I research new computational methods to predict the diagnosis and outcome of infectious disease patients, using demographic, clinical, and genomic data. My goal is to find ways to make these predictions useful to doctors at the clinic, or to the patients themselves. I also work on visual exploration tools to identify relevant patterns and predictors in biomedical datasets.
My original background is in mathematics applied to the simulation of biopolymers (proteins and RNA). I obtained my PhD from the department of Mathematics at the Universidad Nacional del Sur in Bahía Blanca, Argentina, in 2002, for my work on the coarse-graining of stochastic processes to describe the folding of protein molecules (thesis pdf, in Spanish).
Differentially Private Marchine Learning
Unbiased visual exploration of biomedical data
Predictive modeling of infectious disease
GPU-based visualization of large GIS datasets
- Ab-initio Protein Folding Prediction
I will be adding more information about these projects in the coming weeks.