborates with the MIDA group at the University of Genova and participates in research networks like CAA (Constructive Approximation and Applications) and RITA (Rete Italiana di Approssimazione). Current work focuses on p-Laplacians on hypergraphs and associated machine learning frameworks. Scientific Awards INdAM Postdoctoral Grant (2021)
Giovanni Girardi is a Researcher in the Department of Industrial Engineering and Mathematical Sciences at Marche Polytechnic University (UNIVPM) , Italy. His work spans mathematical analysis , partial differential equations , and applied mathematics , focusing on problems involving critical nonlinearity , fractional calculus , and non-local modeling . Contact Information: Email: g.girardi@staff.univpm.it Research Areas: Girardi investigates evolution equations with critical nonlinearities, time-dependent damping mechanisms, and fractional diffusion models. His work extends to environmental applications like plant water deficit modeling through Richards' equation and soil hydrology. Publication Trends: Over the past six years, Girardi's research has focused on fractional and hyperbolic PDEs, critical exponents, and damping effects in wave equations. His 2025 papers emphasize lifespan estimates and well-posedness, while 2024 studies highlight non-local hydrological models. Applications range from theoretical mathematics to environmental engineering.
Luca Ferretti is an Associate Professor at the University of Modena and Reggio Emilia , affiliated with the Department of Physical, Computer and Mathematical Sciences . His research focuses on cybersecurity, edge/cloud computing, and machine learning applications in security. Office located in the former Mathematics campus (Building MO18, third floor), Modena, Italy Scientific disciplinary sector: INFO-01/A Computer Science Research areas include: Edge and Fog Computing Automotive Cyber Security Security for Industry 4.0 Security for Machine Learning and Machine Learning for Security Applied Cryptography for Network and Information Security Luca is a core member of the SECloud (Security Edge and Cloud Lab) , which operates within the Department of Engineering 'Enzo Ferrari' and collaborates with the Interdepartmental Research Center on Security and Risk Prevention (CRIS). His recent publications highlight applications of adversarial AI, decentralized systems, and secure automotive communication protocols.
Mariano Massimiliano Croce serves as Full Professor in the Department of Finance at Bocconi University, holding significant academic appointments including Director of the PhD in Economics and Finance program since 2019. His institutional affiliations extend to CEPR, IGIER, and Baffi-Carefin research centers, reflecting his standing in the international academic community. Professor Croce teaches an extensive curriculum including Capital Markets, Understanding Investments, Empirical Methods for Finance, and advanced topics in Asset Pricing across undergraduate, Master's, MBA, and PhD programs at Bocconi and other leading institutions worldwide. His educational foundation includes: PhD in Economics from New York University Master's and Bachelor's degrees in Economics from Bocconi University, Milan Professor Croce's research program centers on asset pricing within general equilibrium frameworks where uncertainty about long-horizon economic perspectives (growth news shocks) plays a critical role. His work systematically explores international asset prices and exchange rate dynamics, the global interplay between asset prices and investment decisions, connections between investor information processing and market outcomes, and the growth implications of fiscal policy risks. As a pioneer in MacroFinTech, his theoretical contributions have reshaped understanding of how long-term economic uncertainty manifests in financial markets across international boundaries. His methodological approach combines sophisticated theoretical modeling with rigorous empirical analysis, particularly focusing on high-frequency data applications in contemporary financial research. Analysis of his recent publications reveals a clear progression from foundational theoretical work on recursive preferences and long-run risk toward increasingly applied research examining real-world phenomena including climate-related financial risks, pandemic market reactions, and innovative applications of text analysis to financial markets. The interdisciplinary nature of his work bridges traditional finance with macroeconomics, environmental economics, and policy analysis, demonstrating growing relevance to contemporary global challenges. Professor Croce's scholarly achievements have been recognized through: CEPR Research Fellowship (awarded September 2017) NBER Research Associate appointment (April 2018) Teaching Award in PhD Program Co-Editorship of Economics Letters (since 2021) Appointment to the American Economic Review Editorial Board (2025) As academic leader of Bocconi's PhD program since 2019, Professor Croce shapes the next generation of financial economists while maintaining an active research agenda. His teaching portfolio spans institutions including Wharton, STERN, ISB, and Kenan-Flagler, demonstrating global recognition of his expertise. Research support for his work comes through prestigious affiliations including prior research internships at the Federal Reserve Board of Governors and European Central Bank, and ongoing collaborations through CEPR and NBER networks that facilitate international scholarly exchange. Professor Croce maintains active research leadership through his editorial role at Economics Letters and as Co-Editor of the Reading Group in Asset Pricing at Bocconi. His research team focuses on developing new methodologies for analyzing long-term economic risks and their financial market implications, with recent expansion into climate finance and text-based analysis of market reactions to emerging global challenges.
Giorgio Stefano Gnecco is a Full Professor in Mathematical Methods of Economics and of Actuarial and Financial Sciences at IMT School for Advanced Studies Lucca, where he works within the Analysis of compleX Economic Systems (AXES) research unit. His academic career spans multiple disciplines including optimization, machine learning, game theory, and their applications in economics, finance, and engineering. His research interests focus on optimization applied to actuarial sciences, economics, finance, and engineering; game theory; statistics; machine learning theory and applications; causal inference for economic policy evaluation; and environmental economics. His work demonstrates interdisciplinary connections between mathematical theory and practical applications across diverse fields, with particular emphasis on developing computational methods for complex economic systems. His publication record shows consistent output across multiple domains, with recent work spanning machine learning algorithms, image processing techniques, economic modeling, and applications in music performance analysis. The breadth of his research indicates strong methodological foundations in mathematical optimization and statistical learning, applied to problems ranging from flood hazard assessment to Parkinson's disease classification. Among his notable achievements are five Italian National Scientific Qualifications for professorial positions in various fields, demonstrating his recognized expertise across multiple academic disciplines. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Neural Networks, and Associate Editor for Neurocomputing. Professor Gnecco leads numerous research projects including the "PRIN PNRR 2022" project "MOTUS - Automated Analysis and Prediction of Human Movement Qualities," the "INdAM-GNAMPA 2023" project on machine learning methods for Shapley Value estimation, and the "ROBOFARM" project on edge computing for precision agriculture. He has coordinated multiple international research collaborations between Italy and France through the Galileo program. His research unit (AXES) focuses on complex economic systems analysis, with applications spanning environmental economics, financial systems, and human movement analysis. The collaborative nature of his work is evident through his extensive network of domestic and international collaborators across multiple universities and research institutions.
Nicolò Navarin is an Associate Professor in Computer Science at the University of Padua, Italy, within the Department of Mathematics. He serves as the head of the department and is a key member of the Machine Learning Group @ UniPD. His academic journey includes research fellowships at the University of Nottingham under Prof. Thomas Gärtner and the University of Padova under Prof. Alessandro Sperduti, along with a visiting researcher position at the University of Freiburg, Germany. Dr. Navarin's research spans Artificial Intelligence with deep expertise in: Machine Learning on structured data Kernel methods and Artificial Neural Networks Statistical learning theory and Online learning Machine learning for Bioinformatics and Business process mining Natural Language Processing and Knowledge Representation Remote sensing applications His scientific recognition includes: NVIDIA GPU Grant (Titan Xp) awarded in 2018 As an active supervisor, Dr. Navarin leads the Machine Learning Group with secured computational resources through grants like the NVIDIA GPU award. He organizes major academic events including INNS Big Data and Deep Learning 2019 and special sessions at IJCNN 2018 ('Empowering Deep Learning Models') and ESANN 2018 ('Emerging trends in machine learning'). His Friday afternoon office hours facilitate student collaboration through a dedicated booking system. The Machine Learning Group at University of Padua maintains strong international collaborations, evidenced by Dr. Navarin's research fellowships in the UK and Germany. The group's August 2020 website relaunch reflects its dynamic research environment pursuing both theoretical advancements and real-world applications across bioinformatics, business analytics, and remote sensing domains.
