About me

I am a data specialist with expertise in Machine Learning, Bayesian statistics, and computational mathematics. My research focuses on developing efficient algorithms for complex data analysis and high-dimensional statistical inference.

I was born in Guayaquil, Ecuador, where I obtained my Engineering degree at the Escuela Superior Politécnica del Litoral (Polytechnic School of the Littoral - ESPOL). From 2013 to 2017, I worked as a University teacher at ESPOL, teaching Single and Multivariable Calculus, Linear Algebra, and Differential Equations, developing strong analytical and communication skills. In 2018, I obtained an MSc in Computational Applied Mathematics with distinction at the University of Edinburgh, followed by a PhD in Applied and Computational Mathematics in 2022, under the supervision of Konstantinos C. Zygalakis and Marcelo Pereyra.

During my PhD, I specialized in developing and implementing accelerated Markov chain Monte Carlo (MCMC) methods for Bayesian computation in imaging. My work focused on solving complex inverse problems in high-dimensional spaces, where I proposed novel sampling methods that achieved significantly faster convergence rates and higher accuracy compared to standard approaches. This research demonstrates my ability to tackle challenging computational problems and develop innovative solutions. You can explore my published works here.

To gain industry experience, I completed an internship at Public Health Scotland, where I developed and deployed an interactive R Shiny dashboard for the 2019 General Practice Workforce Survey. This project showcased my full-stack data science skills:

• Data processing and analysis of complex healthcare datasets
• Development of interactive visualizations using ggplot2 and plotly
• Implementation of dynamic filtering and statistical summaries
• Creation of a user-friendly interface for stakeholders
• Deployment of a production-ready application with downloadable reports

The dashboard, which is publicly accessible through the General Practice Workforce Survey 2019 publication, has been used by healthcare professionals and policymakers to make data-driven decisions about workforce planning.

I am currently a Postdoctoral Research Associate at the Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID) at the University of Cambridge, working with Chris Wallace. Here, I’m developing efficient Bayesian model-based biclustering methods for gene expression data, combining my expertise in statistical modeling with applications in computational biology. This work involves:

• Developing novel algorithms for high-dimensional data analysis
• Implementing scalable solutions for large-scale genomic datasets
• Applying machine learning techniques to biological problems
• Collaborating with interdisciplinary teams of researchers

My combination of strong mathematical foundations, programming expertise, and experience in both academic and industry settings makes me well-suited for roles in data science, quantitative research, and machine learning.