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Paolo FerraginaView profile

Professor

• Data Compression
• Learned Data Structures
• Search Engines
• +4 more

Prof. Paolo Ferragina is a Full Professor at the Department of Computer Science, University of Pisa. He leads the Advanced Algorithms and Application Lab (Acube), focusing on algorithms for Big Data compression, indexing, and analysis. His work includes collaborations with MIT, Harvard, Google, and others. He has received prestigious awards like the ACM Paris Kanellakis Award (2022) and the ESA Test of Time Award (2023). Education: Laurea (summa cum laude, 1992) and PhD (1996) from the University of Pisa, Postdoc at Max-Planck Institute for Informatics (1997–1998). He has held roles such as Vice-Rector for ICT (2019–2022) and President of the PhD in Computer Science program (2017–2020). Research interests span compressed data structures, search engines, sports analytics, and DNA-based data storage. His work on the FM-index and TAGME system has had significant impact. He co-founded PlayeRank, a startup for soccer performance analysis. Grants and collaborations include Google Cloud Research Innovator (2022), Yahoo! Research Award (2007–2010), and projects with European Broadcasting Union and ST Microelectronics. He has authored over 180 papers and books like Computational Thinking and Pearls of Algorithm Engineering . Labs/Teams: Acube Lab at University of Pisa, collaborations with MIT’s Senseable City Lab, Harvard’s Pinello Lab, and industry partners like Bloomberg and ENEL.

Vincenzo BonniciView profile

Associate Professor

• Bioinformatics
• Artificial Intelligence
• Computational Biology
• +3 more

Vincenzo Bonnici is an Associate Professor in Informatics at the Department of Mathematical, Physical and Computer Sciences , University of Parma, Italy. His academic career includes a PhD in Computer Science from the University of Verona (2015), preceded by a master’s degree from the University of Catania (2011). He has held research positions at prestigious institutions, including the Institute for Genomics and Bioinformatics (IGB) , University of California, Irvine (2013–2014). Education BSc/MSc in Computer Science from University of Catania (2008/2011) PhD in Computer Science from University of Verona (2015) His research focuses on bioinformatics and computational biology , with a strong emphasis on subgraph matching algorithms for biomedical graphs, genomic sequence analysis using information theory, and parallel computing for biological networks. His work spans pangenomics, phylogenomics, and non-coding RNA studies, with applications in GPU and SMP architectures. The article trends reflect his core expertise in graph algorithms and computational genomics. Key contributions include MULTI-GRAPHMATCH (2025) for multigraph analysis, ARC-MATCH (2024) for edge domain-based graph querying, and foundational work on pangenome discovery (e.g., PANDELOS series, 2023). Parallel computing and information theory are recurring themes across his publications. Scientific awards include the ICPR 2014 international graph-matching contest and a best poster award at the Jacob T. Schwartz International School for Scientific Research. He has served as a speaker at 12 international conferences. Teaching includes courses on Artificial Intelligence Algorithms (2025/2026), Artificial Intelligence Laboratory , and Software Engineering at the University of Parma. His work integrates algorithmic innovation with biological data analysis.

Michelangelo CeciView profile

Professor

• Data Mining
• Machine Learning
• Relational Data Mining
• +5 more

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.

Anton DignösView profile

Associate Professor

• Temporal Databases
• Time Series Data
• Data Analytics
• +3 more

Anton Dignös is a tenured Associate Professor at the Free University of Bozen-Bolzano, Faculty of Engineering, where he leads the Database Systems Research Group. He holds a PhD from the University of Zurich (2014) and has been a faculty member since 2014. His research focuses on temporal databases, time series data, and data analytics, with contributions to indexing, query optimization, and benchmarking tools like TSM-Bench and SEER. Education : BSc and MSc (Computer Science) - Free University of Bozen-Bolzano (2008, 2010) PhD (Dr. sc. in Computer Science) - University of Zurich (2014) Research Interests : Dignös specializes in temporal database systems, time series management, and efficient query processing. He develops novel indexing techniques (e.g., RD-INDEX) and tools for benchmarking time series systems (SEER, TSM-Bench). His work addresses challenges like anomaly detection, sparse data summarization, and parallel join algorithms. Awards : Best Paper Award at SSDBM 2023 Best Paper Award at ADBIS 2022 ACM Computing Reviews Best of Computing (2017) Teaching & Advising : Teaches Data Management and Applied Informatics courses at both BSc and MSc levels. Advises ~25 students (PhD/MSc/BSc) on topics like temporal databases and machine learning applications. Active in thesis supervision and course development. Labs/Teams : Leads the Database Systems Research Group, collaborating on projects like the Period Index for temporal data and VISOR for data visualization tools.

Francesco GuerraView profile

Professor

• Scalable Data Management
• Data Mining
• Big Data Analysis
• +5 more

Francesco Guerra is a Full Professor at the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. He teaches Software Engineering and Big Data Analysis. His research focuses on scalable data management, machine learning explainability, entity resolution, data mining, text analytics, semantic web, and keyword-based search on structured data. Academic Positions: Rector’s delegate for ICT (since Feb 2024) President of Computer Engineering Degree Councils (since Nov 2022) Curriculum Coordinator for Computer Engineering Doctorate (2021-2023) Visiting Professor at University of Rijeka (2019) Education: PhD in Information Engineering (2004) Master Degree in Information Engineering (2000) Classical Diploma (1992) from Liceo Classico 'L.A. Muratori' Research Interests: Develops explainable machine learning techniques for entity resolution and anomaly detection. Specializes in interpretable time series clustering (Time2Feat), BERT-based entity matching analysis, and pushing ML predictions into DBMS frameworks. Active in semantic keyword search and big data integration projects like KEYSTONE (COST Action 2013-2017) and Re-search Alps (2017-2019). Projects: KEYSTONE COST Action: Semantic keyword search on structured data Re-search Alps: Big data integration for Alpine research labs AutoML4Clust: Automated machine learning for clustering FAIRnessEval: Framework for algorithmic fairness evaluation Awards: No awards explicitly listed in the provided texts. However, his work has been funded through EU and national projects.

• Human-computer interaction
• Learning analytics
• Semantic web
• +8 more

Laura Farinetti is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino. She is a member of both the College of Management and Production Engineering and the College of Computer, Film and Mechatronics Engineering, reflecting her interdisciplinary role in engineering education. Her work bridges computer science and pedagogy, with a focus on innovative teaching methods. Her research interests include human-computer interaction, learning analytics, semantic web, and the use of information and communication technologies (ICT) to support accessibility, inclusion, aging, and open/distance education. She is particularly interested in fostering creativity and critical thinking through technological tools in educational contexts. Her recent publications (2022–2024) demonstrate a strong trend in applying artificial intelligence and data-driven methods to enhance learning experiences. Key themes include the use of ChatGPT for automated correction of SQL exercises, development of educational chatbots using LangChain, abstractive summarization of video lectures, and serious games like 'SQL Murder Mystery' for teaching database querying. These works highlight her focus on intelligent systems, machine learning, and web-based educational platforms. Laura Farinetti has served as Scientific Responsible or Director for several research and teaching projects, including the EU-funded Virtual Campus Hub (2011–2013) and the ESWES project (2004–2007), as well as commercial teaching initiatives like Challenge@PoliTo by Firms (2021–2022). These projects reflect sustained external funding and leadership in educational innovation. She is an active member of the ELITE research group (Intelligent and Interactive Systems) at DAUIN, which focuses on developing advanced interactive technologies. Her teaching responsibilities include leading and instructing the 'Databases' course across multiple engineering programs, such as Management Engineering and Film and Media Engineering, as well as the 'Challenge@PoliTo' master's course, showcasing her commitment to both curriculum delivery and innovative pedagogical practices.

Giulio Ermanno PIBIRIView profile

Associate Professor

• Computer Science
• Bioinformatics
• Algorithms
• +5 more

Giulio Ermanno PIBIRI is an Associate Professor at the Department of Environmental Sciences, Computer Science and Statistics of Ca' Foscari University of Venice. His primary research focuses on algorithms, data structures, and their applications in bioinformatics, genomics, and information retrieval. He specializes in developing efficient methods for handling large-scale genomic data and optimizing storage and querying of massive datasets. His work includes advancements in minimal perfect hashing, k-mer indexing, and compression techniques for de Bruijn graphs. Key research areas include: Algorithm design for bioinformatics (e.g., sequence analysis, genome assembly) Data compression and indexing (inverted indexes, variable-byte codes) Efficient storage and retrieval of genomic and textual data Secure machine learning (verifiable tree ensembles) Recent publications highlight contributions to: Meta-colored de Bruijn graphs for genomic data representation Efficient k-mer hashing and sampling algorithms Secure and robust machine learning models Teaching includes courses on computer programming and foundational computer science topics for engineering students.

Maikel Lázaro PÉREZ GORTView profile

Researcher

• Computer Science
• Data Security
• Database Systems
• +3 more

Maikel Lázaro PÉREZ GORT is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics (DAIS) at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity Communications and teaches advanced courses on relational data watermarking in doctoral programs. His primary research focuses on database security, specifically developing robust watermarking techniques for relational data integrity protection against attacks in untrusted environments. Teaching Activities: Doctoral Course (DM226/2021): 'Relational Data Watermarking: Fundamentals' and 'Robust Schemes' Past courses include 'Programming and Laboratory-2' for undergraduate Computer Science students and 'Lab of Human-Centered Design' for Digital Management programs. Research Interests: His work addresses challenges in data watermarking resilience, including topology optimization for watermark sources, query impact analysis, and prevention of additive attacks. Recent efforts emphasize earth observation data integrity and quantile-based distortion minimization techniques. Publications: Over 13 peer-reviewed articles from 2017-2025, focusing on watermarking methodologies for relational databases, earth observation systems, and forensic image analysis. Key themes include brute-force attack resilience, semantic-driven marking, and high-quality key generation schemes. Awards/Grants: No specific awards or grants mentioned in the provided materials. Labs/Teams: Affiliated with the DAIS department's research groups working on information systems security and complexity science.

• Multimedia Data Management
• Collaborative Filtering
• Similarity Search
• +5 more

Ilaria Bartolini is a Full Professor at the University of Bologna's Department of Computer Science and Engineering, specializing in multimedia data management and preference learning. She coordinates the MultiMedia Database Group and leads the PhD Programme in Computer Science and Engineering. Her research focuses on similarity search, collaborative filtering, and applications in cultural heritage and health domains. Education: Laurea in Computer Science (1997), University of Bologna PhD in Electronic and Computer Science Engineering (2002), University of Bologna Research Interests: Her work spans multimedia systems, data curation, and AI-driven solutions for healthcare and cultural heritage. She collaborates with institutions like HKUST, NJIT, and the European Processor Initiative, leading projects such as DETECt (EU H2020) and S-PIC4CHU (PRIN 2022). Grants & Projects: EU H2020 DETECt (2018–present): Transcultural crime narrative analysis PRIN 2022 S-PIC4CHU (2024–present): Data science integrity GenData 2020, WISDOM, COOPERARE (Italian national projects) Teaching: Leads courses on web applications, multimedia data management, and digital humanities at the undergraduate and graduate levels. Her educational framework emphasizes active e-learning through tools like ALDO. Labs & Teams: Directs the MultiMedia Database Group, fostering interdisciplinary research in data science and AI applications.

Fabio PersiaView profile

Associate Professor

• Complex Event Processing
• Event Detection
• Analysis in Large Scale Databases
• +4 more

Fabio Persia is an Associate Professor at the Department of Information Engineering, Computer Science and Mathematics (DISIM) of the University of L'Aquila , Italy. Previously, he held positions as Tenure-Track Assistant Professor (RTD/B) and Assistant Professor (RTD/A) at the same department and the Free University of Bozen-Bolzano , respectively. He earned his MSc and PhD in Computer Engineering from the University of Naples Federico II (2009 and 2013), with a six-month visiting scholar stint at the University of Maryland, College Park (2012). Education : MSc, PhD in Computer Engineering (University of Naples Federico II, 2009/2013) Visiting Scholar (University of Maryland, 2012) His research focuses on event detection and analysis in large-scale databases , applied to multimedia, semantic analysis, and security . This spans video surveillance systems, social network analysis, and probabilistic itinerary planning. He co-developed ISEQL (Interval-based Surveillance Event Query Language) with advanced temporal operators and efficient algorithms for interval joins, extending its use to social networks and PostgreSQL integration. Notable collaborations include work with Prof. Sven Helmer and Dr. Paolo Bolzoni. Recent research trends include robotic platforms for dynamic surveillance (SARRI robot), multi-camera configurations , and social sensing for tourist applications . He has secured over €45,000 in research funding and published in top venues like IEEE TKDE, VLDBJ, IJCAI, and IEEE ICSC.

Domenico LemboView profile

Professor

• Knowledge Representation and Reasoning
• Description Logics
• Ontology-Based Data Access
• +6 more

Domenico Lembo is a Full Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering. He is a leading figure in Knowledge Representation and Reasoning, focusing on Description Logics, Semantic Web, and Ontology-Based Data Access (OBDA). His research spans information integration, inconsistency-tolerance, and controlled query evaluation, with significant contributions to theoretical and applied data management. Academic Roles: Full Professor (since 2004), Program Committee Chair for major AI/DB conferences (IJCAI, AAAI, ISWC), Co-founder of OBDA Systems startup Leadership: Head of CAIRNE Rome Office (since 2023), General Chair of SEBD 2025, Vice-President of RR Conference Steering Committee Research Interests: Lembo's work bridges Description Logics with database theory, enabling efficient query answering over inconsistent data. He pioneered DL-Lite framework for tractable ontologies, developed tools like MASTRO and Graphol for visual ontology modeling, and advanced controlled query evaluation with epistemic policies for privacy-preserving systems. Awards: 2021 AAAI Classic Paper Award for DL-Lite 2024 IJCAI Distinguished Paper Award 2020 ISWC Best Poster/Demo Award Exceptional Guest Editor, Semantic Web Journal (2014) Teaching: He teaches courses on databases, data science, and computer science fundamentals. Currently coordinating 'Fondamenti di Programmazione I' and 'Data Management for Data Science', with extensive experience in course design and educational innovation.

• Medical Informatics
• E-learning
• Semantic Web
• +5 more

Biagio Lenzitti is a Researcher at the Department of Mathematics and Computer Science , University of Palermo, School of Basic and Applied Sciences. He has taught courses like Programming and Laboratory , Computer Networks , and Educational Planning of Information Systems since at least 2015. Current Office Hours: Monday 9:00-11:00, Studio 201 Contact: biagio.lenzitti@unipa.it | +3909123891101 His research focuses on medical text simplification , patient empowerment , and interactive educational tools , including the development of systems like: MED-VTD: Multilingual Medical Dictionary SimpleHealth Platform: Medical Text Simplification Tools U-MedSearch: Meta Search Engine for Medical Content ETN-FETCH Project: Future Education in Computing Recent publications address AI-driven health information systems , IoT data sharing , and conversational agents for medical education. Key collaborations include the Anghelos Communication Studies Center and European networks like ETN-FETCH . He has supervised theses on topics spanning semantic web applications , open data , and network security since at least 2005.

Giovanni TrappoliniView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +9 more

Giovanni Trappolini is an Assistant Professor in the Department of Computer, Control, and Management Engineering at Sapienza University of Rome, where he conducts research in the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He earned his PhD in Machine Learning from Sapienza in 2022, focusing on Geometric Deep Learning under Prof. Emanuele Rodolà. Trappolini has collaborated with leading institutions such as Stanford, Technion, Meta, Amazon, TII, and the University of Pisa, contributing to cutting-edge research in AI and information retrieval. His research interests include machine learning, deep learning, geometric deep learning, multimodal AI, information retrieval, and large language models. He has pioneered work on Retrieval-Augmented Generation (RAG), Neural Databases, and the development of Fauno, the leading Italian LLM. His teaching experience includes courses on Python for Data Science, Advanced Data Mining, and Statistical Learning at both Sapienza and Luiss Guido Carli. Trappolini's recent publications span top-tier venues like SIGIR, NeurIPS, ECCV, and IEEE Transactions, with a focus on graph neural networks, federated learning, anomaly detection in 5G networks, and multimodal systems. His work shows a strong trend toward robust, privacy-preserving, and multimodal AI systems with real-world applications in language, vision, and network security. Sapienza honor graduate IELTS 8.0 He has advised several teaching roles and delivered courses on Python, data science, and statistical learning. His research is supported through collaborations with industry leaders like Meta and Amazon, and he leads innovative projects such as Fauno and Neural Databases. Trappolini is actively involved in the AI research community and continues to push the boundaries of language and retrieval systems. He is a core member of the RSTLess Lab and has been instrumental in advancing geometric and multimodal deep learning at Sapienza University. His work bridges theoretical innovation with practical implementation, particularly in the domains of Italian language modeling and secure, efficient information retrieval systems.

• Social Network Analysis
• Graph Mining
• Community Detection
• +3 more

Pasquale De Meo is an Associate Professor in the Department of Information Processing Systems at the University of Messina, where he also serves as Coordinator of the Master's Degree Course in Methods and Languages of Journalism. Previously, he was a Tenured Researcher at the same university from 2011 to 2015. He received his PhD in Computer Science from the University of Calabria in 2006 and his VO Degree in Electronic Engineering (magna cum laude) from the University of Reggio Calabria in 2002. His academic journey includes prestigious fellowships such as the Marie Curie Fellowship at VU University Amsterdam (2010-2011) and roles as External Member at Birbeck Institute for Data Analytics since 2017. Dr. De Meo specializes in Social Network Analysis, Graph Mining, Community Detection, and Network Centrality. His research spans multiple domains including social media analysis, criminal network analysis (as evidenced by Salvo Catanese's PhD thesis on "The Network Structure of Mafia Syndicates"), and data science applications. He has published extensively in top-tier venues with an H-Index of 35 on Google Scholar and 29 on Scopus. His recent publications demonstrate strong interdisciplinary connections between computer science, social sciences, and practical applications, covering advanced techniques for educational technology, fraud detection, knowledge graphs, and social influence modeling. Best Italian doctoral thesis in Artificial Intelligence (AI*IA, 2006) Marie Curie Fellowship (2009-2010) Multiple best paper awards at international conferences Dr. De Meo has supervised several graduate students and serves on program committees for major conferences including ACM SIGKDD and IJCAI. His research is supported by significant grants including an ARC Discovery Project on "Dynamics and Control of Complex Social Networks" and a Microsoft Azure Research Grant for community detection in directed graphs.

• Natural Language Processing (NLP)
• Multilingual Lexical Semantics
• Crosslingual NLP
• +4 more

Alessandro Raganato is an Associate Professor at the University of Milano-Bicocca's Department of Informatics, Systems and Communication (DISCo), affiliated with the IKR3 Lab led by Gabriella Pasi. His research focuses on Natural Language Processing (NLP), including multilingual lexical semantics, crosslingual NLP, and neural machine translation. Previously, he was a postdoctoral researcher at the University of Helsinki's Language Technology Lab, contributing to the FoTran project under Jörg Tiedemann. He completed his PhD at Sapienza University of Rome under Roberto Navigli, working on multilingual lexical semantics within ERC-funded projects (MultiJEDI and MOUSSE). He is a member of the ELLIS Society, an EU AI network emphasizing fundamental science and societal impact. Education: PhD in Computer Science, Sapienza University of Rome (SapienzaNLP Lab) Postdoctoral Research, University of Helsinki (Language Technology Lab) Research Interests: Developing crosslingual NLP systems Word sense disambiguation in multilingual contexts Optimizing neural machine translation architectures Evaluating large language models' reasoning capabilities Key Research Trends (2023-2025): Advancing multilingual benchmarking (e.g., Mu-SHROOM task) Exploring diffusion models for concept blending Democratizing NMT via open-source tools like OPUS-MT Personalized search systems leveraging cognitive complexity metrics Labs/Teams: IKR3 Lab (University of Milano-Bicocca) Language Technology Lab (University of Helsinki) ELLIS Society