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

Russ B. Altman

Research Areas: 

  • Biomedical Informatics
  • Artificial Intelligence (AI)
  • Machine Learning
  • Pharmacogenomics
  • Structural Bioinformatics
  • Natural Language Processing
  • Knowledge Graphs
  • Drug Discovery
  • Pharmacology

James Zou

We develop a wide range of machine learning algorithms and are especially interested in extracting disease insights from population genomics and epigenomics. On the methodology side, we are investigating new approaches to adaptive data analysis, representation learning for bio-medical data, new probabilistic models that encourage diversity, and multi-view learning. Application topics include: whole-genome and exome sequence analysis, risk prediction, synthetic biology, chromatin dynamics and transcription regulation.

Research Areas: 

  • Machine/Statistical Learning
  • AI for Health
  • Computational Biology

Serena Yeung

Our group’s research develops artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. We have a primary focus on computer vision, and developing algorithms to perform automated interpretation and understanding of human-oriented visual data across a range of domains and scales: from human activity and behavior understanding, to human anatomy, and human cell biology. Current projects include computer vision for extracting insights and knowledge from visual data ranging from surgery and behavioral science videos, to cell imaging data.

esearch Areas: 

  • Machine/Statistical Learning
  • Deep Learning
  • Bioinformatics
  • Computer Vision
  • Medical Imaging
  • Computational Biology