Lecturer, University of Malta, AI Department. Research on tensor-based machine learning methods. Published extensively.

Dr Konstantinos Makantasis is a lecturer in the Department of Artificial Intelligence at the University of Malta. He holds a Diploma in Computer Engineering as well as MSc and PhD degrees from the Technical University of Crete in Greece. In 2020, he was awarded the prestigious Marie Skłodowska-Curie Actions Individual Fellowship (Widening) to advance research on tensor-based machine learning methods for affect modelling. Dr Makantasis has been recognized for his research excellence, including his inclusion in Stanford University’s World’s Top 2% Scientists List in 2023 and an extensive publication record, with 23 journal articles and 49 peer-reviewed conference papers in top-tier venues. His research focuses on the intersection of machine and statistical learning, image and signal processing, and computer vision, aiming to develop state-of-the-art methodologies for data analytics and decision-making technologies. Dr Makantasis values interdisciplinary collaboration and has worked with academic groups such as the KIOS Centre of Excellence at the University of Cyprus, the Institute of Digital Games at the University of Malta, and the Dynamical Systems and Simulation Laboratory at the Technical University of Crete, as well as industry partners including EXUS (UK), 7Reasons (Austria), and Massive Entertainment-Ubisoft (Sweden). His approach combines academic rigor with practical application, contributing to the advancement of artificial intelligence research and its real-world impact.

Senior Statistician, Central Bank of Malta, AI and Data Science expert. Focused on big data, machine learning and statistical processes.

Mr. Bilal Kurban is a seasoned data scientist and AI expert. He is currently a senior statistician in Central Bank of Malta. Before that he was leading the Artificial Intelligence and Data Analysis Unit at the Turkish Statistical Institute. He holds an M.Sc. in Data Science and a B.Sc. in Statistics. Throughout his career, Bilal has spearheaded numerous innovative projects including the implementation of big data analytics, machine learning algorithms and the development of AI-powered tools to enhance statistical processes. His expertise spans across data governance, business intelligence and the integration of administrative data into official statistics. An internationally recognized professional, Bilal has contributed to UNECE workshops and published extensively on topics such as data collection methodologies and the use of administrative data in official statistics. Bilal has a strong background in data acquisition, administrative registers and statistical methodologies. Bilal's multifaceted skill set, combining technical prowess with strategic thinking, positions him as a key figure in the intersection of artificial intelligence, data science, official statistics and data governance.

Statistician, Central Bank of Malta, specializing in data analysis and modeling for financial data.

Roxanne Spiteri is a Statistician within the Statistical Collection and Information Management Office in the Statistics Department at the Central Bank of Malta, a position she has held since July 2025. She brings to this role two years of prior experience as an intern in the same department, during which she built a strong technical foundation in data analysis, modelling and statistical operations that support informed decision-making within the financial sector. A dedicated machine learning enthusiast with a particular interest in the intersection of statistical theory and modern computational methods, Roxanne is a graduate of the University of Malta, where she earned a Bachelor of Science (Honours) in Mathematics, Statistics and Operations Research. Her undergraduate dissertation, “Identifying Cyberattacks using Machine Learning Techniques,” focused on applying Neural Networks and Variable Importance techniques to detect malicious network traffic, contributing to the broader field of anomaly and outlier detection.