Stavros Stavroglou

Organiser of the annual Quantitative Finance and Risk Analysis international symposium (Corfu 2025, Santorini 2024, Crete 2023, Kos 2020, Mykonos 2019, Rhodes 2018)

Research Director

• PhD students in AI, East Asian Economies, Econometrics

• MSc students in Quantitative Finance, Risk Management, and Credit Scoring

Course Designer & Instructor

• Time Series Forecasting (CMSE11640)

• Data-driven Business Insights (CMSE11648)

• Statistical Learning in Banking (CMSE11651)

Stavros is currently an Assistant Professor in Fin Tech at the University of Edinburgh Business School, Scotland, UK. Before joining Edinburgh, he held research roles at Monash University (Australia) and University College Dublin (Ireland).

His work focuses on quantitative modeling, narrative tracking, real-time forecasting, and bridging academic innovation with tangible global market impact.

Stavros publishes in top academic journals—such as Proceedings of the National Academy of Sciences (PNAS) and Risk Analysis—and organize the annual international QFRA (Quantitative Finance and Risk Analysis) symposium.

Research Highlights

• Development of Pattern Causality
  Designed a causality-driven forecasting framework—“pattern causality”—used to uncover hidden interactions in complex financial and environmental systems, featured in PNAS.

• Pioneering Causal Decision-Making AI
  Created “Prometheus,” a proactive data-to-decision analytics prototype for tackling large-scale challenges (e.g., air pollution in Beijing) and a risk-based framework for environmental health policies.

• Software Contributions
  Led development of two open-source packages:

  • pattern-causality (Python Module)

  • patterncausality (R Library)
    These are capable of quantifying “dark causality” in financial instruments such as Credit Default Swaps.

He earned his PhD and MRes in Applied Mathematics and Decision Making (University of Liverpool, UK), funded by prestigious EPSRC-ESRC scholarships. He also holds an MSc and BSc in Mathematics from Aristotle University of Thessaloniki (Greece), both under full IKY Scholarships. During his academic journey, he served as a visiting scholar at California Institute of Technology, won a trading competition in 2018, and continues to be recognized for both his research achievements (Best PhD Thesis 2020, University of Liverpool) as well as his practical impact on Fin Tech companies.

Stavros has collaborated with policy-makers, governmental organizations, and large-scale firms to develop decision frameworks that reduce risk and optimize performance in finance and beyond. This includes a major consulting project for an Investment bank in Germany, focusing on asset allocation, as well as inventory management AI for a leading supermarket chain in Thailand.

Current research interests and potential areas for PhD supervision are as follows:

• Forecasting Future States of Financial Markets

  • Time series analysis, narrative tracking, global macro strategies, emergent market behaviors

• Pattern Recognition for Decision-Making

  • Designing transparent and explainable AI solutions, bridging data to decisions

• Transparent & Explainable AI

  • Cutting-edge causal inference methods for risk assessment and policy decisions