Research Areas:
- Biostatistics
- Machine/Statistical Learning
Our goal is to use experimental and statistical tools to construct a high dimensional picture of gene regulation, including cis and trans control of the full repertoire of RNAs expressed by cells. Currently, we are focusing on the function and biogenesis of circular RNA, which we recently discovered to be a ubiquitous and uncharacterized component of eukaryotic gene expression. A second major goal is to study gene expression variation in human cancer. Here, we combine mining massive public datasets, and experimental study of primary tumors and cell lines with bioinformatic and statistical methods. We use the cancer genome as window into functional roles played by RNA, and are attempting to characterize potential biomarkers.
Research Areas:
- Bioinformatics
- Genetics
- Machine/Statistical Learning
- Computational Biology
The Rivas lab is based in the Department of Biomedical Data Science. We are a new lab with a focus on population analytics using genomic and phenotype data. We will develop statistical models, algorithms, and computational tools for the analysis of millions of samples. Scientific themes that the lab will focus on: 1) Generating effective therapeutic and preventative hypotheses for human diseases from human genetic, imaging, wearable sensor, and environmental data; 2) developing technologies for integrated learning healthcare systems with a particular focus on underserved communities and developing regions of the world; 3) genetic epidemiology where the aim is to understand the global distribution of common and rare disease predisposition genes; and 4) high dimensional methods development and optimization.
Research Areas:
- Human Genetics
- Statistical Genetics
- Learning Healthcare Systems
- Biostatistics
I seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics. My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory.
Research Areas:
- Machine/ Statistical Learning
- Computational Biology
- Genetics
- Evolutionary Genetics
- Statistics