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Andrea MaurinoView profile

Professor

• Data Science
• Knowledge Graphs
• Machine Learning
• +5 more

Andrea Maurino is a Full Professor at the University of Milano-Bicocca and leads the Insid&s LAB. His research focuses on data quality, knowledge graphs, machine learning, and their applications in healthcare, finance, urban planning, and organizational analysis. He explores cutting-edge techniques like Large Language Models (LLMs) for decision support systems and semantic annotation of tabular data. Key research interests include improving data quality frameworks for large RDF datasets, developing enterprise knowledge graphs for organizational insights, and applying AI to social media analysis and hate speech detection. His work bridges theoretical advancements with real-world applications such as smart city mobility prediction and nutritional strategies for healthy aging. Notable contributions include scalable tools like ABSTAT-HD for knowledge graph profiling and the 3d-clost mobility prediction model. Maurino’s interdisciplinary approach integrates data science with fields like psychology (ICD-11 decision support) and environmental science (ESG activity detection in financial texts). His lab collaborates on projects like Food NET, combining nutrition science with social network analysis. While no formal awards are listed here, his prolific publication record reflects sustained innovation in data-driven methodologies.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

Alfredo BensoView profile

Professor

• Bioinformatics
• Systems Biology
• Artificial Intelligence
• +16 more

Alfredo Benso is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. He has been continuously involved in the Doctoral College for Computer and Systems Engineering since 2008, guiding PhD education across numerous cycles. His academic roles span teaching and research leadership in bioinformatics, systems biology, and computer engineering. Research interests: Bioinformatics and computational biology Systems biology and gene regulatory networks Artificial intelligence and machine learning in biomedicine Molecular modeling and multiscale simulation Biological database systems and data integration Health informatics and public health modeling His recent publications reflect a strong trend in applying AI and machine learning to biological and medical challenges, including protein function prediction, Alzheimer’s and Multiple Sclerosis modeling, food fraud detection, and viral genome analysis. These works span journals and conferences in bioinformatics, computational biology, and biomedical engineering, demonstrating interdisciplinary innovation. Scientific Awards: BIOINFORMATICS 2014 BEST PAPER AWARD (INSTICC, United Kingdom) Advising and Grants: He supervises PhD students, including Sofia Ostellino, and leads major research initiatives such as the BIGMECH (2023–2025) and FISHUB (2016–2018) projects. He has served as Scientific Director for collaborative agreements with public administrations and as Principal Investigator on EU, national (PRIN), and regional research grants focused on bioinformatics, space systems, and reliable digital technologies. Labs and Teams: He is a key member of the SBG - System Biology Group (DAUIN) and contributes to PolitoBIOMed Lab, fostering collaborative research in biomedical engineering and computational biology.

• Human-Computer Interaction
• Human-Centered Design
• End-User Development
• +5 more

Antonio Piccinno is an Associate Professor at the Department of Computer Science , University of Bari Aldo Moro (UNIBA). He is a founding member and coordinator of the IVU (Interaction, Visualization, Usability & UX) Laboratory , focusing on the interplay between Human-Computer Interaction (HCI) and Software Engineering through End-User Development (EUD) techniques. His career spans roles as Research Fellow , Assistant Professor , and now Associate Professor , with a Ph.D. in Computer Science from UNIBA (2005). His research interests include Human-Centered Design (HCD) , End-User Development (EUD) , Adaptive Interfaces , Secure Software Design , and Ambient Intelligence . He has authored over 150 publications and contributed to EU-funded projects like EUD-Net , FairsNet , and FAIRWIS . His work bridges academia and industry through collaborations with international institutions such as the University of Colorado at Boulder. His recent publications highlight trends in Human-AI Symbiosis , IoT for Cultural Heritage , and Security Education . He actively organizes conferences, including roles as Full Papers Co-Chair at INTERACT 2023/2021 and Founder/Co-Chair of workshops like CoPDA and PDEUD . He serves on the Steering Committee of IS-EUD and is a member of ACM , IEEE , and ACM SIGCHI . At UNIBA, he teaches Software Engineering (undergraduate) and Cloud Computing (graduate). He has supervised ~70 bachelor’s/master’s theses and one PhD student. The IVU Lab he co-founded investigates adaptive systems, ambient intelligence, and secure software design.

• Asymptotic statistics
• Bayesian inference
• Biostatistics
• +9 more

Enrico Bibbona is an Associate Professor at the Department of Mathematical Sciences (DISMA) at Politecnico di Torino, where he contributes to the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic profile spans statistics, mathematical modeling, and data science applications across multiple engineering disciplines. His primary research interests include: Asymptotic statistics Bayesian inference Biostatistics Industrial statistics Mathematical biology and modeling in life sciences Statistical inference for stochastic processes Stochastic modeling His specific research lines focus on Reaction Networks approximation and inference, Statistical modeling of animal calls, Statistics and modeling for the SmartPad system (in collaboration with ITT), and Statistics for deterministic and stochastic dynamical models. His work bridges theoretical statistics with practical applications across various scientific domains, particularly in biological systems, industrial applications, and data science. His notable recognition includes the Elsevier/SPA Travel Award presented by Elsevier and Bernoulli Society, Denmark (2011). Professor Bibbona actively mentors PhD students across multiple programs, currently supervising Daniele Poggio and Saeed Sani in Mathematical Sciences, and has previously guided Elena Sabbioni (thesis: Statistical Methods for Complex Biological and Clinical Data) and Hiu Ching Yip (thesis: Statistical models for understanding biomedical data). His teaching spans Mathematical Engineering, Data Science and Engineering, and Communications Engineering programs, where he teaches courses including BioQuants, Statistical methods in data science, and Project: Software-defined communication systems. He is a key member of the Statistics and Data Science research group at DISMA and has contributed to practical applications through patents including "BRAKING SYSTEM TEMPERATURE ESTIMATOR" and "A method for estimating the wear of a vehicle brake element," demonstrating his ability to translate theoretical work into real-world solutions.

• Dependability
• Stochastic Modeling
• Cloud Computing
• +6 more

Paulo Romero Martins Maciel is a full professor at the Center for Informatics, Federal University of Pernambuco (UFPE), Brazil, where he has been a member since 2001. He previously served as a professor in the Department of Electrical Engineering at the University of Pernambuco from 1989 to 2003. He completed his graduate degree in electronic engineering from the University of Pernambuco in 1987, followed by master's and doctorate degrees in electronic engineering and computer science from UFPE. During his doctoral studies, he conducted a "sandwich internship" at Eberhard-Karls-Universität Tübingen, Germany (1996-1997), and later spent a sabbatical year at Duke University's Edmund T. Pratt School of Engineering. Graduate Degree: Electronic Engineering, University of Pernambuco (1987) Master's Degree: Electronic Engineering and Computer Science, Federal University of Pernambuco Doctorate: Electronic Engineering and Computer Science, Federal University of Pernambuco Professor Maciel leads the MoDCS Research Group and has established himself as a leading expert in dependability engineering, stochastic modeling, and performance evaluation of computing systems. His research spans cloud computing, edge computing, software aging and rejuvenation, and reliability analysis of complex systems. His work demonstrates a consistent focus on developing mathematical models (particularly Markov chains and Petri nets) to evaluate system behavior under various conditions. Recent research extends into emerging areas including Metaverse infrastructure, drone surveillance systems, satellite communications, and smart agriculture applications. His publication record is impressive with 191 publications and 1,298 citations, showing remarkable productivity and impact across nearly three decades. His most recent work (2024-2025) continues to push boundaries in aging dependability of cloud-edge systems for digital twins, high availability quantification in Metaverse storage, and stochastic modeling of satellite systems. His research methodology consistently applies hierarchical modeling approaches to address complex system interdependencies. Professor Maciel has mentored numerous researchers who have become active contributors in the field, including doctoral students Paulo Pereira and several co-authors who appear in his recent publications. His collaborative network extends across Brazil and internationally, reflecting the global recognition of his expertise.

• Requirements Engineering
• Web Engineering
• Artificial Intelligence
• +9 more

Luisa Mich is an Associate Professor in the Department of Industrial Engineering at the University of Trento, Italy, holding this position since 2002 after progressing from Research Fellow (1983-1987) to Researcher (1988-2001). She teaches Enterprise Information Systems, Tourism Information Systems, and Web Strategies across multiple departments including Economics, Humanities, and Mathematics, while pioneering ICT integration at the university since 1989. Education: PhD in Physics, University of Trento (1983), thesis: "Theory and experimentation on interaction in human systems" Scientific High School Diploma, Marcelline Institute, Bolzano (1976) Her research centers on Requirements Engineering innovations including the award-winning 7Loci meta-model for web presence strategy and enhanced creativity techniques surpassing traditional brainstorming. Current work integrates Natural Language Processing with legal document analysis and web reputation monitoring, demonstrating strong interdisciplinary connections between computer science, tourism management, and semantic technologies. Recent publications (2022-2025) reveal a decisive shift toward AI-driven business process development and tourism applications, with semantic technologies bridging legal compliance and requirements engineering. Key trends include agentic AI systems, optimized creativity techniques for requirements elicitation, and ontology-based personalization frameworks – all addressing practical challenges in destination management and customer experience. Scientific Recognition: While lacking major prizes, her expertise is validated through editorial board roles (Journal of Information Technology & Tourism, Journal of e-Learning) and leadership in professional societies including ACM, IEEE, and IFITT. Advising and Grants: Mich has supervised approximately 100 theses across scientific and humanities disciplines, including doctoral programs in Information Technology and Materials Science. Her grant leadership includes the European WEE-NET project (2005-2008) establishing Web Engineering networks and Papyrus (2008-2010) for cultural digital libraries, alongside consultancy for tourism boards like Suedtirol and Visit Trentino. Research Infrastructure: She co-founded Trento's Department of Information and Communication Technology and directed the Computer Science and Organisations program. Her ECDL certification initiative (1998-2010) became Italy's first university-adopted ICT certification, while her "ICT and tourism" research group drives destination management innovations through the Trentino Tourism System.

• Modeling & Simulation
• Production Systems Design
• Supply Chain Management
• +7 more

Francesco Longo is an Assistant Professor at the Department of Mechanical, Energy and Management Engineering , University of Calabria, Italy. He serves as Director and Scientific Responsible of the Modeling & Simulation Center – Laboratory of Enterprise Solutions (MSC-LES), the sole Italian member of the McLeod Modeling & Simulation Network (MS&Net). His academic career spans teaching 'Design of Production Systems' and 'Industrial Plant Laboratory' since 2005, with visiting researcher roles at Rutgers University and University of Genoa. Education: PhD in Mechanical Engineering (2006), Summa Cum Laude Degree in Mechanical Engineering (2002) Research Focus: Modeling & Simulation applied to production systems, supply chain security, digital twins, and human-robot collaboration His recent publications (2024-2025) demonstrate interdisciplinary work across digital twin technology (for food industry, human-robot workspaces), human-centered design (smart operators, wearable AR solutions), and sustainability (ESG impacts, energy efficiency). He has served as Editorial Board Member for Simulation journals and General Chair at multiple European Modeling & Simulation Symposia. Scientific Recognition: MISS Certificate of Appreciation, ICAMES 2008 First Runner Up Technical Expertise: HLA/RTI Standards, 3D Real-Time Simulation, Industry 4.0/5.0 Tools As MSc course tutor since 2002 and conference organizer (22nd EMSS, MAS 2010), he bridges academic research with industrial applications through partnerships with Rutgers University , Kennedy Space Center/NASA , and University of Ottawa . His lab develops simulation tools for maritime security and ergonomic workstation design.

Roberto BruniView profile

Professor

• Operational Semantic
• Petri Nets
• Reaction Systems
• +5 more

Roberto Bruni is a Professor in the Department of Computer Science at the University of Pisa, Italy. He has been actively contributing to theoretical computer science with a focus on formal methods, operational semantics, and reaction systems. His academic service includes serving as PC member for SAS 2025 and CMSB 2025 conferences. Professor Bruni's research interests span Operational Semantic, Petri Nets, Reaction Systems, Graph Transformation, and Monoidal Category. His work bridges theoretical computer science with applications in computational biology and program analysis. He has developed significant tools like BioReSolve, a Prolog interpreter for Reaction Systems analysis, and has contributed to projects such as RemConf (Confusion removal in Petri nets) and The Link Calculus. His recent publications demonstrate a strong focus on reaction systems, program analysis, and formal verification methods. The research trend shows increasing application of theoretical computer science concepts to biological modeling and healthcare applications, particularly through enhanced reaction systems frameworks. Professor Bruni holds several institutional responsibilities including membership in the Commission Didattica del GRIN since 2020, delegation to the University Information System (SIA) for the Department of Computer Science since 2020, and membership in the Doctoral College for Smart Industry since 2018. He has been Vice-president of the Master's Degree in Data Science and Business Informatics at the University of Pisa since 2017. His teaching portfolio is extensive, covering courses in programming, algorithms, quantum computing, business process modeling, and program analysis for both undergraduate and graduate students in multiple languages (Italian and English). He has taught courses such as Programmazione e Algoritimica, Models for Programming Paradigms, Business Processes Modeling, Program Analysis, and Introduction to Quantum Computing. Professor Bruni has been involved in numerous research projects including PRA (FM4HD, DECLWARE, FOG), PRIN (ASPRA, CINA, IPODS), and several EU-funded projects (IST-FP7 ASCENS, IST-FP6 SENSORIA, CoMeta, AGILE, NAPI, GETGRATS). His previous academic positions include PhD Student at University of Pisa, International Fellow at SRI Computer Science Laboratory, and Visiting Scholar at UIUC.

• Explainable AI
• Personal Data Mining
• Clustering
• +7 more

Riccardo Guidotti is an Assistant Professor (RTD-B) at the Department of Computer Science, University of Pisa, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa, Italy. His research spans explainable AI, personal data mining, clustering, and transactional data analysis. PhD in Computer Science, University of Pisa Master's Degree in Computer Science, University of Pisa (110/110 cum laude) Bachelor's Degree in Computer Science, University of Pisa (110/110 cum laude) His research interests include Explainable AI , Personal Data Mining , Clustering , and Transactional Data Analysis . He focuses on making AI systems interpretable and understandable, especially through local and global explanation techniques applied to behavioral and mobility data. His work bridges theoretical models with real-world applications in retail, mobility, and social networks. The recent publications highlight a strong trend in explainability of black-box models , with emphasis on rule-based and exemplar-driven methods. His work extends to temporal pattern discovery , privacy risk assessment , and software engineering analytics , demonstrating interdisciplinary reach across AI, data science, and human-centric systems. Scientific awards received: IBM Fellowship Award (2014) ISTI Young Researcher Award (2016, 2017, 2018, 2019) Next Generation Data Scientist Award (NGDS 2018) He has contributed to teaching courses such as Data Mining, Logic Programming, and Advanced Data Mining topics. He has been involved in major EU-funded projects including SoBigData++, XAI, AI4EU, and HumanE-AI-Net, where he contributes to advancing explainable and ethical AI. He collaborates extensively within the KDDLab, focusing on knowledge discovery and data-driven decision-making. His lab work emphasizes scalable, interpretable, and socially aware data mining systems.

Milli LetiziaView profile

Assistant Professor

• Data Mining
• Complex Networks
• Diffusion of Innovation
• +7 more

Milli Letizia is an Assistant Professor in the Department of Computer Science at the University of Pisa, Italy. She is a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group connecting the University of Pisa, CNR-ISTI, and Scuola Normale Superiore. Her work bridges theoretical and applied aspects of network science, data mining, and computational social science. Education: PhD in Computer Science, University of Pisa (2018) Master Degree in Computer Science, University of Pisa, magna cum laude (110/110 cum laude, 2013) Bachelor Degree in Mathematics, University of Pisa (2010) Her research focuses on data mining , complex networks , diffusion of innovation , quantification , and the science of success . She investigates how information, behaviors, and diseases spread across networks, using both data-driven and simulation-based approaches. Her work integrates machine learning, network modeling, and social theory to understand spreading phenomena in real-world systems. The trend in her recent publications reveals a strong emphasis on modeling diffusion processes in complex networks, with a focus on algorithmic bias, community-aware diffusion, and opinion dynamics. She has developed influential open-source tools such as NDlib and CDLIB , which are widely used in network science for simulating diffusion and detecting communities. Her work spans disciplines including computer science, public health, and social science, demonstrating interdisciplinary impact. Scientific Service and Recognition: Program Committee Chair, 3rd and 4th International Workshop on Dynamics in Networks (DyNo) at PKDD 2017 and ASONAM 2018 Program Committee Member, NetSciX 2019, DATA ANALYTICS 2017–2019, GOODTECHS 2017, DataMod 2018 Member of Local Organizing Committee, XIII AI*IA Symposium on Artificial Intelligence (2014) She has contributed to major EU projects including SoBigData++ , HumanE-AI-Net , SBD@RT , and CIMPLEX . Although no formal advising or grant leadership is explicitly mentioned, her active research output and tool development suggest significant involvement in funded research and potential mentorship. She has taught courses on data mining, big data analytics, and databases at both undergraduate and master’s levels. Milli Letizia is affiliated with the Knowledge Discovery and Data Mining Laboratory (KDDLab) , a leading interdisciplinary research group focused on big data analytics, social mining, and network science. The lab fosters collaboration between academia and research institutions, promoting innovation in data-driven societal applications.

• Economic History
• Welfare Measurement
• Poverty Analysis
• +4 more

Professor Giovanni Vecchi is a leading academic in Economic History at the University of Rome Tor Vergata. His primary research focuses on welfare measurement, poverty analysis, and inequality dynamics. He specializes in historical and contemporary studies using household budget data, spatial cost-of-living analysis, and composite indices evaluation. Vecchi teaches courses on economic inequality and poverty at the undergraduate level, contributing to both bachelor's and master's programs. His work spans Italy, Bhutan, Madagascar, and Iraq, addressing poverty dynamics, cost-of-living disparities, and policy impacts. Notable contributions include developing the OUTDETECT Stata module for outlier detection and pioneering methodologies for setting poverty lines in diverse contexts. Vecchi's research emphasizes methodological rigor, bridging historical analysis with contemporary policy challenges. Publications highlight interdisciplinary approaches, integrating econometrics with economic history. His studies on Italy’s long-term economic growth (1861–2011) and child labor patterns demonstrate deep engagement with national socioeconomic trends. Despite no explicit mentions of awards, his work has shaped academic discourse on poverty measurement and spatial inequality.

• Static Analysis
• Software Engineering
• Program Verification
• +4 more

Peter FERRARA is an Associate Professor in Computer Science at Ca' Foscari University of Venice, Italy. He joined the university in November 2019 as a tenure-track assistant professor and transitioned to his current rank. His primary affiliation is with the Department of Environmental Sciences, Computer Science and Statistics (D.A.I.S.) and the Research Institute for Complexity. He is part of the Software and System Verification group and maintains an active role in the Temporary Center Innovation Ecosystem Project. Education: Holds a PhD in Computer Science (2009) from École Polytechnique (Paris) and Ca' Foscari University of Venice, advised by Radhia Cousot and Agostino Cortesi. Completed his MA (2005) and BA (2003) in Computer Science from Ca' Foscari. Defended his thesis at École Normale Supérieure in May 2009. His research experience spans 15 years, with 50+ publications and 20 patents, focusing on static analysis for software reliability and security. Research Interests: Specializes in applying abstract interpretation theories to enhance software security, reliability, and performance. Recent work emphasizes static analysis of mobile and .NET systems, blockchain smart contracts, IIoT vulnerabilities, and automated policy enforcement. He bridges formal methods with practical software development, addressing challenges in precision-efficiency tradeoffs and industrial tool adoption. Industry Experience: From 2013-2019, worked in industry as Head of R&D at JuliaSoft SRL (2016–2019) and IBM Research (2013–2015). Developed commercial static analysis tools, managed research projects, and delivered technical presentations to clients. His work focused on transitioning research into deployable solutions. Teaching: Instructs courses in Object-Oriented Programming, Software Architectures, and Coding/Data Management. Engages students through practical LiSA framework experiences. Has taught at ETH Zurich (2009–2013) and supervised over a dozen students across PhD, Master's, and Bachelor's levels. Awards: No specific scientific awards listed, but recognized through extensive patent portfolio and impactful industry-academia collaborations. Holds 20 patents, including innovations in permissions extraction, privacy enforcement, and malware detection. Grants & Projects: Involved in multiple research projects from proposal to execution, including EU Data Act analysis, IoT security frameworks, and blockchain verification initiatives. Collaborates with institutions like IBM, Microsoft Research, and the University of Verona's JuliaSoft spin-off. Labs/Teams: Active in the Software and System Verification group at Ca' Foscari. Co-developed the LiSA static analysis framework. Engages in interdisciplinary projects combining robotics, IoT, and blockchain domains.

Marina MongielloView profile

Associate Professor

• Blockchain Technology
• IoT Systems
• Software Engineering
• +3 more

Marina Mongiello is Associate Professor at Polytechnic University of Bari focusing on blockchain applications, IoT security, and distributed software systems. Her work integrates formal methods with practical solutions for cybersecurity challenges. Research Focus: Develops secure blockchain architectures for supply chain traceability, IoT security frameworks, and intrusion detection systems combining formal modeling with machine learning approaches. Technical Innovations: Designs hybrid systems using Petri nets and LSTM networks for enterprise security, and biometric-based cryptographic solutions for blockchain platforms. Application Domains: Implements traceability solutions for agri-food supply chains, secure data transmission frameworks, and decentralized alternatives to cloud computing infrastructures.

Nicola SalvatiView profile

Associate Professor

• Survey Sampling
• Model-Assisted Inference
• Robust Regression
• +4 more

Nicola Salvati is an Associate Professor in Statistics at the University of Pisa's Department of Economics and Management. He holds a PhD in Applied Statistics from the University of Florence (2004). His research focuses on survey sampling, model-assisted inference, robust regression, quantile and M-quantile regression, spatial statistics, and applications to poverty mapping and Big Data. He contributes to interdisciplinary studies bridging statistics with socio-economic analysis and environmental science. Research interests include developing methodologies for small area estimation, handling linked data, and addressing challenges in causal inference. His work is supported by grants from the European Union and Italy's Ministry of Research. He is a Fellow of the Italian Statistical Society and affiliated with the International Business and Human Rights focus area. Key publications emphasize temporal M-quantile models, causal small area estimation, and applications of statistical methods in air quality and healthcare analysis. His articles frequently address policy-relevant topics like poverty measurement, unemployment, and enterprise sustainability. Advances in his work include methodologies for bias correction, spatial semiparametric modeling, and robust regression techniques. He collaborates on projects involving hedonic pricing, nursing home performance evaluation, and environmental exposure assessment.