جستجوی استاد راهنمای دانشگاهی

Tommaso Vanzan is a Researcher at the Department of Mathematical Sciences "GL Lagrange" (DISMA) at Politecnico di Torino . He holds a PhD from the University of Geneva (2020) under Prof. Martin J. Gander and was a postdoctoral researcher at EPFL (2020-2023) under the CSQI Chair. His research focuses on iterative methods , optimization algorithms , and uncertainty quantification for PDE-constrained problems, with applications in Stokes-Darcy coupling and manifold-based machine learning . His recent work includes multilevel quadrature for risk-averse optimization and spectral coarse spaces for substructured Schwarz methods. He has received the SWICCOMAS Prize 2021 for computational methods. He contributes to the open-source GDGMatlab library for finite element and discontinuous Galerkin methods, emphasizing educational and research usability over high-performance computing. Future developments target generalized boundary conditions in DG formulations.

• Numerical Analysis
• Domain Decomposition
• PDE-Constrained Optimization
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Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)

• Computer Networks
• Machine Learning
• Reinforcement Learning
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Francesco Buono is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) within Politecnico di Torino . His research focuses on probability theory, mathematical statistics, and entropy-based measures, with a particular emphasis on reliability engineering and stochastic modeling. He has published extensively on topics such as extropy, Kullback-Leibler divergence, and generalized entropy in applications to system reliability and ranked set sampling. His recent publications (2023–2025) highlight collaborations with researchers like Franco Pellerey, Jorge Navarro, and Maria Longobardi. Key trends include advancements in stochastic orders , predictive modeling , and information-theoretic measures applied to failure analysis and statistical inference. Notable journals include Entropy , Communications in Statistics , and Journal of Applied Probability .

• Probability
• Mathematical Statistics
• Entropy Measures
• +۳ مورد دیگر

Valentino Peluso is a Fixed-Term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on Edge AI , Electronic Design Automation , Federated Learning , and Low-Power Design , with applications in distributed systems and cyber-physical technologies. Associate Editor at IEEE Transactions on Circuits and Systems II (2025-) Organizer of workshops on smart grids and IoT at IEEE conferences Recipient of Politecnico di Torino's RIMINI SCHOOL 2025 badge for research funding participation His recent work explores homomorphic encryption for secure federated learning, DVFS side-channel attacks in edge inference, and energy-efficient pipeline optimization for embedded systems. Publications span IEEE Transactions, ICECS, ICCD, and VLSI-SoC conferences. Scientific Awards & Roles: RIMINI SCHOOL 2025 Certification Learning to Teach (L2T) badge Editorial contributions to IEEE journals Chair and co-chair roles in hardware/software co-design sessions He contributes to teaching in High-Level Synthesis , Machine Learning for IoT , and Efficient AI Computing at both PhD and MSc levels.

• Edge AI
• Electronic Design Automation
• Federated Learning
• +۳ مورد دیگر

Massimo Melucci serves as Associate Professor of Computer Science at the University of Padua's Department of Information Engineering, maintaining an active research and teaching profile with office location in Building DEI/G, Room 312, Padova, Italy. His instructional responsibilities include undergraduate Information Retrieval and Databases courses, alongside graduate-level Computing Methods for Data Science instruction, complemented by freely available lecture notes on information retrieval fundamentals. His research program centers on two pioneering domains: Quantum-inspired Information Retrieval , where he develops mathematical frameworks applying quantum probability to model contextual relevance and interference effects in search systems, and Algorithmic Fairness , investigating trade-offs between ranking effectiveness and equitable outcomes. These threads converge in his exploration of fairness-effectiveness optimization through eigensystems and variation modeling, while his interdisciplinary work extends into diving medicine neurophysiology , analyzing EEG-based brain connectivity disruptions in professional divers under hyperbaric conditions. Recent publication analysis (2020-2025) reveals three dominant trajectories: quantum-inspired neural architectures for multimodal sentiment analysis, rigorous fairness-effectiveness quantification in ranking algorithms, and biomedical applications of signal processing in extreme environments. He actively fosters academic collaboration through initiatives like the BIRDS workshop series and CIRCLE conference, which bridge information science, retrieval, and data science communities. His work demonstrates consistent innovation in applying non-classical probability models to information access challenges while maintaining strong connections to real-world applications in both search technology and human safety.

• Information Retrieval
• Data Science
• Quantum-inspired Information Retrieval
• +۲ مورد دیگر

Gabriele Accarino is a Postdoctoral Research Scientist at Columbia University's Learning the Earth with Artificial Intelligence and Physics (LEAP) Science and Technology Center, focusing on machine learning applications in climate science. He previously held a research fellowship at the Department of Engineering for Innovation at the University of Salento and served as an adjunct professor there. His career includes a Junior Scientist role at the CMCC Foundation's Advanced Digital Innovation Center (ADIC), where he led the machine learning research unit and contributed to European projects like Silvanus. Education: Master’s in Computer Engineering (with honors), University of Salento PhD in Environmental Sciences, University of Salento His research spans data-driven climate modeling, spatio-temporal analysis, and machine learning for extreme weather events, pandemics, and environmental conflicts. He specializes in LSTM networks, Transformer architectures, and HPC integration with big data analytics. Recent publications highlight his work on AI applications in aquatic sciences, climate change knowledge transfer, ensemble machine learning for cyclones, and pandemic modeling. His projects address interdisciplinary challenges like environmental-COVID-19 links and pollution-conflict dynamics. Scientific Awards: Research Fellowship at University of Salento's Department of Engineering for Innovation Gabriele co-supervises European research initiatives and maintains affiliations with the CMCC Foundation while advancing postdoctoral work at Columbia University. His expertise bridges climate science, computational methods, and public health analytics.

• Machine Learning
• Climate Science
• Environmental Data Analysis
• +۴ مورد دیگر

Anna Palmisano is an Associate Professor of Diagnostic Imaging and Radiotherapy at Vita-Salute San Raffaele University , Milan, Italy. She combines clinical expertise in diagnostic radiology with cutting-edge research in advanced imaging techniques for cardiovascular, oncological, and metabolic diseases. Education : Master's in Medicine (2010, summa cum laude), Specialization in Diagnostic Radiology (2016, summa cum laude), PhD in Molecular Medicine (2020). Her research focuses on multimodal imaging (CT/MRI) for tissue characterization, biomarker discovery, and artificial intelligence applications in cardiology and oncology. She has coordinated competitive projects like Trustworthy AI for Coronary Plaque Prediction (Regione Lombardia 2022) and Advanced Cardiac CT for TAVR Prognostication (PRIN-PNRR 2023). Recent publications highlight her work in cardiovascular imaging (e.g., myocarditis diagnostics via T2 mapping, arrhythmogenic risk in mitral valve prolapse), oncological radiomics , and AI-driven imaging platforms . Collaborations span the AI-hub Vita-Salute San Raffaele and the Preclinical Imaging Facility at IRCCS San Raffaele. Scientific Awards 2013 NIH Travel Grant 2018 Best Presentation at ESCR

• Advanced Imaging Techniques
• Cardiovascular Imaging
• Oncological Imaging
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Antonio Piccininni serves as an Assistant Professor in the Department of Mechanics, Mathematics & Management at Polytechnic University of Bari, Italy. His academic focus centers on advanced manufacturing technologies with particular expertise in metal forming processes, biomedical implant production, and sustainable manufacturing techniques. His research interests span multiple domains of manufacturing engineering, with emphasis on incremental forming , hydroforming , laser-assisted manufacturing , and superplastic forming techniques. Dr. Piccininni has made significant contributions to the development of customized biomedical implants, particularly cranial prostheses using titanium alloys, and has pioneered research in eco-friendly manufacturing processes including sustainable lubricants for metalworking applications. Analysis of his publication record reveals a clear progression from fundamental metal forming research toward increasingly specialized applications in biomedical engineering and sustainable manufacturing. His recent work demonstrates sophisticated integration of computational methods including machine learning, finite element analysis, and multi-criteria optimization techniques to solve complex manufacturing challenges. The trend shows growing emphasis on biomedical applications and environmental sustainability in manufacturing processes. Dr. Piccininni's research has practical applications in multiple industries including medical device manufacturing, automotive components production, and sustainable metalworking practices. His work bridges theoretical modeling with experimental validation, often incorporating advanced materials like titanium alloys and aluminum for specialized applications requiring high precision and biocompatibility.

• Advanced Manufacturing Processes
• Metal Forming Technologies
• Biomedical Implant Manufacturing
• +۵ مورد دیگر

Sabrina Giordano is Associate Professor of Statistics at the University of Calabria , where she teaches Statistics and Data Science courses in both Italian and English. She holds a PhD in Methodological Statistics from the University of Milano-Bicocca and has held visiting positions at RWTH Aachen University, University of Florida, and University of Plymouth. Her research focuses on Statistical modeling of categorical and ordinal data Latent variable models and copula functions Longitudinal analysis and hidden Markov chains Applications in finance, health, and environmental statistics Her methodological contributions are implemented in the hmmm R-package and supported by grants like the PRIN2022 project "SMILE: Statistical Modelling and Inference to Live the Environment". She serves as Associate Editor for Biometrical Journal and Statistical Methods & Applications Director of the Post-Graduate Master in Artificial Intelligence & Data Science Local coordinator for the COST Fin-AI research group Her recent collaborative work explores Dynamic response styles in longitudinal data Fairness-aware classification algorithms Copula-based dependence structures Applications to financial vulnerability and risk perception

• Categorical Data Analysis
• Longitudinal Data Analysis
• Copula Functions
• +۷ مورد دیگر

Arturo Leccadito serves as Associate Professor in the Department of Economics, Statistics and Finance (DESF) at the University of Calabria, where he has held academic positions since 2009. His current role builds on prior experience as Assistant Professor (2009-2018) and multiple visiting appointments at Cass Business School in London. As Course Director for the Master Programme in Finance and Insurance (2020-2023), he oversees advanced financial education while maintaining active research and teaching responsibilities in Financial Econometrics and Risk Management courses. Ph.D. in Computational Methods for Financial and Economic Forecasting (University of Bergamo, 2008) Marie Curie Fellow at Cass Business School (2005) Degree in Statistics and Actuarial Science (University of Calabria, 2004; 110/110 cum Laude) Leccadito's research focuses on quantitative financial modeling with specialization in risk management frameworks, commodity market dynamics, and cryptocurrency forecasting methodologies. His work integrates advanced econometric techniques with practical financial applications, particularly in covariance matrix estimation, tail risk prediction, and derivative pricing. Recent publications demonstrate increasing emphasis on sustainable finance applications including green bonds and energy markets. Analysis of his 15 most recent publications reveals strong methodological consistency in nonlinear time series modeling and extreme value theory , with expanding applications from traditional commodities to digital assets. The research portfolio shows progressive complexity from early binomial tree models to current wavelet-based geopolitical risk analysis, maintaining focus on practical risk management solutions. As member of the Metodi quantitativi per l'economia, la finanza ed il management research group, Leccadito collaborates on developing quantitative models for decision-making across finance, actuarial science, and industrial economics. His teaching portfolio spans undergraduate Probability and Financial Mathematics courses to graduate-level Financial Risk Management instruction in English.

• Financial Risk Management
• Quantitative Finance
• Econometrics
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Giuseppe Vignali is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy, specializing in Mechanical Industrial Systems (ING-IND/17). He has held this position since 2017, previously serving the Department of Industrial Engineering from 2007 to 2016. His research focuses on sustainable food industry systems, packaging technologies, and Industry 4.0 applications. Ph.D. in Industrial Engineering (2009, University of Parma) Passed State Exam for Engineering Profession (2005) Recipient of Emilia Romagna Region Scholarships (2005, 2006) His work integrates Life Cycle Assessment (LCA) , smart agriculture technologies , and supply chain resilience , with recent studies on biochar concrete , IoT irrigation systems , and digital twins for food process optimization. Publications span environmental impact analysis , mechanical properties of sustainable materials , and behavior-based safety protocols in industrial settings. Scientific achievements include national qualifications for Associate (2012) and Full Professor (2016) roles. Key awards highlight his contributions to food industry sustainability and technology innovation . He serves as Master's Course Tutor and Curriculum Advisor for Engineering for the Food Industry programs.

• Food Industry Systems
• Sustainable Packaging
• Life Cycle Assessment (LCA)
• +۴ مورد دیگر

Roberto Finesso is an Associate Professor at the Department of Energy (DENERG) at Politecnico di Torino, Italy. He is a member of the College of Mechanical, Aerospace and Automotive Engineering and contributes to the CARS@PoliTO Interdepartmental Center for Automotive Research and Sustainable Mobility. His research focuses on combustion modeling and control in diesel engines, fluid power systems, and optimization of hybrid electric vehicle layouts and control strategies. Scientific Disciplinary Sector: IIND-06/A - Fluid Machinery ERC Sectors: PE8_5 - Fluid Mechanics, Hydraulic-, Turbo-, and Piston-Engines SDG Goals: 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), 12 (Responsible Consumption and Production) His work spans combustion diagnostics , NOx and PM emission control , hybrid powertrain optimization , and machine learning for engine calibration . He employs 3D-CFD (Fluent) and 0D (Amesim) techniques for hydraulic component modeling and develops real-time control algorithms for diesel engines. His research also includes model-based calibration , pressure-based combustion control , and predictive tools for emission analysis . Recent research trends focus on integrating physics-based , semi-empirical , and neural network models for combustion control. He has pioneered techniques for offline and real-time EGR optimization , MFB50 control , and IMEP management in diesel engines. His studies address both light-duty and heavy-duty applications, emphasizing CO2 minimization and cost efficiency in hybrid vehicles. Scientific Awards: SAE Excellence in Oral Presentation Award (2013) Teaching Contributions: Course Instructor for Fundamentals of Fluid Machinery and Fundamentals of Thermal and Hydraulic Machines and Fluid Power (Mechanical Engineering, 2019–2025) Course Collaborator for Automotive Fluid Power Systems (Automotive Engineering, 2019–2025) Research Groups: FPRL Research Group (DENERG)

• Combustion Modeling
• Fluid Power
• Internal Combustion Engines
• +۳ مورد دیگر

Massimo Rundo is an Associate Professor at the Polytechnic of Turin 's Department of Energy (DENERG) and coordinator of the Fluid Power Research Laboratory (FPRL) . With over 70 Scopus-indexed publications, his work focuses on modeling, simulation, and experimental validation of fluid power systems. Research Interests: Positive displacement pumps (gerotor, gear, vane, piston) Hydraulic valves (flow forces, pressure control) Mobile hydraulics (excavators, telehandlers) CFD and lumped parameter modeling Incomplete filling phenomena at high speeds Recent Publications analyze flow dynamics in pumps, vibration-based fault detection, and energy-efficient hydraulic architectures through 1D/3D simulations. He has coordinated industry-funded projects with companies like Casappa and Pierburg Pump Technology. Honors: MDPI Energies Editor's Choice (2018) SAE Excellence in Oral Presentation (2004) SAE Certificate of Appreciation (2000) Rundo teaches graduate courses in Mobile and Industrial Fluid Power and advises master's theses, while serving as Associate Editor for the International Journal of Fluid Power and committee member for major conferences including ASME/BATH Symposium and International Maha Fluid Power Conference.

• Hydraulic Fluid Power
• Fluid Mechanics
• Positive Displacement Pumps
• +۵ مورد دیگر

Giovanni Di Franco serves as Full Professor in the Department of Social and Economic Sciences at Sapienza University of Rome's Faculty of Political Science, Sociology and Communication. He directs the book series "La cassetta degli attrezzi" (Franco Angeli Milano) and holds affiliations with CIRES at Roma Tre University and Bonanno publishing house's scientific committee. His research centers on Social Research Methodology , emphasizing data collection/analysis techniques, historical evolution of methods, qualitative-quantitative integration, and micro-macro data synthesis. Specialized in Electoral Sociology , he examines polling methodologies in digital contexts, while his work on Youth Attitudes explores values during economic crises. Additional expertise spans Europeanization Processes , Social Representations of eating disorders, and Social Cohesion frameworks. Recent publications reveal methodological innovation through machine learning applications and critical analysis of intergenerational inequality, reflecting his commitment to advancing sociological research in contemporary digital landscapes. Professor Di Franco has led significant projects including immigrant family-school interactions (2009-10), food education integration (2009), Campanian legality culture (2008), and European identity formation (2007-09), alongside European Commission-funded e-inclusion research. He supervises doctoral candidates through Sapienza's PhD program in Sociology and Applied Social Sciences. He contributes to interdisciplinary research through LARFAD laboratory and CIRES center, developing methodological tools for social analysis while directing academic publications that shape methodological discourse in Italian sociology.

• Social Research Methodology
• Electoral Sociology
• Attitudes and Values of Young People
• +۳ مورد دیگر

Alessandro Polli is a confirmed researcher (Ricercatore confermato) in Economic Statistics at the Department of Social and Economic Sciences, Sapienza University of Rome. He teaches three courses: Quantitative Methods for Time Series Analysis (1st semester), Economic Statistics (2nd semester), and Statistical Methods and Models for Time Series and Panel Data (2nd semester). His office is located in room 108 of the department, with weekly office hours held every Wednesday from 2:00 PM to 5:00 PM. Polli holds a PhD in Economic Analysis, Mathematics and Statistics of Social Phenomena from Sapienza University of Rome. His academic foundation combines rigorous statistical training with social science applications, enabling interdisciplinary research at the intersection of data science and socioeconomic analysis. His primary research focuses on Economic Statistics with specialized expertise in Time Series Analysis and Text Mining methodologies. Polli applies these techniques to examine migration patterns, gender statistics, security perception, labor market dynamics, and political discourse. His work bridges quantitative methods with social science inquiry, particularly through the analysis of textual data from social media, official statistics, and political communications to derive actionable socioeconomic insights. Analysis of his recent publications reveals a consistent trajectory in developing and applying text mining techniques to social phenomena. His research demonstrates increasing sophistication in emotional text mining, temporal validity assessment of sentiment analysis, and integration of machine learning with traditional statistical models. Key thematic clusters include migration discourse in European elections, government crisis dynamics, vaccine sentiment analysis, and automation impacts on Italian labor markets, all characterized by methodological innovation in handling complex textual datasets. Professional engagements include: Consultant to the Historical Research Office of the Bank of Italy Consultant to the Presidency of the Council of Ministers - Guarantee Commission for Statistical Information Member of the Istat quality circle for territorial statistics Referee for international scientific journals including Lexicometrica and Social Indicators Research While specific student advising activities are not documented in available sources, his institutional roles indicate significant contributions to national statistical policy and quality assurance. His collaborative work with major Italian institutions demonstrates applied research impact on official statistics and policy development, particularly in migration and territorial statistics.

• Economic Statistics
• Time Series Analysis
• Text Mining
• +۳ مورد دیگر