جست‌وجوی استاد و پژوهشگر

Michelangelo Ceciمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Relational Data Mining
• +۵ مورد دیگر

Michelangelo Ceci is a full professor at the Department of Computer Science, University of Bari, Italy. His academic career spans over two decades with significant contributions to data mining and machine learning research. He has established himself as a leading figure in the European data mining community through his extensive publication record and leadership roles in major conferences. Dr. Ceci's primary research interests focus on data mining and machine learning, with particular emphasis on multi-relational data mining, text mining, spatial data mining, spatio-temporal data mining, and semi-supervised/transductive learning. His work bridges theoretical foundations with practical applications across diverse domains including social network analysis, financial technology, healthcare informatics, and environmental monitoring. His research demonstrates a consistent trajectory toward increasingly complex data structures and more sophisticated modeling techniques. The most recent publications reveal several key trends: a growing focus on explainable AI systems, increased application of graph-based methods for spatio-temporal data, expansion into biomedical applications particularly in microbiome analysis, and development of robust methods for anomaly detection in cryptocurrency and social networks. His work increasingly integrates multiple data sources and perspectives through multi-view learning approaches. Dr. Ceci has served on the program committees of major international conferences including IEEE ICDM, IJCAI, ECMLPKDD, SIAM SDM, ECAI, ISMIS, PAKDD, DEXA, and ACM SAC. He has held editorial positions with journals such as IJSNM, IJDATS, and Journal on Advances in Intelligent Systems, and has been actively involved in organizing conference tracks and workshops. His research has been supported through significant projects including serving as national coordinator of FP7612944 MAESTRA and coordinator of a research unit in the PONREC project Vi-POC. He has participated in numerous national (PRIN-COFIN 2001, 2009) and international research projects (IST-1999-20882: COLLATE). Dr. Ceci has also contributed to the academic community through mentoring PhD students and early-career researchers, though specific names of advisees are not documented in the available information.

Luca Teseiمشاهده پروفایل

دانشیار

• Computational Structural Bioinformatics
• RNA Secondary Structure Analysis
• Topological Data Analysis
• +۵ مورد دیگر

Luca Tesei is an Associate Professor in the Department of Computer Science at the University of Camerino's School of Science and Technology, Italy. His research focuses on computational structural bioinformatics, particularly RNA secondary structures and their functional implications. Current Projects: Principal Investigator of PRIN 2022 PNRR Project RNA2Fun (RNA Secondary Structures & Function in non-coding RNAs) Editorial Roles: Guest Editor for BMC Bioinformatics' RNA Prediction Collection; Editorial Board Member of BMC Bioinformatics Luca's work integrates topological data analysis with biological systems modeling, spanning RNA structure alignment, pseudoknot detection, and machine learning applications in biomedical fields. He has received the Italian National Scientific Qualification as Associate Professor in INF/01 (Computer Science) and ING-INF/05 (Information Systems Engineering) in 2017. Recent Trends: His publications emphasize RNA computational modeling (2016-2025), with growing integration of topological methods (persistent homology, entropy analysis) and machine learning techniques for structural comparisons and biomedical applications like seizure detection and stock market modeling. Scientific Awards: Italian National Scientific Qualification as Associate Professor (2017)

Roberto Di Mariمشاهده پروفایل

استادیار

• Model-Based Clustering
• Finite Mixture Models
• Latent Variable Models
• +۴ مورد دیگر

Roberto Di Mari is an Assistant Professor at the Department of Economics and Business , University of Catania, Italy. His research focuses on advanced statistical methodologies, particularly model-based clustering, finite mixture models, latent variable models, and latent Markov models. He holds a Ph.D. in Economics and Finance from the University of Rome Tor Vergata (2017) and has conducted research at Tilburg University (Netherlands) as a visiting scholar. Education: Ph.D. in Economics and Finance (2017), University of Rome Tor Vergata M.Sc. in Economics (2013), University of Rome Tor Vergata Bachelor's Degree in Economics (2011), University of Catania Erasmus Exchange, University of Lille 1 (France), 2009–2010 Research Interests: Di Mari specializes in developing and applying statistical models to analyze complex data structures. His work bridges methodological advancements in latent variable modeling (e.g., mixture models, latent Markov approaches) with applications in economics, business, and social sciences. Key themes include robustness in modeling outlier risk, multilevel latent class analysis, and measurement invariance across longitudinal studies. Publications: His research emphasizes methodological contributions to latent variable modeling, including advancements in bias correction, R package development (e.g., multilevLCA ), and applications to high-frequency financial data and SME risk assessment. His work is published in journals like Journal of Econometrics , Structural Equation Modeling , and International Journal of Approximate Reasoning . Grants & Collaborations: Di Mari’s academic trajectory includes collaborations with Tilburg University and the Einaudi Institute for Economics and Finance. His research often integrates computational tools (e.g., LASSO penalization, dynamic mixtures) to address real-world challenges in data analysis.

Marta Nai Rusconeمشاهده پروفایل

دانشیار

• Statistics
• Data Analysis
• Environmental Economics
• +۳ مورد دیگر

Marta Nai Ruscone is an Associate Professor in the Department of Economics at the University of Genoa. She specializes in statistical methodologies, particularly focusing on copula models, data analysis, and their applications in environmental economics and econometrics. Her teaching responsibilities include courses such as Advanced Data Analytics, Statistical Forecasting, and Statistics for Business Economics. Her research emphasizes developing and applying copula-based techniques for clustering, dependence analysis, and modeling complex economic and environmental datasets. Key areas include studying the influence of economic sectors on financial markets, analyzing the dynamics of human development indicators, and exploring relationships between environmental factors and pandemic outcomes. She has also contributed to open-source statistical software like the R package OBsMD for Bayesian model discrimination. Dr. Nai Ruscone’s work bridges theoretical statistical advancements with practical applications, addressing challenges in multivariate analysis, ordinal data modeling, and interdisciplinary problem-solving. Her publications reflect a strong commitment to advancing methodologies in statistics and their relevance to real-world economic and environmental issues.

Andrea Giachettiمشاهده پروفایل

پژوهشگر

• Structural Biology
• Metals in Biology
• NMR Methods
• +۵ مورد دیگر

Andrea Giachetti is a Researcher affiliated with the Center for Magnetic Resonance (CERM) and the Center for Integrated Research on Metalloproteins and Magnetic Resonance (CIRMMP) at the University of Florence, Italy. His research focuses on structural biology, metals in biological systems, and advanced NMR methodologies. He specializes in investigating the structural and dynamic properties of metalloproteins, including ferritin, zinc transporters, and amyloid-β peptides, with applications in drug delivery and disease mechanisms. Giachetti’s work integrates experimental techniques like NMR spectroscopy with computational methods such as molecular dynamics simulations and machine learning. He has contributed to developing protocols for protein structure determination and refining computational models of oligomeric systems. His recent projects explore the role of metals in neurodegenerative diseases and the optimization of NMR infrastructure for cloud-based structural biology platforms. His research also extends to collaborative initiatives like WeNMR-EOSC and West-Life, which aim to advance computational infrastructure for structural biology. Despite no listed awards, his contributions to metalloprotein dynamics and computational protocols are widely recognized in the field. Giachetti’s affiliations with CERM and CIRMMP position him at the forefront of interdisciplinary research in structural biology and metallomics.

Isadora ANTONIANO VILLALOBOSمشاهده پروفایل

دانشیار

• Statistics
• Probability Theory
• Bayesian Inference
• +۵ مورد دیگر

Isadora Antoniano Villalobos is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. Her research focuses on statistical models and methods for complex data structures, particularly Bayesian inference for intractable models, extreme value theory, and risk analysis. She leads projects such as RISE (Rethinking and Innovating Statistics for Extremes) and SMARTMUS-E, addressing environmental and energy challenges. Her teaching spans courses like Applied Probability and Statistics for Empirical Research in Management across various degree programs. She collaborates internationally, speaks English, Spanish, and Italian fluently, and contributes to editorial boards in statistical journals. Research Interests : Bayesian methods, extreme value analysis, statistical modeling of spatio-temporal data, risk assessment, and applications in environmental science, hydrology, and social sciences. Her work emphasizes flexible models for complex datasets and interdisciplinary collaborations. Recent Projects : RISE: PRIN-funded project on extreme value analysis (2023-2026) SMARTMUS-E: EU Horizon Europe project on museum energy management (2023-2025) Bayesian Nonparametric Mixture Models for the Dependence of Extremes (BayMEx, 2019-2021) Teaching : Courses include Applied Probability for Computer Science , Statistics for Language Sciences , and Statistics for Empirical Research in Management . She has taught at undergraduate and graduate levels since 2019.

• Evolutionary Algorithms
• Machine Learning
• Optimization
• +۷ مورد دیگر

Giovanni Iacca is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science (DISI), where he serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. He leads the Distributed Intelligence and Optimization Lab (DIOL) and teaches courses including Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, and Optimization Techniques across multiple academic programs. PhD in Computer Science, University of Jyväskylä, Finland (2011) MSc in Computer Engineering, Technical University of Bari, Italy (2006) Professor Iacca's research focuses on the intersection of evolutionary computation, machine learning, and optimization with applications in distributed systems and robotics. His work spans from theoretical foundations of memetic computing and multi-objective optimization to practical implementations in soft robotics, embedded systems, and healthcare applications. Recent efforts emphasize interpretable AI, particularly in reinforcement learning contexts, where his team develops methods to make decision processes transparent while maintaining performance. His research bridges the gap between fundamental algorithmic development and real-world engineering challenges, with over 15 years of industrial experience in optimization applied to engineering, logistics, and scheduling. Analysis of his recent publications reveals a strong trend toward interpretable AI systems, particularly in reinforcement learning contexts, with significant contributions to federated learning optimization, evolutionary neural architecture search, and applications in healthcare scheduling. His work consistently combines evolutionary algorithms with modern machine learning techniques to solve complex optimization problems across diverse domains including soft robotics, batteryless edge computing, and supply chain management. Scientific Awards: EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) Professor Iacca actively supervises a large research group with numerous PhD students across multiple doctoral programs, including Information Engineering and Computer Science, Industrial Innovation, and the National PhD in Artificial Intelligence for Society. His lab has secured significant research funding through collaborations with industry partners and international research consortia. Recent grants support work on interpretable reinforcement learning, federated optimization, and applications of evolutionary computation in healthcare and robotics. He has also been appointed to editorial roles for prestigious journals including IEEE Transactions on Evolutionary Computation and Evolutionary Intelligence. The Distributed Intelligence and Optimization Lab (DIOL) under Professor Iacca's leadership comprises over 30 researchers including postdocs, PhD students, and master's students. The lab maintains strong international collaborations and has developed specialized expertise in evolutionary computation, interpretable AI, and optimization for embedded systems. Current projects include work on the EIC Pathfinder Challenge "Awareness Inside," development of methods for batteryless edge intelligence, and applications of evolutionary algorithms to healthcare scheduling problems.

• fuzzy clustering
• time series clustering
• clustering methods for complex data structures
• +۶ مورد دیگر

Pierpaolo D'Urso is a Full Professor of Statistics at Sapienza University of Rome, where he serves as Dean of the Faculty of Political Science, Sociology, Communication. He is Director of the Master's Program in Data Science for Public Administration and holds the Department of Social and Economic Sciences. His academic career includes serving as Director of the Department of Social and Economic Sciences, Vice Rector for Staff Training, and Member of the Academic Senate at Sapienza University. His primary research interests focus on advanced statistical methodologies, particularly fuzzy clustering , time series clustering , and clustering methods for complex data structures . He has extended these methods to diverse applications including statistics and democracy, electoral systems analysis, sports analytics, and public sector management. His work bridges theoretical statistical development with practical applications in social sciences and policy-making. His recent publications demonstrate a strong focus on spatial regularization techniques, entropy-based approaches, and novel applications of fuzzy clustering to interval-valued data and network structures. The research spans multiple disciplines including statistics, data science, economics, and political science, reflecting his interdisciplinary approach to statistical methodology. D'Urso has been recognized with inclusion in Stanford University's World's Top 2% Scientists ranking. He serves as Associate Editor and reviewer for numerous international journals in Statistics and Data Science, contributing significantly to the scholarly community through peer review and editorial work. At the institutional level, he has held significant administrative roles including Director of the Master in Data Science for Public Administration, demonstrating his commitment to developing statistical education for public sector applications. His work with government entities, particularly as a member of expert committees at the Presidency of the Council of Ministers, highlights the practical impact of his research on policy and governance.

Mario Piccioliمشاهده پروفایل

دانشیار

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Metalloproteins
• Protein Structure
• +۴ مورد دیگر

Mario Piccioli is an Associate Professor at the University of Florence, affiliated with the Magnetic Resonance Center and the Department of Chemistry. His research focuses on advanced NMR spectroscopy techniques applied to metalloproteins, particularly investigating iron-sulfur clusters, paramagnetic systems, and protein structural dynamics. Key areas include structural biology of metalloproteins, NMR methodology development, and understanding biological processes involving metals such as iron and nickel. Education details are not explicitly stated here, but his professional trajectory includes extensive contributions to NMR instrumentation and structural studies. Research interests emphasize paramagnetic NMR as a tool for studying protein structure-function relationships, with applications in metallomics and enzymology. His work bridges chemistry and biology, addressing fundamental questions in protein biogenesis and metal coordination. Publications highlight innovations in NMR techniques like 13C relaxation-filtered methods and NOE-less structural determination for highly paramagnetic systems. These advancements enable deeper insights into metalloprotein behavior under physiological conditions. Awards or grants are not listed in the provided text, but his involvement in collaborative projects such as CERM (Magnetic Resonance Center) underscores institutional support for his research. He advises students in the Department of Chemistry and collaborates on projects related to iron-sulfur protein biogenesis and paramagnetic systems. His lab’s work often involves interdisciplinary approaches, combining experimental NMR data with computational modeling to elucidate protein mechanisms. The Magnetic Resonance Center serves as a hub for his experimental activities, leveraging state-of-the-art facilities for biomolecular studies.

Roberto Grossiمشاهده پروفایل

استاد

• Algorithms and Data Structures
• Dynamic and External Memory Algorithms
• Algorithm Engineering
• +۴ مورد دیگر

Roberto Grossi is a Professor of Algorithms and Data Structures at the University of Pisa's Dipartimento di Informatica. He is actively involved in editorial roles for journals like Theory of Computing Systems and RAIRO-Theoretical Informatics, and has served on program committees for conferences such as SWAT, IWOCA, ESA, FOCS, and SPIRE. His research focuses on algorithms, data structures, graph theory, and bioinformatics, with contributions to string processing, compressed data structures, and algorithm engineering. He advises Ph.D. students and collaborates on projects involving large-scale networks and community detection. Grossi has authored numerous publications, including works on clique enumeration, graphlet algorithms, and phylogenetic reconstruction. His teaching includes courses on Algorithm Design and Data Structures, and he maintains an active presence in academic networks through his research and editorial activities. Education: Not explicitly stated, but inferred from his role as a professor at a top Italian university. Research Interests: Dynamic algorithms, external memory methods, experimental algorithmics, pattern matching in strings and graphs. Grants/Awards: While no specific prizes are listed, his extensive publication record and editorial roles reflect sustained recognition in the field. Lab/Teams: Leads research groups focused on algorithmic foundations and applications, collaborating with international teams on projects like Zuckerli (compressed graph representations) and motif trie indexing. His work bridges theoretical computer science with practical applications in bioinformatics and network analysis.

Franco Turiniمشاهده پروفایل

استاد

• Programming Languages
• Logic Programming
• Functional Programming
• +۷ مورد دیگر

Franco Turini is a Full Professor in the Department of Computer Science at the University of Pisa, where he has been a central academic figure for decades. He has held major leadership roles including Director of the Department of Computer Science (1996–2002, 2010–2016), President of GRIN, and Academic Senator since 2011. He has also served on national research commissions such as the FIRB Commission of the Italian Ministry of University and Research. Research Interests: His work spans programming languages, formal semantics, logic and functional programming, and more recently, explainable AI (XAI), fairness in AI, and data mining with constraints. He has led national and international research initiatives and co-edited influential works such as Meta-logics and Logic Programming with Krzysztof Apt. The recent publications (2014–2022) show a strong focus on XAI, with a trend toward developing interpretable, rule-based, and counterfactual explanation methods for black-box AI systems. His work frequently appears in top venues in AI and data mining, emphasizing transparency, fairness, and actionable insights. Scientific Awards: Ordine del Cherubino, University of Pisa (2007) Advising and Grants: While specific students are not listed, Turini has been a principal investigator in major research projects such as XAI (ERC Advanced Grant), NoBIAS, ICON, and ANONIMO. He has secured significant national and European funding, including leadership in national programs sponsored by the Italian National Research Council. Labs and Teams: He is affiliated with the KDD Lab at the University of Pisa, a leading research group in knowledge discovery and data mining, collaborating closely with Fosca Giannotti, Dino Pedreschi, and others in advancing ethical and explainable AI.

Arianna Croseraمشاهده پروفایل

پژوهشگر

• European Union (EU)
• Environmental governance
• Digital transformation
• +۷ مورد دیگر

Arianna Crosera is a Researcher at the Department of Law, European University Institute, joining in cohort 2022. She is affiliated with the Environmental challenges and climate change governance research group, INFOSOC (Information Society) Working Group, and Digital transformations and society cluster. Her research focuses on the critical intersection of environmental and digital transitions within European regulatory frameworks. Key areas include EU environmental law and regulation, sustainable corporate governance, energy regulation for digital infrastructure, and empirical legal studies examining how technological change impacts societal structures. Her work addresses the complex regulatory challenges of reconciling green and digital transitions, particularly regarding data-center environmental impacts. Her recent publication analyzes barriers to standing in environmental litigation before the Court of Justice of the European Union, reflecting her expertise in EU judicial mechanisms and environmental law enforcement. This work contributes to understanding how legal frameworks can better facilitate environmental protection through improved access to justice. Arianna is supervised by Peter Frank and Drahos for her thesis research on Reconciling the green and the digital transitions: the environmental regulation of data-centres . Her Italian nationality and position within the Department of Law place her at the forefront of European legal scholarship addressing contemporary environmental and digital governance challenges.

Matilde Biniمشاهده پروفایل

استاد

• Statistical Models
• Econometrics
• Multivariate Analysis
• +۵ مورد دیگر

Matilde Bini is a Professor of Economic Statistics (SECS-S/03) and Director of the Department of Human Sciences at the University of European Rome (UER). She holds a PhD in Applied Statistics from the University of Florence (1995) and has taught Corporate Statistics and Statistical Methods for Business Decisions at UER since 2009. Visiting positions: University of Florida (USA), IMT Lucca, University of Cordova (Spain) Active in national research groups funded by the Italian Ministry of Research Editorial roles: Associate Editor for Statistical Methods and Applications and Italian Journal of Applied Statistics Her research focuses on generalized linear mixed models, structural equation models, multivariate methods, and longitudinal models. She specializes in robust diagnostic analysis for data quality and anomaly detection, with applications in labor markets, firm productivity, entrepreneurship, and higher education evaluation. Recent publications examine zero-inflated beta regression for university career prediction, multilevel modeling of educational effectiveness, and robust analysis of high-tech firm competitiveness. Her work frequently appears in peer-reviewed international statistics journals. Professional affiliations include the Italian Statistical Society, International Statistical Institute, American Statistical Association, Royal Statistical Society, and the International Society for Business and Industrial Statistics.

Edmondo Trentinمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Neural Networks
• Pattern Recognition
• +۵ مورد دیگر

Edmondo Trentin is an Associate Professor of Artificial Intelligence at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He chairs the IAPR Technical Committee 3 (Neural Networks & Computational Intelligence) and serves as an Associate Editor for Neural Processing Letters . His research focuses on neural networks, probabilistic models, and their applications in bioinformatics, speech processing, and pattern recognition. Trentin has taught extensively at both undergraduate and graduate levels, including courses on Artificial Intelligence, Neural Networks, and Pattern Recognition. He has advised numerous PhD students and contributed to international conferences and workshops. His work integrates machine learning with statistical methods, emphasizing hybrid models for complex data analysis. Teaching Highlights: Artificial Intelligence (MSc. courses since 2001) Neural Networks for Statistical Pattern Recognition Machine Learning for Graphs Supervised and Unsupervised Learning Research Interests: Connectionist density estimation Probabilistic graphical models Hybrid ANN/HMM systems Bioinformatics applications Professional Activities: Former Vice-Chair of IAPR-TC3 IEEE Computational Intelligence Society Secretary (2005-2006) Organizer of international workshops on neural networks and pattern recognition

• Bioinformatics
• Computational Biology
• Graph Algorithms for Biological Networks
• +۷ مورد دیگر

Rosalba Giugno is a Full Professor of Informatics at the University of Verona's Department of Computer Science. She serves as Principal Investigator of the InfOmics Laboratory and is the reference person for the Master in Medical Bioinformatics program. Previously, from 2017 to 2022, she directed the Infolife laboratory, which comprised 35 research groups from various Italian universities. Her research focuses on algorithmic bioinformatics and computational biology, specifically developing graph algorithms for biological networks, integrating and analyzing biomolecular data, and modeling biological systems for personalized medicine. She leads a research group consisting of 5 PhD students and multiple master's thesis students. Her work bridges theoretical computer science with practical biomedical applications, utilizing approaches from machine learning, data science, mathematics, and graph theory. Professor Giugno has authored 130 scientific publications, with 70 appearing in international journals. Her recent work shows a strong trend toward applying advanced computational methods to solve complex problems in personalized medicine, particularly in patient stratification using multi-omics data, drug repurposing, and combination therapy prediction. She has secured funding for numerous national and European research projects, with recent initiatives focusing on multi-drug resistance in rheumatoid arthritis and personalized prostate cancer evaluation. She serves as an editor for the journal Information Systems and participates in scientific committees for international conferences and schools. Her leadership extends to university governance through roles on the Computer Science Teaching Committee and Department Council. Professor Giugno teaches courses in the Master's program in Medical Bioinformatics and the PhD program in Computer Science, including 'Analisi di dati Multi-omics da single-cell' and 'Programming for bioinformatics.' She has consistently taught these courses from 2016 through 2025, demonstrating commitment to training the next generation of bioinformatics specialists. The InfOmics Laboratory under her direction develops computational methods for biomedical data analysis, with applications spanning genomics, patient classification, and therapeutic optimization. The lab maintains strong connections with both academic and industrial partners through collaborative research projects.