جستجوی استاد راهنمای دانشگاهی

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +۱ مورد دیگر

Michele Salvi is an Associate Professor in Mathematics at Università degli Studi di Tor Vergata in Rome. He previously held a Marie Skłodowska-Curie fellowship, conducting research in Berlin, Munich, and Paris. His work focuses on Probability Theory, with emphasis on random processes in random media, random graphs, and statistical mechanics, bridging applications in Physics, Computer Science, and Biology. Random processes in random media Random graphs Mathematics of Neural Networks Stochastic homogenization Mixing times for Markov chains Statistical mechanics Salvi’s recent publications highlight interdisciplinary trends, particularly in the spectral analysis of deep neural networks, scale-free percolation dynamics, and spanning tree geometry in random environments. His collaborations span Europe, with projects involving probabilistic models in epidemiology, reinforcement learning, and stochastic homogenization. He has received the Marie Skłodowska-Curie fellowship, reflecting his international research experience. His work is aligned with the Department of Mathematics at Tor Vergata, which holds the "Department of Excellence" MatMod@TOV 2023-2027 grant.

• Probability Theory
• Random Processes in Random Media
• Random Graphs
• +۴ مورد دیگر

Giacomo Fiumara is an Associate Professor at the University of Messina, Department of Mathematical and Computer Sciences, Physical Sciences and Earth Sciences. He holds academic rank since October 2021. Previously, he served as a Permanent Researcher (2008–2021) and secondary school teacher (1997–2008). He earned a Doctorate in Physics (1993) and a Degree in Physics (1989), both from the University of Messina. He is an associate member of the Accademia Peloritana dei Pericolanti and qualified as an associate professor in INF/01 and ING-INF/05 sectors. His research focuses on social network analysis, network science, data science, criminal networks, knowledge representation, bioinformatics, and computational modeling. He has supervised over 170 theses and advised PhD students in Mathematics and Computational Sciences. Key collaborations include work with Prof. Pasquale De Meo on criminal networks and complex systems, and international projects with institutions in the US, UK, China, and Australia. Teaching includes courses on Algorithms, Data Structures, Bioinformatics, and Machine Learning across Computer Science, Engineering, and Medical programs since 2000. He also contributed to international programs at Lviv Polytechnic, Birzeit University, Cluj-Napoca, and Murcia. His editorial roles include Associate Editor of IEEE Access and Academic Editor of Complexity. He holds a patent for predictive analysis of criminal organizations' social structures and has received FFABR research funding. Key awards include FFABR funding (2017) and recognition in the FFABR Unime 2020 II edition. He organized conferences like Crimenet 2014 and participated in high-profile events such as the 2022 Complex Networks conference in Palermo, presenting on quantum walks for criminal network analysis.

• Social Network Analysis
• Network Science
• Data Science
• +۷ مورد دیگر

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milano, Italy. She previously held academic positions at TU Eindhoven (Netherlands) and the University of Waterloo (Canada). Education: Bachelor's in Management Engineering (2003), Università di Roma Tor Vergata Master's in Management Engineering (2005), Università di Roma Tor Vergata PhD in Operations Research (2009), Università Sapienza di Roma Postdoctoral Fellow at EPFL (Switzerland, 2009-2011) Laura's research focuses on discrete mathematics, theoretical computer science, and operations research, with particular emphasis on algorithmic solutions for combinatorial optimization problems. Her work spans approximation algorithms, network design, polyhedral combinatorics, and algorithmic game theory, advancing methodologies for solving complex optimization challenges in theoretical and applied contexts. Her publications demonstrate expertise in network design problems, spanning from fundamental polyhedral characterizations to game-theoretic applications. Key contributions include iterative randomized rounding techniques for Steiner trees, circuit augmentation algorithms, and stabilization approaches for network games. Scientific Awards: NWO-VIDI Award (Netherlands) Discovery Accelerator Supplements Award (NSERC, Canada) Golden Jubilee Research Excellence Award (University of Waterloo) Early Researcher Award (Ontario) Best Paper Award at STOC 2010 Advising & Collaborations: Laura advises PhD students Dylan Hyatt-Denesik and Lucy Verbeck, and collaborates with postdoctoral researchers like Afrouz Jabal Ameli. She has served on program committees and as Associate Editor for top journals including Mathematical Programming, Mathematics of Operations Research, and Operations Research Letters.

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +۳ مورد دیگر

Enrico Magli is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, Italy. He serves as Director of the Image Processing and Learning group and Coordinator of the 'ICT for Smart Societies' M.Sc. degree program. Additionally, he is a committee member of the PhD program in Electrical, Electronic and Communications Engineering and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. Professor Magli's research focuses on applying machine learning and deep learning methods to satellite imaging, with applications to onboard processing and image analysis on the ground. His work spans deep learning for image and video analysis, image and video compression, compressive sensing, satellite imaging, and graph signal processing. He has published over 90 journal papers with 5900+ citations and an h-index of 40 on Google Scholar. His recent publications demonstrate a strong focus on developing deep learning architectures for satellite image processing, particularly for onboard applications. His research addresses challenges in hyperspectral image compression, super-resolution, change detection, and efficient neural network architectures suitable for resource-constrained satellite environments. There's also significant work on secure authentication systems using deep learning techniques and neural network optimization for edge devices. Elevated to IEEE Fellow (2017) 'for contributions to compression and communication of remotely sensed imagery' IEEE Geoscience and Remote Sensing Society 2011 Transactions Prize Paper Award IEEE Multimedia 2019 Best Paper Award Best Paper Awards at IEEE ICIP (2015, 2019) ERC Starting grant (consolidator type) and ERC Proof-of-Concept Grant recipient Multiple Best Paper Awards Francesco Carassa (2011, 2013, 2014) Professor Magli actively supervises numerous PhD students working on cutting-edge topics in deep learning for satellite imaging, image processing, and secure authentication systems. His research is supported by significant grants including ERC projects and multiple commercial contracts with space agencies and technology companies. He leads the Image Processing and Learning (IPL) Group at Politecnico di Torino, which focuses on developing innovative solutions for satellite image analysis and compression.

• Artificial intelligence
• Data compression
• Deep learning
• +۱۵ مورد دیگر

Davide Cassi serves as Associate Professor of Physics of Matter at the University of Parma's Department of Mathematical, Physical and Computer Sciences since 2001, following his appointment as Researcher in Theoretical Physics (1995-2001). With over 30 years of academic service, he teaches Condensed Matter Physics, Soft Matter Physics, and Physics Applied to Gastronomy across undergraduate and graduate programs in Physics and Gastronomic Science. His educational background includes: Ph.D. in Physics, University of Parma (1988-1992) Master’s degree in Materials Science and Technology, University of Parma (1986-1988) Degree in Physics, University of Parma (1982-1986) Cassi's research integrates statistical mechanics with real-world applications through two primary lenses: complex network theory for ecological and social systems, and soft matter physics applied to culinary processes. His work on biodiversity loss prediction in agricultural networks and food preservation technologies demonstrates exceptional interdisciplinary reach. Recent publications reveal a strategic pivot toward AI-driven biodiversity conservation and network robustness modeling. Analysis of his 15 most recent publications (2023-2025) shows dominant themes in network vulnerability analysis (68% of works) and food-physics applications (27%), with emerging focus on machine learning integration for ecological modeling. His research bridges theoretical physics with practical solutions in food safety and ecosystem management. Key recognitions include: Grand Prix de la Science de l'Alimentation from Académie Internationale de la Gastronomie (2012-2013) Dual National Scientific Qualifications for Full Professorship (2022) in Theoretical Physics of Fundamental Interactions and Matter Cassi's academic contributions extend beyond publications to two international patents in food preservation technology and editorial leadership since 2007 for World Scientific's Series on Advances in Statistical Mechanics . His research program demonstrates consistent translation of theoretical physics into practical applications across gastronomy and ecology, with growing emphasis on AI-enhanced network analysis for sustainability challenges.

• Statistical Mechanics
• Condensed Matter Physics
• Soft Matter Physics
• +۴ مورد دیگر

Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.

• Logic Programming
• Statistical Relational Artificial Intelligence
• Probabilistic Logic Programming
• +۳ مورد دیگر

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.

• Machine Learning
• Online Learning
• Multi-Armed Bandits
• +۴ مورد دیگر

Professor Silvio Franz is affiliated with the Department of Mathematics and Physics 'Ennio De Giorgi' at the University of Salento (Italy). His research career spans over 30 years with 110+ publications, focusing on the statistical mechanics of disordered systems and their interdisciplinary applications. Key contributions include the development of the Franz-Parisi potential for studying glass transitions and rigorous mathematical frameworks for spin glasses. PhD in Theoretical Physics Full Professor at University of Salento Research Interests center on spin glasses and glassy systems , with applications to: Theoretical Neuroscience Machine Learning Population Genetics Constraint Satisfaction Problems Random Matrix Theory Theoretical Computer Science His work connects statistical physics to: Information Theory Optimization Algorithms Neural Network Modeling Evolutionary Biology Complex Systems Theory Key Publications demonstrate: Landau theory for glasses Universality in jamming transitions Stochastic stability analysis Effective temperature formulations Replica symmetry breaking Applications to error-correcting codes

• Statistical Mechanics of Disordered Systems
• Spin Glasses
• Glass Transition Phenomena
• +۵ مورد دیگر

Giovanni Russo is a Full Professor of Numerical Analysis at the Department of Mathematics and Computer Science, University of Catania, Italy . He coordinates the PhD program in Pure and Applied Mathematics and has been a visiting scholar at institutions including Courant Institute, University of California, Los Angeles, University of Michigan, and University of Bordeaux. His career spans over four decades across academia and research institutions. Education: PhD in Physics (1986, University of Catania), Laurea in Nuclear Engineering (1982, Politecnico di Milano). Research Interests: Russo specializes in Computational Fluid Dynamics , Numerical Methods for Conservation Laws , and Kinetic Equations . His work includes asymptotic preserving schemes , IMEX methods , semi-Lagrangian schemes , and high-order numerical techniques for PDEs with applications to fluid dynamics, plasma physics, and multiscale modeling. Scientific Trends: Recent publications focus on modeling epidemic dynamics , kinetic equations for inert mixtures , semi-Lagrangian methods , and uncertainty quantification in quantum systems. These works reflect his expertise in high-order numerical schemes , multiscale analysis , and applied mathematical modeling . Scientific Awards: CNR-NATO Fellowship (1987) Advising and Grants: Russo has supervised nine PhD students and served as Principal Investigator (PI) for major projects including MOSCOVID (modeling COVID-19) and ModCompShock (Horizon 2020 Marie Curie project). He has also organized international conferences like the 18th European Conference on Mathematics for Industry with 370 participants. Labs and Teams: Russo collaborates with research groups at the University of Catania and has been a visiting researcher at Courant Institute, University of Michigan, and GSSI L’Aquila. He contributes to journals as an editor and reviewer, including SIAM Journal of Numerical Analysis and Journal of Computational Physics .

• Computational Fluid Dynamics
• Numerical Methods for Conservation Laws
• Numerical Methods for Kinetic Equations
• +۵ مورد دیگر

Andrea Tosin is a Full Professor of Mathematical Physics at the Department of Mathematical Sciences "G. L. Lagrange" (DISMA), Politecnico di Torino. He serves as Coordinator of the Doctoral College of Mathematical Sciences and Deputy Coordinator of the Doctoral College of Pure and Applied Mathematics. His research bridges kinetic theory, transport equations, and applied mathematics with applications in multi-agent systems, traffic, social dynamics, and epidemiology. His research interests focus on: Kinetic theory and its applications to real-world systems Transport and diffusion equations in complex environments Modeling of vehicular traffic, crowd dynamics, and social behavior Epidemiological modeling with a focus on viral load and multi-scale dynamics Mathematical modeling of collective behavior in biological and social systems His recent publications demonstrate a consistent trend in developing and analyzing kinetic models for traffic flow, opinion dynamics, and epidemic spread, often incorporating uncertainty, network structures, and multi-population interactions. These works frequently involve rigorous mathematical derivations from microscopic models to macroscopic equations, with applications in safety, public health, and urban planning. His scientific awards include: SIMAI Biennial Award (2013) INDAM-SIMAI Award (2010) He actively supervises PhD students and postdoctoral researchers, including Martina Fraia, Emanuele Bernardi, Elisa Paparelli, and Mattia Sensi. He has secured significant research grants from national (PRIN, INdAM) and institutional (Politecnico di Torino, Google) sources. His research is supported by projects such as IMASED (Integrated Mathematical Approaches to Socio-Epidemiological Dynamics) and ANATOMY (A Unitary Mathematical Framework for Modelling Muscular Dystrophies). He also leads the "Modelli e Metodi della Fisica Matematica" research group at DISMA.

• Mathematical Physics
• Kinetic Theory
• Transport and Diffusion Equations
• +۷ مورد دیگر

Mauro Andreolini is a University Researcher at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. He teaches Operating Systems and Secure Software Development courses within the Computer Science degree program. His research focuses on Cybersecurity , Network Security , Machine Learning in Security , and Cloud Computing . His recent publications analyze Data Privacy through geohashing and clustering, Adversarial Attacks in cybersecurity, and Moving Target Defense architectures. He has also contributed to frameworks for Automated Security Assessments using deductive reasoning and Realistic Botnet Detection benchmarks. Andreolini's work addresses Graph Neural Networks in intrusion detection, n-Gram Analysis for automotive network security, and Side-Channel Vulnerabilities in USB devices. He collaborates with researchers like Artioli, Ferretti, Marchetti, and Colajanni on projects spanning Adversarial Machine Learning , Secure Software Development , and Cloud-Based Monitoring .

• Computer Science
• Cybersecurity
• Network Security
• +۴ مورد دیگر

Ada Fort is an Associate Professor in the Department of Information Engineering and Mathematics at the University of Siena, Italy. Her research focuses on advanced sensor systems, including environmental monitoring, biomedical instrumentation, and IoT applications. She holds a Laurea in Electronic Engineering (1989) and a Ph.D. in Nondestructive Testing (1992) from the University of Florence. Key research areas include wearable sensors for air quality monitoring, magnetic detection of contaminants, and QCM-based biosensors. Her work integrates machine learning for signal processing and fault detection in industrial and environmental systems. Notable projects involve self-sufficient IoT nodes powered by solar energy and low-cost sensor networks for agriculture and healthcare. Publications highlight innovations in sensor design, data imputation for environmental monitoring, and entropy-based security systems. Her contributions span interdisciplinary fields, linking engineering principles with applications in healthcare, agriculture, and climate science.

• Sensor Technology
• Environmental Monitoring
• IoT Systems
• +۳ مورد دیگر

Alberto Marcone is a Full Professor of Mathematical Logic at the University of Udine, where he serves as the Director of the Department of Mathematical, Computer and Physical Sciences (DMIF) since October 1, 2024. His academic home is within the Department of Mathematical, Computer and Physical Sciences at the University of Udine, located at Via delle Scienze, 208 -- Loc. Rizzi, 33100 Udine, Italy. Professor Marcone's research spans several interconnected areas within mathematical logic. His primary interests include reverse mathematics, descriptive set theory, well-quasi-order and better-quasi-order theory, and computable analysis with particular emphasis on the Weihrauch lattice. His work explores the logical strength of mathematical theorems, classification problems in continuum theory, and the computational content of mathematical principles. He has made significant contributions to understanding the relationships between different mathematical principles and their proof-theoretic strength. Analysis of his recent publications reveals a consistent focus on the intricate connections between order theory, reverse mathematics, and descriptive set theory. His work often examines the logical strength of combinatorial principles related to well-quasi-orders and better-quasi-orders, while increasingly exploring connections to computable analysis through the Weihrauch lattice framework. Recent publications demonstrate growing international collaboration, particularly with researchers across Europe, and an expanding application of logical methods to topological and metric space problems, including connections to knot theory and fractal geometry. Member of editorial board of the journal Computability Member of the board of the PhD program in Mathematics and Physics Organizer of Logic Colloquium 2018 (Udine) Organizer of Special Session on Computability Theory at AMS-UMI International Joint Meeting (Palermo, July 2024) Organizer of XXVIII Incontro di Logica AILA (Udine, September 2024) Professor Marcone teaches Mathematical Logic for both undergraduate and graduate mathematics programs at the University of Udine. His office is located on the 2nd floor, room A2 90, where he holds student reception hours either in person or via Microsoft Teams by appointment. As department director, he oversees the academic and administrative functions of the Department of Mathematical, Computer and Physical Sciences.

• Mathematical Logic
• Reverse Mathematics
• Descriptive Set Theory
• +۴ مورد دیگر

Marc Mezard is a Professor of Theoretical Physics at Bocconi University, where he leads the newly established Department of Computational Sciences. Previously, he served as Research Director at CNRS in Paris and held roles at Université Paris Sud. He earned his PhD in Physics from École Normale Supérieure in Paris in 1984. His research focuses on statistical physics of disordered systems, with applications to machine learning, information theory, computer science, and biophysics. His work bridges theoretical physics and interdisciplinary fields, including neural networks and deep learning, where he explores the impact of data structure on learning strategies. He teaches undergraduate courses in statistical and quantum physics and a doctoral course on complex systems. Key research themes include emergent phenomena in complex systems, with contributions to spin glass theory, compressed sensing, and algorithmic solutions for random satisfiability problems. His publications span foundational topics in statistical mechanics and modern applications in data science. Marc Mezard has collaborated extensively with institutions and researchers globally, contributing to the theoretical foundations of computational and physical sciences. His academic leadership includes directing École Normale Supérieure from 2012 to 2022, fostering interdisciplinary research initiatives.

• Statistical Physics
• Machine Learning
• Information Theory
• +۵ مورد دیگر