Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
Post hoc uncertainty quantification for deep regression using contextualized normalizing flows without retraining the base model.
I’m a computational biologist working at the interface of data science and biology - applying statistical and machine learning approaches to uncover structure in complex biological data.
My background spans experimental biology, computational modeling, and bioinformatics. After completing a PhD at Lund University focused on visual systems in invertebrates, I transitioned toward data-driven biology - developing analytical tools and predictive models that bridge biological insight and computation.
I enjoy learning new analytical methods, writing code, and communicating science clearly. My goal is to continue growing as a biological data scientist, expanding across diverse fields within the life sciences while keeping biological meaning at the core of the analysis.
PhD
Lund University
MSc
University College Dublin
BSc (Hons)
University College Dublin
Post hoc uncertainty quantification for deep regression using contextualized normalizing flows without retraining the base model.
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