About me

I am a Research Associate with expertise in Bayesian statistics, computational biology, and machine learning. My work bridges the gap between advanced mathematical theory and scalable data science solutions, focusing on developing efficient algorithms for high-dimensional statistical inference and complex data analysis.

I currently work at the MRC Biostatistics Unit at the University of Cambridge, collaborating with Oscar Rueda. Since February 2026, my research has focused on discovering novel biomarkers and developing survival and prediction models for pathological complete response in breast cancer. This role involves building robust statistical frameworks to analyze clinical outcomes, processing high-dimensional genomic datasets, and translating complex biostatistical models into actionable medical insights.

Previously, I was a Postdoctoral Research Associate at the Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), University of Cambridge, working with Chris Wallace. In this role, I developed Outcome-Guided Spike-and-Slab Lasso Biclustering (OG-SSLB), an efficient Bayesian model-based biclustering method for gene expression data. My work included designing novel algorithms for large-scale genomic datasets and implementing scalable solutions within interdisciplinary teams.

Industry Experience & Applied Data Science

To complement my academic background with industry experience, I completed an internship at Public Health Scotland. I designed, built, and deployed an interactive R Shiny dashboard for the 2019 General Practice Workforce Survey. This end-to-end data science project involved:

• Processing, cleaning, and validating national healthcare datasets.
• Creating a dynamic online dashboard and statistical summaries.
• Deploying a production-ready application utilized by policymakers and healthcare professionals for workforce planning, accessible via the official Public Health Scotland publication.

Academic Background

I completed my PhD in Applied and Computational Mathematics at the University of Edinburgh, supervised by professors Konstantinos C. Zygalakis and Marcelo Pereyra. My doctoral research specialised in developing accelerated Markov chain Monte Carlo (MCMC) methods for Bayesian computation, specifically solving complex inverse problems in high-dimensional imaging spaces. Before this, I earned an MSc in Computational Applied Mathematics with distinction from the University of Edinburgh.

My career began at the Escuela Superior Politécnica del Litoral (ESPOL) in Guayaquil, Ecuador, where I obtained my Engineering degree and subsequently worked as a University Lecturer, teaching Calculus and Linear Algebra.

You can explore my full list of publications here.