Eliana Pastor is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . She teaches courses including Explainable and Trustworthy AI (Computer Engineering) and Business Intelligence for Big Data (Management Engineering) across academic years 2023-2025. Scientific branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC sectors: Algorithms, Artificial Intelligence, Machine Learning, Software Engineering, Web Systems Her research focuses on Algorithm Fairness , Explainable AI , and Trustworthy AI , with applications in speech processing, computer vision, and ethical AI systems. She leads the commercial research project Root Cause Analysis in Mechatronic Systems via Pattern Recognition and Causal AI (2024-2025) and supervises PhD students Eleonora Poeta (Safe and Trustworthy AI) and Alkis Koudounas (Speech Foundation Models). Recent publications address fairness in speech models, video LLMs for zero-shot summarization, and Kolmogorov-Arnold Networks for language understanding Collaborates with DBDM - Database and Data Mining Group (DAUIN) on AI ethics and data science projects
Akhil Arora is a researcher at Aarhus University, Denmark, specializing in data mining, graph analytics, and information retrieval. His work focuses on scalable systems for network data analysis, Wikipedia navigation patterns, and influence propagation models. Current affiliations: Aarhus University (2025) Previous affiliations: EPFL (Switzerland), Xerox Research Centre India, IIT Kanpur Research Interests : Arora's research bridges graph data management, machine learning applications, and web-scale analytics. His recent projects include large-scale Wikipedia usage analysis and neural methods evaluation for entity alignment. Publication Trends : His work spans from 2014-2025 with consistent output in ACM SIGMOD/PODS conferences and affiliated venues, covering graph algorithms, influence maximization, and web interface design. Technical Contributions : Developed scalable systems for graph querying (RAQ), benchmarked influence maximization methods, and created synthetic data frameworks for Wikipedia navigation studies.
Johann Gamper is a Professor and Head of the Database Systems Group at the Free University of Bozen-Bolzano. He previously served as Vice-Rector for Research (2018-2024) and Vice-Dean for Studies (2014-2018). Education: Information not provided in text. Research Focus: His core research investigates data-intensive systems with emphasis on temporal data management, time series analysis, and efficient query processing. His team combines fundamental research with system development for applications in governance, tourism, healthcare, and agriculture. Current projects include temporal RDBMS extensions, time series motif mining, and multidimensional range count approximation. Publication Trends: Recent work addresses temporal query optimization, time series classification, and generative approaches for e-commerce data. His research demonstrates consistent innovation in temporal data handling and database performance optimization. Teaching: Currently teaches Database Management Systems and advises PhD students in temporal database research.
Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.
Dr. Gabriele Mencagli is an Associate Professor in the Department of Computer Science at the University of Pisa, Italy. He holds a Ph.D. in Computer Science (2012) and has served as an Assistant Professor (2014–2018) and Tenure-Track Professor (2018–2021) before his current position. His research focuses on parallel systems, including architectures, programming models, and runtime systems for data stream processing. He leads work on the WindFlow stream processing library and has contributed to projects like TEXTAROSSA, ADMIRE, and EUPEX. He co-organized major conferences like HPDC 2024 and DEBS 2025 and serves on editorial boards for journals like Future Generation Computer Systems and Cluster Computing. Education: B.Sc. (2006, summa cum laude), M.Sc. (2008, summa cum laude), Ph.D. (2012) in Computer Science, all from the University of Pisa. Research Interests: Parallel programming, self-adaptive systems, data stream processing, GPU/FPGA acceleration, and high-performance computing. Over 80 publications in top journals and conferences, including IEEE TPDS, JPDC, and Euro-Par. Awards include Italian Habilitation as Full Professor (2025). Teaching: Courses on High-Performance Computing and Computer Architecture, including CUDA programming and parallel design patterns. Active in curriculum development for both bachelor’s and master’s programs. Grants & Projects: Principal investigator in EU-funded projects (e.g., TEXTAROSSA, NOUS) and collaborations with industry (e.g., List-group S.p.A., Autodesk). Focus on exascale computing, digital twins, and edge computing.
Giuseppe Pirro is an Associate Professor at the Department of Computer Science, Sapienza University of Rome. His research focuses on Semantic Web technologies, graph databases, and adversarial social network analysis. Specializes in RDF(S) inference, ontology alignment, and knowledge graph analytics Develops methods for community deception in networks and privacy-preserving data publishing Active in temporal query languages and web cartography Research trends include: Advancing graph embedding techniques for knowledge representation Exploring intersection of differential privacy and neural networks Designing novel algorithms for community detection and network deception Improving query languages for semantic web and temporal data Scientific awards: ERC PE6_7 grant ERC PE6_10 grant KET Big data & computing recognition
Diego Reforgiato Recupero is a Full Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, since December 2015. He is the director and creator of the Human-Robot-Interaction Laboratory (http://hri.unica.it) and co-director of the Artificial Intelligence and Big Data Laboratory (http://aibd.unica.it). His roles include quality responsible for the department and membership in the Commission for start-up and spin-off at the University. He teaches multiple courses across disciplines: Computers Architecture for Computer Science bachelor's, Big Data for Computer Science master's, and Web Design and Digital Storytelling for the Master's in Philosophy and Theories of Communication. His research spans interdisciplinary domains including Artificial Intelligence Big Data Analytics Knowledge Graphs Human-Robot Interaction Digital Transformation His scholarly output demonstrates significant focus on AI applications in energy systems (smart grids, district heating), knowledge graph engineering (ontology generation, semantic conversion), and human-centric AI (conversational agents, digital coaching). Publications frequently address data augmentation techniques and transformer architectures across finance, tourism, and mental health domains. He leads the Human-Robot Interaction Laboratory and co-founded the Artificial Intelligence and Big Data Laboratory. His work integrates Apache Spark for big data processing, Unity for synthetic data generation, and RDF/OWL standards for knowledge representation.
Anna Bernasconi is a Tenure-Track Researcher (Assistant Professor) at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, where she leads the Bioinformatics and Data Science Lab within the Genomic Computing group. She has been a visiting researcher at Universitat Politècnica de València (Jan-June 2022). Her research focuses on applying conceptual modeling, data integration, and knowledge engineering in life sciences and other applied sciences domains, with an emphasis on building open-source tools and services. Dr. Bernasconi earned her Master in Computer Engineering in 2015 from Politecnico di Milano and the University of Illinois, Chicago. She completed her PhD from Politecnico di Milano in 2021 with a thesis on genomic data integration. Her academic journey has positioned her at the intersection of computer science and bioinformatics. Her research interests span multiple areas of bioinformatics and data science. She specializes in bioinformatics data and metadata integration methodologies to support complex biological query answering. In recent years, she has focused on viral genomics , particularly sequence modeling, integration, and search systems for mitigating the effects of pandemics like COVID-19. She applies conceptual modeling and knowledge engineering techniques to develop open-source tools for genomic data analysis. Her work bridges computer science with life sciences, addressing challenges in genomic surveillance, data integration, and knowledge management. Dr. Bernasconi's recent publications demonstrate a clear trend toward developing practical tools for genomic surveillance and text analysis. Her work spans from viral genome analysis (Nature Communication 2024) to database systems (SIGMOD 2024) and topic modeling for large text corpora (EDBT 2025), showing her ability to work across multiple technical domains while maintaining a focus on real-world applications in health and environmental science. National Scientific Habilitation in two categories: 09/H1 – Sistemi di Elaborazione delle Informazioni (II Fascia) and 01/B1 – Informatica (II Fascia) Principal Investigator of the SENSIBLE PRIN PNRR 2022 project (funded with ~240K Euros) Principal Investigator of the NGI Search TETYS project (150K Euros funding) Co-founder of LegisRatio S.r.l., a Politecnico di Milano spin-off innovative startup Academic Editor for Plos One and BMC Bioinformatics Dr. Bernasconi actively mentors students and collaborates across disciplines. She is Principal Investigator of significant research projects including SENSIBLE, which aims to develop an early warning system for viral pathogens based on genomic surveillance, and TETYS, which focuses on topic modeling and visualization for large text corpora. Her research has secured funding from the Italian Ministry of University and Research (MUR) and the European Union's Horizon Europe program. She collaborates with experts across disciplines, including virologists like Prof. Ilaria Capua from Johns Hopkins University. She leads the Bioinformatics and Data Science Lab at Politecnico di Milano, which has developed several notable tools including ViruClust (for comparing SARS-CoV-2 genomic sequences), VariantHunter (for monitoring mutation evolution), and RecombinHunt (for identifying recombination events in viral species). Her team also created CORToViz for exploring the CORD-19 dataset and TETYS for topic modeling in various domains including climate change research.
Sergio Tessaris is an Assistant Professor at the KRDB Research Center for Knowledge and Data, affiliated with the Faculty of Computer Science at the Free University of Bozen-Bolzano. His research focuses on semantic technologies, Description Logics, and data-aware workflows. University: Free University of Bozen-Bolzano School: Faculty of Computer Science Emails: Sergio.Tessaris@unibz.it, tessaris@inf.unibz.it Research Interests: His work centers on semantic technologies for data access and process management, including Description Logics reasoning algorithms, declarative business processes, and ontology-driven systems. He has contributed to temporal event data analysis, chatbot design for legal reporting, and SQL null value handling. Recent publications emphasize business process verification, constraint mining, and deep learning applications. Teaching Activities: Local coordinator of the European Masters Program in Computational Logic (EMCL) Lecturer for courses: Integrated Logic Systems, Computational Logic, Introduction to Artificial Intelligence, Non-monotonic Logics Teaching assistant for Semantic Web Technologies and XML/Semi-structured Databases Organisational Contributions: Co-organiser of DL 2007 and DL 2002 workshops Former member of Description Logic steering committee Program committee member for conferences including AAAI-06, ESWC, ISWC, and ODBASE
EMANUELE STORTI serves as an Associate Professor in the Department of Information Engineering at the Faculty of Engineering, Marche Polytechnic University (UNIVPM) in Ancona, Italy. His scientific sector is classified as IINF-05/A - Information Processing Systems. He maintains an active presence at the university with regular office hours on Thursdays from 15:00 to 16:00 at his office located at via Brecce Bianche, 60121 Ancona. Professor Storti's research spans multiple domains within information systems, with particular expertise in semantic technologies and knowledge representation. His work focuses on developing semantic models for performance indicators, knowledge graphs for industrial applications, data lake architectures, and IoT integration systems. He has made significant contributions to the fields of data quality assessment, ontology engineering, and multidimensional data analysis. Analysis of his recent publications (2023-2025) reveals a strong emphasis on knowledge graph applications across various domains including Industry 5.0, environmental impact assessment, and medical applications. His work demonstrates a consistent focus on semantic approaches to solve complex data integration and analytics challenges, particularly in industrial and environmental contexts. The research shows increasing sophistication in handling multidimensional data sources and developing frameworks for semantic representation of complex systems. Professor Storti appears to be actively engaged in the academic community through participation in symposia and workshops, as evidenced by his contributions to the Italian Symposium on Advanced Database Systems and specialized workshops like MADTECC focused on medical applications with digital twins.
Federico Siciliano is a Postdoctoral Researcher and Ricercatore (Researcher) at Sapienza University of Rome, affiliated with the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He has been teaching Algorithmic Methods of Data Mining in the Master’s program in Data Science since 2024. His research spans recommender systems, information retrieval, explainable AI, and deep learning theory. Education: PhD in Data Science, Sapienza University of Rome (2020–2024) Master’s in Data Science, Sapienza University of Rome (2017–2019) Bachelor’s in Management Statistics, Sapienza University of Rome (2014–2017) His research interests lie at the intersection of Artificial Intelligence and Data Science , particularly focusing on sequential and graph-based recommender systems , robustness and explainability in AI models , and theoretical foundations of deep learning . His recent work explores architectural improvements in RAG systems, anomaly detection in 5G networks, and trustworthy AI through counterfactual explanations. The most recent publications (2023–2025) reveal a strong trend toward robust and interpretable AI systems , with recurring themes in recommendation , retrieval , and security . His work combines theoretical rigor with practical applications in healthcare, telecommunications, and misinformation detection. He frequently publishes in high-impact venues such as IEEE Access, ACM RecSys, and CVPR. Scientific Collaborations: University of Cambridge Meta Amazon University of Pisa (UniPi) INAF (Italian National Institute of Astrophysics) Federico actively contributes to the research community by organizing a special session on Neural Methods for IR and RecSys at IJCNN 2025. He has been involved in projects related to space weather monitoring and clinical outcome prediction in thyroid cancer. No formal students or grants are explicitly mentioned, but his collaborative projects suggest involvement in externally funded research. Laboratories and Teams: RSTLess Lab, Sapienza University of Rome
Lorenzo Porcaro is a Marie Skłodowska-Curie Postdoctoral Fellow at the Department of Computer, Control, and Management Engineering (DIAG) at Sapienza University of Rome, where he leads the project Algorithmic Auditing for Music Discoverability (AA4MD) in collaboration with Professor Tiziana Catarci of the HCI group and Professor Fabrizio Silvestri of the RSTLess research group. He completed his PhD cum laude at Universitat Pompeu Fabra (UPF) in Barcelona, with research conducted at the Music Technology Group, Department of Information and Communication Technologies. Prior to his postdoctoral role, he served as a Scientific Project Officer at the European Commission’s Joint Research Centre (JRC), contributing to the Human Behaviour and Machine Intelligence (HUMAINT) team and the European Centre for Algorithmic Transparency (ECAT). Education: Bachelor's in Applied Mathematics, Sapienza University of Rome (2014) Master's in Sound and Music Computing, Universitat Pompeu Fabra (2015) Master's in Intelligent Interactive Systems, Universitat Pompeu Fabra (2018) PhD in Information and Communication Technologies, Universitat Pompeu Fabra (2022, cum laude) His research focuses on recommender systems, algorithmic auditing, music information retrieval, and AI ethics. He investigates how algorithmic systems impact user behavior, diversity, and fairness in music discovery, with a strong emphasis on transparency and user empowerment. His work integrates technical AI methods with human-centered evaluation and societal impact analysis, particularly in the context of digital platform regulation such as the Digital Services Act (DSA). He has contributed to EU-funded projects like TROMPA and MusicalAI, and his publications appear in top venues including ACM Transactions on Recommender Systems, IEEE Transactions on Affective Computing, and RecSys. His recent publications highlight a consistent focus on diversity, fairness, and auditing in music and information systems. Themes include longitudinal studies on music recommendation diversity, user-centric algorithmic auditing frameworks, open datasets for emotion recognition, and critical analyses of diversity in AI research communities. His work bridges technical innovation with ethical and societal considerations. Scientific Awards and Recognition: Marie Skłodowska-Curie Postdoctoral Fellowship PhD awarded cum laude (with full marks) 2024 Featured Article by the Editor-in-Chief of ACM Transactions on Recommender Systems Lorenzo actively mentors prospective PhD students and is involved in research leadership through organizing workshops such as MuRS (Music Recommender Systems) and HCMIR. He has no formal advisees listed yet but welcomes PhD applicants interested in music recommender systems and algorithmic auditing. His research is supported by significant grants, including the Marie Curie fellowship and prior EU and national funding. He is a member of the RSTLess research group and collaborates with leading experts in HCI and AI. Labs and Research Groups: Principal Investigator of AA4MD project, Sapienza University of Rome Member of the RSTLess research group (led by Prof. Fabrizio Silvestri) Former member of the HUMAINT team at the European Commission’s JRC Collaborator with the Music Technology Group (MTG) at UPF Collaborator with the HCI group at DIAG, Sapienza
Giorgio Ghelli is a Full Professor at the Department of Computer Science, University of Pisa. His research focuses on database systems, XML/JSON schema validation, type systems, and formal methods. He has contributed to foundational work on programming languages and query systems, including the development of the Fibonacci database programming language and the TQL query language for semistructured data. His recent work emphasizes JSON schema formalization, validation techniques, and interactive schema inference for large datasets. Teaching includes advanced database courses and foundational database theory. Research interests span data management, formal semantics, and efficient query processing. Over 150 publications including seminal works on XML updates, JSON schema validation, and type systems for mobile ambients.
Sergio Greco is Full Professor at University of Calabria's DIMES Department, Coordinator of the Computer Engineering Degree, and Vice-president of the Computer Engineering national Group. His research spans database theory, data integration, inconsistent data processing, and computational logic. Research Focus: Development of theoretical frameworks for data management including argumentation frameworks, knowledge base querying under inconsistency, and decentralized learning systems. Recent work integrates machine learning with formal reasoning methods. Honors: Best Paper Award at International Conference on Logic Programming (2020) Best Paper Award at RuleML Symposium (2014) Leads projects on cybersecurity and data management funded by EU and Italian Ministry of Research, coordinating national and international research groups.
Alessio Zamboni is an academic researcher and senior software engineer at the University of Trento, where he earned a MSc in Computer Science. He holds roles in the Department of Information Engineering and Computer Science, contributing to the KnowDive research group focusing on Knowledge Management, DevOps, and NLP. His career began at 17 as a full-stack developer, evolving into research roles at Fondazione Bruno Kessler (geographical information systems) and exchange studies in China (Zhejiang and Jilin Universities). Research interests span knowledge graphs, data integration, and software engineering. Notable projects include interoperable electronic health records and university system integration architectures. He has advised EU-funded initiatives in technical education and contributed to multilingual data disambiguation systems. Labs: Active member of the KnowDive Group at University of Trento, collaborating on interdisciplinary projects blending AI, data systems, and educational technology.
