Ava Khamseh

Dr. Ava Khamseh is a Lecturer in Biomedical AI at the Institute of Genetics and Cancer, University of Edinburgh. She leads a research group at the intersection of cancer biology, population biomedicine, and causal machine learning, focusing on quantifying molecular interactions in biological systems.

• Research spans cancer initiation, evolutionary dynamics, and causal machine learning for biomedical data analysis.
• Specializes in single-cell biology, transcriptional trajectories, and higher-order genetic interactions.

Key research themes include:

• Developing experimental-computational models to trace early cancer evolution via mutational and transcriptional dynamics.
• Designing non-parametric probabilistic ML methods for causal inference in UK Biobank and scRNA-seq data.
• Collaborations with experts in experimental biology, mathematics, and machine learning.

Recent publications highlight her work on:

• Causal modeling of single-cell gene expression to resolve cryptic states in cancer progression.
• Investigating MITF-low melanoma subtypes and residual disease mechanisms.
• Machine learning applications in quantitative biomedicine, including Myalgic Encephalomyelitis biomarker discovery.
• Physics-inspired computational approaches, such as Ising model parameter estimation for biomedical applications.

Projects funded by Cancer Research UK, MRC, and GlaxoSmithKline support her work on cancer inception, research culture, and biomarker prediction.