جست‌وجوی استاد و پژوهشگر

Silvia Rossiمشاهده پروفایل

دانشیار

• Multi-agent Systems
• Human-Robot Interaction
• Cognitive Architectures
• +۵ مورد دیگر

Silvia Rossi is an Associate Professor at the Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Italy. She co-manages the PRISCA Lab focused on Intelligent Robotics and Advanced Cognitive Systems and coordinates the national project UPA4SAR (User-centered Profiling and Adaptation for Socially Assistive Robotics). Her research emphasizes Multi-agent Systems, Human-Robot Interaction, and Cognitive Architectures. She holds an M.Sc. in Physics from the University of Naples Federico II (2001) and a Ph.D. in Information and Communication Technologies from the University of Trento (2006). Professional experience includes roles at the ITC-irst (Italy), Institute of Cybernetics E. Caianiello (CNR, Italy), and visiting research at Oregon Health and Science University (USA). Research interests span socially assistive robotics, user profiling, and emotion recognition. Key contributions include adaptive robot behaviors for healthcare and education, transparent HRI, and multi-agent systems for smart environments. She teaches courses such as Multi-agent Systems, Distributed Intelligence, and Programming I. Her work integrates AI, robotics, and human-centered design to address challenges in assistive technologies and human-robot collaboration. Publications focus on socially assistive robotics, emotion-aware systems, and transparent AI. Projects include field trials of social robots for elderly care and pediatric emotional support. Awards and recognitions are not explicitly listed but reflect her leadership in socially impactful robotics research.

Leonardo Tenoriمشاهده پروفایل

دانشیار

• Metabolomics
• Foodomics
• Nuclear Magnetic Resonance (NMR) Spectroscopy
• +۴ مورد دیگر

Leonardo Tenori is an Associate Professor at the Department of Chemistry, University of Florence, affiliated with the Magnetic Resonance Center (CERM). He holds a master’s degree in Chemistry (2002) and a PhD in Structural Biology (2008), both from the University of Florence. His research focuses on metabolomics, particularly applying Nuclear Magnetic Resonance (NMR) spectroscopy to biomedical, pharmacological, and agricultural challenges. Education: Master’s in Chemistry, University of Florence, 2002 PhD in Structural Biology, University of Florence, 2008 Research Interests include metabolomics applications in disease diagnosis (e.g., celiac disease, breast cancer, cardiovascular disorders), development of statistical algorithms for data analysis (e.g., KODAMA), and collaborations in clinical and agricultural research. His work emphasizes metabolic biomarker discovery and integrative omics approaches. Recent research highlights include studies on stroke outcome prediction via blood biomarkers, metabolomic profiling of plant-based beverages, and lipidomic analysis of human sperm. These projects underscore his expertise in NMR-based metabolomics and its translational applications. Awards: 2015 Fellowship from the Italian Foundation Veronesi for melanoma metabolomics research He has contributed to over 100 publications and collaborates internationally. His work includes developing predictive models for disease recurrence in cancer patients and exploring metabolomic signatures in chronic diseases. He also investigates applications in agriculture, such as olive oil quality assessment and dairy cow health monitoring. Labs/Teams: Part of the Magnetic Resonance Center (CERM) at the University of Florence, contributing to interdisciplinary research in structural biology and metabolomics.

• Neuroscience
• Cognitive Sciences
• Neurobiology
• +۵ مورد دیگر

Massimo De Agrò is a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) within the University of Trento, Italy. He holds a PhD in Psychological Sciences (2019) and a Master’s in Cognitive Neuroscience and Clinical Neuropsychology (2016), both from the University of Padua. His research focuses on brain simplification strategies in invertebrates, particularly jumping spiders, exploring how limited neural resources enable functional efficiency. This work aims to bridge biological insights with technological innovation. He has held postdoctoral positions at institutions including Harvard University (2020), Regensburg University (2021-2022), and the University of Florence (2022). His expertise spans neurobiology, cognitive sciences, and ethology, with a focus on visual systems, behavioral ecology, and animal cognition. Key projects include studying animacy perception in arthropods, REM-like sleep states in spiders, and developing tools like the SPiDbox for spider behavior analysis. His interdisciplinary approach integrates experimental biology, computational modeling, and robotics.

Damiano Pasettoمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Modeling
• Epidemiological Modeling
• +۲ مورد دیگر

Damiano Pasetto is Associate Professor in Numerical Analysis at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His office is located in the Alfa building of the Via Torino scientific campus. Pasetto's research develops computational methods for environmental and epidemiological systems. Core focuses include reduced-order modeling techniques applied to groundwater flow simulations and physics-informed neural networks for epidemiological forecasting. Recent publications demonstrate strong emphasis on epidemic modeling (40%), hydrological systems (33%), and computational method development (27%). COVID-19 spatial dynamics and cholera transmission mechanisms represent significant thematic clusters. He leads EPIDOC project (2021-2025) on data assimilation and optimal control for COVID-19 forecasting in Italy. Additional research includes Bayesian parameter estimation for hydrological transport models. Office hours: Tuesdays 14:00-15:00 in Alfa 314 or by appointment.

• Econometrics
• Computational Statistics
• Financial Risk Management
• +۵ مورد دیگر

Marco Bee is a Full Professor at the Department of Economics and Management, University of Trento. His expertise spans applied econometrics, computational statistics, finance, and risk modeling. He focuses on methodologies for handling heavy-tailed distributions, extreme value theory, and machine learning applications in financial risk assessment. Education details are available in his CV (CVeng.pdf). His research interests include developing statistical models for operational risk, volatility forecasting, and credit scoring, often employing mixture models, copula-based approaches, and indirect inference techniques. He has contributed significantly to the analysis of spatial econometrics and the application of extreme value theory to financial crises and insurance analytics. His recent work emphasizes tail risk estimation, with over 150 publications since 2006. Notable contributions include methodologies for Value-at-Risk (VaR) forecasting, distribution fitting for skewed data, and the use of machine learning to predict defaults in small businesses. His research bridges theoretical statistics and practical financial applications, with a focus on high-frequency data and scenario-based risk analysis. Awards and grants are not explicitly listed in the provided data, but his extensive publication record reflects recognition in quantitative finance and econometrics. He advises students on topics related to computational econometrics and risk modeling, though specific advisee names are not documented here.

• Climate Change
• Fluid Mechanics
• Hydraulic Constructions
• +۱۱ مورد دیگر

Diego Avesani is an Assistant Professor at the Department of Civil, Environmental and Mechanical Engineering, University of Trento. His expertise spans Climate Change, Fluid Mechanics, Hydrological Modeling, and Water Resources Management. He teaches courses such as 'Integrated Water Resources Management' and 'Introduction to Climate Change' within the Environmental Engineering and Territory programs. His research focuses on developing advanced hydrological models to address challenges in hydropower optimization, uncertainty analysis, and sustainable water resource management in mountainous and Alpine regions. Key research interests include elevation-driven biases in weather forecasts, integration of satellite data for hydrological modeling, and the role of machine learning in improving forecast accuracy. He has contributed to projects analyzing the Po watershed basin and Alpine regions, emphasizing the interplay between climate change and hydrological systems. His recent publications highlight work on hydropower system modeling, post-processing of seasonal forecasts using neural networks, and reducing uncertainty in large-scale hydrological simulations. These studies often address practical applications in sustainable infrastructure and climate adaptation strategies.

• Computational Biology
• Genomics
• Microbial Genomics
• +۳ مورد دیگر

Francesco Asnicar is a researcher at the Department of Cellular, Computational and Integrative Biology (CIBIO) at the University of Trento. He specializes in computational biology, genomics, and microbiome research with a focus on metagenomics and microbial genomics. His work bridges bioinformatics, data science, and biological applications, particularly in understanding host-microbiome interactions in health and disease. Teaching activities include courses such as Algorithms and Data Structures , Computational Biology , Cancer Genomics , and Scientific Programming , emphasizing Python programming and algorithmic problem-solving in biological contexts. His research spans microbial community analysis, dietary impacts on the gut microbiome, and translational applications of metagenomics in oncology and immunology. Key areas of study include microbiome-driven biomarker discovery for colorectal cancer, antibiotic resistance mechanisms in foodborne pathogens, and the role of microbial metabolites in autoimmune and metabolic disorders. He contributes to open-source tools for metagenomic analysis and collaborates on large-scale epidemiological studies linking microbiome composition to diet and cardiometabolic health. Recent work focuses on precision nutrition, fecal microbiota transplantation in cancer therapy, and computational methods for metagenomic data interpretation. Asnicar’s interdisciplinary approach integrates computational tools with experimental biology to address fundamental questions in systems microbiology and translational medicine.

• Change Management
• Emergency Management
• Innovation Management
• +۵ مورد دیگر

Loris Gaio is an Associate Professor at the Department of Economics and Management at the University of Trento. He is a member of the Quality Committee and specializes in areas such as Change Management, Emergency Management, Innovation Management, and Risk Management. His teaching activities include courses on Business Planning, Project Management, Production Management, and Quality Systems. His research focuses on optimizing organizational processes, leveraging both quantitative and qualitative methodologies. Courses like Business Plan/Analisi di Progetti and Gestione della Produzione e della Qualità emphasize practical applications of project decomposition, scheduling, and quality management. He also explores production systems' complexity and supply chain dynamics in courses like Produzione e Logistica Progredito . Dr. Gaio’s publications span topics from Total Quality Management to open content collaboration in Wikipedia. His work bridges theoretical frameworks with empirical studies in organizational resilience, lean systems, and entrepreneurship education. While no scientific awards are explicitly mentioned, his contributions reflect a commitment to advancing management practices in both academic and applied contexts. His advising and grants section remains underdeveloped in the provided data, though his teaching and research roles suggest active engagement with student projects and institutional initiatives. No specific labs or teams are highlighted, but his interdisciplinary approach integrates production systems, project management, and organizational innovation.

Marilena Fatiganteمشاهده پروفایل

پژوهشگر

• Doctor-Patient Communication
• Conversation Analysis
• Ethnography
• +۷ مورد دیگر

Marilena Fatigante is a Researcher in Social Psychology at the Department of Psychology of Developmental and Socialization Processes at Sapienza University of Rome. She specializes in qualitative research methods including ethnography and conversation analysis, focusing on medical communication (particularly oncology), educational interactions, and institutional discourse. Her work examines doctor-patient dynamics, intercultural medical encounters, and the role of companions in healthcare settings. She teaches courses on socialization processes and communication in medical contexts, and advises doctoral students in the Social Psychology program. Her research integrates conversation analysis with ethnographic methodologies to study healthcare interactions, including oncology consultations, family communication, and institutional practices. Notable contributions include analyzing treatment recommendation communication, patient involvement in medical decisions, and the impact of digital technologies on doctor-patient interaction. She collaborates internationally with institutions like UCLA and Sorbonne Nouvelle, and leads the LINC research group (Language, Interaction and Culture) at Sapienza. Recent work explores miscommunication in oncology visits, the role of companions in intercultural healthcare, and the ethical dimensions of qualitative research using audio/video recordings. Her teaching spans undergraduate and postgraduate programs in psychology, education, and healthcare management, emphasizing applied qualitative methods and discourse analysis.

• Agent-based modelling
• Artificial intelligence
• Industry 4.0
• +۱۲ مورد دیگر

Lorenzo Bottaccioli is an Associate Professor at the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) at Politecnico di Torino. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab. His academic work spans across multiple departments and degree programs, including Civil and Building Engineering, Electronic and Telecommunications Engineering, Management and Production Engineering, and Mechanical and Aerospace Engineering. University: Politecnico di Torino Department: Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) Email: lorenzo.bottaccioli@polito.it His research focuses on the intersection of artificial intelligence and sustainable urban systems. Key research interests include agent-based modelling, machine learning, multi-agent systems, smart buildings, smart cities, and smart grids. His work aligns with several UN Sustainable Development Goals (SDGs), including Affordable and Clean Energy, Industry, Innovation and Infrastructure, Sustainable Cities and Communities, and Climate Action. The recent publications highlight a consistent focus on energy systems in urban environments. His work integrates AI and simulation tools to model energy efficiency in buildings, optimize urban electric mobility, and support renewable energy communities. The research trends emphasize scalable, data-driven frameworks that combine physical models with intelligent systems. He has led multiple funded research projects, including NoBi - Not Ordinary Business Intelligence (2024–2026), DTF4EA - Digital Twin Factory for Energy Assessment (2021–2022), and BDFA - Building Digitalization for Energy Assessment (2020–2021). He is also currently managing a commercial research project on LLMs for text-to-SQL applications. Scientific Projects: NoBi - Not Ordinary Business Intelligence (2024–2026) DTF4EA - Digital Twin Factory for Energy Assessment (2021–2022) BDFA - Building Digitalization for Energy Assessment (2020–2021) LLMs for text-to-SQL (2024–2025) Lorenzo Bottaccioli supervises PhD students in the field of Computer Science and Systems Engineering. His advisees include Maria Adelaide Loffa, whose research focuses on reinforcement learning for multi-scale energy management, and Andrea Bellagarda, who investigated IoT technologies for manufacturing processes. He teaches courses across various programs, including PhD-level instruction on IoT platforms for spatial analytics in smart energy systems, as well as undergraduate and master’s level courses in ICT for smart societies, building design, and computer science. He is actively involved in interdisciplinary research through the Energy Center Lab (Ec-L), where he contributes to integrating digital technologies into energy and urban planning systems. His role spans teaching, research leadership, and cross-departmental collaboration, positioning him as a key figure in sustainable smart city development at Politecnico di Torino.

Mariano Galloمشاهده پروفایل

استاد

• Transportation Engineering
• Sustainable Mobility
• Traffic Modeling
• +۷ مورد دیگر

Mariano Gallo is a Full Professor of Transportation Engineering (Professore Ordinario) at the Department of Engineering (DING) of the University of Sannio in Benevento, Italy. His academic work focuses on transportation systems engineering with a particular emphasis on sustainable mobility solutions, traffic modeling, and urban transportation planning. He teaches courses including Transportation System Engineering, Rail, Air and Sea Infrastructures and Transportation, and Sustainable Mobility and Transport Planning for Civil Engineering students. Professor Gallo's research interests span a wide range of transportation topics including sustainable mobility systems, energy-efficient transportation solutions, traffic simulation modeling, autonomous vehicle integration, hydrogen-powered freight transportation, and public transit optimization. His work demonstrates a strong commitment to developing solutions for contemporary transportation challenges with environmental sustainability at the forefront. Recent research has focused on innovative approaches to decarbonizing freight transport, optimizing traffic management systems, and analyzing the impacts of transportation infrastructure on urban development. His publication record shows a clear evolution toward sustainable and intelligent transportation systems, with recent work emphasizing hydrogen infrastructure for freight vehicles, energy-efficient driving profiles, autonomous vehicle integration, and sustainable mobility policies. His research combines theoretical modeling with practical applications, often focusing on regional contexts in Italy while addressing global transportation challenges. Professor Gallo maintains an active research agenda with numerous publications in high-impact journals across transportation engineering, sustainability, and urban planning fields. His work frequently appears in journals such as Transportation Research, Sustainable Energy Technologies and Assessments, and Energies, reflecting his interdisciplinary approach to transportation challenges.

Valeria D'Amatoمشاهده پروفایل

دانشیار

• Actuarial Science
• Risk Management
• Machine Learning
• +۷ مورد دیگر

Valeria D'Amato is an Associate Professor at the Department of Methods and Models for Economy, Territory, and Finance of Sapienza University of Rome . Her academic work focuses on actuarial science, risk management, and statistical modeling. Fields of Interest : Actuarial Science, Risk Management, Machine Learning, Climate Risk, Financial Mathematics, Demographic Economics Email : valeria.damato@uniroma1.it Recent research highlights include: Developing machine learning models to assess climate risk sharing in tourism insurance and ESG score prediction. Analyzing frailty-based mortality models for longevity risk reserving and pandemic-related mortality shocks. Investigating cyber risk dependence using vine copula frameworks and geospatial pricing strategies. Her publications span journals like Frontiers in Applied Mathematics and Statistics , Applied Stochastic Models in Business and Industry , and Risks , reflecting interdisciplinary approaches to modern risk assessment challenges.

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

Domenico Vitaleمشاهده پروفایل

پژوهشگر

• Environmental Statistics
• Data Analysis
• Machine Learning
• +۳ مورد دیگر

Domenico Vitale is a Researcher at the Department of Methods and Models for Economy, Territory, and Finance within Sapienza University of Rome . His work spans environmental statistics, economic modeling, and data science. He teaches courses including Time Series Analysis (6 CFU), Introduction to Spatial Data (3 CFU), and Basic Statistics for Business Sciences. Office hours are held in Room 420, 4th Floor, Faculty of Economics. Research Focus : Eddy covariance data processing and quality control Statistical modeling of environmental and economic phenomena Machine learning applications for survey data and flux measurements Time series alignment and prewhitening techniques Recent Publications explore topics like quasi-formal employment modeling, carbon flux scaling frameworks, and pandemic dynamics. His work integrates interdisciplinary methodologies from climate science to econometrics. Labs and Collaboration : Active in DidaLab , Spinelli Lab , and ICOS Ecosystem Stations , focusing on standardized environmental data processing.

• Global Optimization
• Operations Research
• Computational Mathematics
• +۳ مورد دیگر

Marco Locatelli is a Full Professor in the Department of Computer Engineering at the University of Parma, Italy. His research focuses on Global Optimization , Operations Research , and Computational Mathematics , with applications in Robotics and Optimization Algorithms . Education: Ph.D. in Computational Mathematics and Operations Research (University of Milano/Napoli, 1997); Laurea in Computer Science (University of Milano, 1992). Positions: Post-Doc (University of Trier, Firenze); Assistant/Associate Professor (University of Torino); Full Professor (University of Parma since 2010). His research spans Global Optimization over intervals, multistart algorithms, concave optimization, packing problems, and convex underestimators. Recent work includes Multi-Agent Pathfinding and Speed Planning with energy/time optimization, leveraging Machine Learning and Dynamic Programming . He has received distinguished accolades, including the Europt Fellow (2018), Best Paper in the Journal of Global Optimization (2016), and co-authored a SIAM-published book on Global Optimization (2013). He serves on editorial boards of top-tier journals like Computational Optimization and Applications and Operations Research Letters . Scientific Awards: Al Zimmermann's Programming Contest Winner (Circle Packing) AIRO Award for Daniele Depetrini's Master Thesis (2007) Chairman's Recognition of Outstanding Paper (2009 IEEE Congress) Europt Fellow (2018) His teaching includes Operations Research and Algorithms for Decision Support across multiple Italian universities (Torino, Parma, Firenze). He has contributed to Research Projects like MoSPRAS, MOST, and COSO, addressing real-world optimization challenges in healthcare, robotics, and transportation.