Lecturer in Credit Risk and Fin Tech
Roles and Responsibilities
Organiser of Quantitative Finance and Risk Analysis international symposium
Background
Stavros currently serves as an Assistant Professor in the Management Science and Business Economics group (Lecturer U.K. equivalent) at the University of Edinburgh Business School, Scotland, U.K. Before joining the University of Edinburgh, he enriched his academic journey with roles at Monash University, Australia, and University College Dublin, Ireland. His expertise lies at the intersection of data science, algorithms and forecasting with a focus in designing transparent systems so that stakeholders can understand the processes behind decision analytics.
Stavros' work primarily focuses on developing methodologies to uncover causal interactions within complex systems. His flagship causality-driven forecasting framework "Pattern Causality" is demonstrated in his significant contributions to understanding stock markets, ecosystem changes, key factors in physiological ailments, and credit derivatives. To bridge data with decision-making through analytics he also developed the prototype "Prometheus", a proactive decision-making framework which he demonstrated with a project on the Air Pollution in Beijing, China.
Stavros publishes in top academic journals - PNAS (Proceedings of the National Academy of Sciences of the U.S.A.), Risk Analysis: An International Journal. He is the organiser representing Scotland in the annual international symposium of QFRA (Quantitative Finance and Risk Analysis).
Stavros has a PhD in Applied Mathematics (University of Liverpool, U.K.); MRes in Decision Making (University of Liverpool, U.K.); MSc in Complex Systems (Aristotle University of Thessaloniki, Greece); BSc in Mathematics (Aristotle University of Thessaloniki, Greece).
Stavros' research has a strong practical focus and collaborates with Fin Tech and Artificial Intelligence companies as well as Governmental Organizations. He led and collaborated on a number of diverse research and consultancy projects involving portfolio optimization, chatbots for banks and superstores as well as river flooding forecasting.
Research Interests
Current research interests and potential areas for PhD supervision are as follows:
- Forecasting future states of financial markets
- Pattern recognition for decision-making
- Transparent and explainable artificial intelligence
- Understanding climate and its changes via machine and deep learning