Posts classified under: Advising Faculty

The Witte Lab is a computational and epidemiological research group investigating the genetic and environmental contributions to disease risk and progression. We develop and apply novel genetic epidemiological methods to decipher the mechanisms underlying complex diseases. Current areas of research include the following: 1) Evaluating disease risk with novel approaches to polygenic risk scores and hierarchical models; 2) Assessing the shared genetic basis across different diseases and traits (pleiotropy); and 3) Finding genetic risk factors, improving screening, and reducing disparities in cancer.

Research Areas: 

• Bioinformatics
• Statistical Genetics
• Cancer Risk Prediction
• Genetic Epidemiology
• Epidemiology Methods

Dr. Langlotz is Professor of Radiology and Biomedical Informatics and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center), which supports outstanding interdisciplinary artificial intelligence research that optimizes how clinical images are used to promote health. As Associate Chair for Information Systems and a Medical Informatics Director for Stanford Health Care, he is also responsible for the computer technology that supports the Stanford Radiology practice, including 7 million imaging studies that occupy 0.7 petabytes of storage. Read more about rhe Center for Artificial Intelligence in Medicine & Imaging.

Research Areas:

• Design algorithms that detect and classify disease on medical images
• Enabling the clinical use of ideas conceived in the laboratory
• Develop natural language processing methods that use narrative radiology reports to create large annotated image training sets for supervised machine learning experiments

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

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