Posts classified under: Faculty

Tina Hernandez-Boussard

Research Areas: 

  • Biomedical Informatics
  • Clinical Informatics
  • Epidemiology
  • Health Policy

Olivier Gevaert

My lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, we developed computational algorithms for the identification of driver genes using multi-omics data. Furthermore, we are working on multi-scale biomedical data fusion methods, bridging the molecular using omics data, cellular using pathology data and tissue using medical imaging data.

Research Areas: 

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Manisha Desai

Research Areas: 

  • Biostatistics
  • Missing Data
  • Generalizing Trial Findings to Target Populations
  • Incorporating Mobile and Digital Health Data Into Clinical Trials
  • Design of Pragmatic Clinical Trials

Euan A. Ashley

The Ashley lab is focused on the application of whole genome sequencing to the medical care of individuals and families. We lead the Stanford Center for Inherited Cardiovascular Disease, one of the few medical centers in the country where patient genome sequences can be readily incorporated into clinical care. In 2010, we led the team of BMI faculty that completed the first clinical interpretation of a human genome. We extended this to a pipeline that would handle families in 2011. We are also fascinated by network biology. Part of the Stanford heart transplant team, we are focused on understanding the heart’s response to disease or exercise stress. We are part of a team of three major transplant centers that was recently awarded $9m to explore the genetic control of cardiac transcriptional activity via RNA sequencing and network modeling. Finally, although many of our questions can be answered in silico, to establish causality, we turn to the wet lab to explore the biology of key genes and signaling modules.

Research Areas: 

  • Genetics & Genomics
  • Multi-Omics
  • Machine Learning
  • Deep Learning
  • Digital Health
  • Human Performance
  • Network Biology