Dr. Manuel Rivas's Lab is located in the Department of Biomedical Data Science. Their main research focus is the genetics of chronic diseases.

They have developed multiple tools to analyze the genetics of chronic diseases. First, they developed statistical algorithms that optimize their ability to fit linear, logistic, and Cox regression with some penalties (allowing for sparse solutions) to large-scale datasets. Here, they refer to large-scale datasets as datasets that have very large , i.e. large number of individuals – on the order of millions to billions, and very large , i.e. very large number of predictors – on the order of millions to billions. This amounts to about a quadrillion data points. This is what is referred to as big data.

The model is that by aggregating large-scale datasets they can learn about disease including its underlying biology and ability to predict susceptibility and progression.

The Rivas Lab has developed multiple algorithms to scale down the number of datapoints to more manageable datasets while maintaining the same inferential and predictive power.