Motivation

My research aim is ultimately and broadly to support better, fairer, and more informed decision-making. Much of my work addresses credit-related challenges, many of which reflect issues common to decision-making in other domains, too. Therefore, the methods I use and develop are well-suited to complex environments where uncertainty, dynamics, and prediction intersect.

Research Landscape

My work spans several interrelated themes:

Methodological Challenges

• Dynamic prediction in time-evolving settings
• Uncertainty quantification for risk-sensitive decisions
• Interpretability and explainable AI (XAI)
• Stress testing under extreme or systemic scenarios
• Spatial dependence in entities behaviour
• Selection bias from non-random data access (e.g., accepted loans only)
• Imbalanced data and rare-event learning
• Scalability and variable selection to high-volume, high-dimensional data
• From association to causation in predictive modelling

Emerging & Societal Challenges

• Climate change and biodiversity loss as systemic risk factors
• Sustainability in resources allocation and financial markets
• Fairness and equitable access to financial services

Data Modalities

• Cross-sectional observations
• Longitudinal (panel) data
• Time-to-event data in continuous or discrete time
• Unstructured data such as text, transactions, and network traces

Methodological Approaches

• Bayesian hierarchical modelling
• Distributional regression frameworks
• Survival analysis including multi-state models, cure models, and joint models for longitudinal and survival data
• State-space models
• Causal inference

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I am currently accepting PhD students. If you’re interested in working on related topics, feel free to get in touch.