Paolo Mignone

Paolo Mignone is an Assistant Professor in the Department of Computer Science at the University of Bari, Italy. His academic career includes serving as a Research Fellow at the same department from December 2019 to December 2020, followed by his current position as Assistant Professor since December 2020. He earned his PhD in Computer Science from the University of Bari in February 2020.

Dr. Mignone's research spans multiple domains within computer science and data analytics. His primary interests include Big Data Analytics, Bioinformatics, Data Mining and Knowledge Discovery, Link Prediction, and Transfer Learning. He also specializes in Positive Unlabeled Learning, Anomaly Detection, Physical Threat Intelligence, Handwriting Signature Verification, Cyber Security, and Social Network Analysis. His work demonstrates a strong interdisciplinary approach, connecting computer science methodologies with applications in bioinformatics, security, and environmental monitoring.

His publication record shows a clear trend toward distributed computing approaches for transfer learning and anomaly detection, with significant contributions to bioinformatics applications. Mignone frequently collaborates with Michelangelo Ceci, Gianvito Pio, and other researchers at the University of Bari. His work bridges theoretical machine learning advancements with practical applications in areas like gene regulatory network reconstruction, physical threat intelligence, and public transport systems.

As an active member of the academic community, Mignone serves on program committees for major conferences including IJCAI-PRICAI (2020-2025), ECML-PKDD (2020-2025), ECAI (2024-2025), and CIKM (2024-2025). He has also been involved in organizing events such as the International Spring School on Deep Learning (DeepLearn 2023) and the New Frontiers in Mining Complex Patterns workshops at ECML-PKDD (2022-2025).

Dr. Mignone contributes significantly to academic service through extensive reviewing activities for top journals including Bioinformatics, IEEE Transactions on Human-Machine Systems, Machine Learning, and numerous IEEE and ACM publications. He teaches courses ranging from Big Data and Data Mining to Cyber Security and Database Systems at both undergraduate and graduate levels.

His research is conducted within the KDDE lab at the University of Bari, where he works on projects related to STEAL (Source TargEt ALignment) and BioSfer (Biological tranSfer learning) software frameworks. His work demonstrates strong connections between theoretical machine learning advancements and practical applications across multiple domains.