Research
Research Areas
Work at the intersection of machine learning methods, domain expertise, and real-world deployment constraints, across six core research themes.
Medical & Clinical AI
AI systems for radiology, pathology, fetal ultrasound, and clinical decision support. Human-AI collaboration studies, clinician-in-the-loop evaluation protocols, and federated learning for privacy-preserving clinical research. Current PhD focus at the Technical University of Denmark.
Related publications
Uncertainty-Calibrated Explainable AI for Fetal Ultrasound Plane Classification
Transparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP
Explainability & Trustworthy AI
Uncertainty-calibrated explainability methods including GradCAM variants, LIME-style surrogates, and uncertainty-weighted activation maps. Transparency frameworks, model cards, data provenance, and governance for responsible AI deployment.
Related publications
Uncertainty-Calibrated Explainable AI for Fetal Ultrasound Plane Classification
Transparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP
Bioinformatics & Molecular Systems
Multi-omics integration, large-scale proteomics pipelines, WGS/WES workflows, and ML-driven biomarker discovery. Current PhD focus at University of Luxembourg on systems and molecular biomedicine.
Foundation Models & LLMs
Evaluation methodology and architecture research for large language models and multimodal systems. Patch-based time series transformers, transparent clinical NLP, and mechanistic analysis of catastrophic forgetting.
Related publications
PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting
Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Geospatial & Urban AI
GeoAI frameworks for urban mobility, spatio-temporal traffic modeling, climate-resilient housing, and autonomous site selection. Evidence-based urban policy and public-sector analytics.
Related publications
Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility
Urban Spatio-Temporal Foundation Models for Climate-Resilient Housing: Scaling Diffusion Transformers for Disaster Risk Prediction
Scientific Computing & Physics ML
Physics-informed neural networks with Bayesian uncertainty quantification for PDEs. Multi-fidelity frameworks, neuromorphic edge computing, and graph neural networks for combinatorial optimization.
Related publications
Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations
Energy-Efficient Neuromorphic Computing for Edge AI: A Comprehensive Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization