Posts classified under: Advising Faculty

My research areas include statistical genetics, risk prediction modeling, cancer screening, and health policy modeling. I have been developing various statistical methods to analyze large-scale genetic data to understand the interplay between genes and the environment for various complex disease including cancer and neurological diseases. My recent methodological papers were published in high-profile statistical journals including the Journal of the American Statistical Association and Biometrics. In addition to statistical genetics, I have worked on several cancer screening and health policy modeling projects, by developing stochastic simulation models utilizing/merging various data sources including cancer registry data, epidemiologic case-control or cohort data, and nationally representative data such as NHANES (National Health and Nutrition Examination Survey). I have a wide range of methodological projects that BMI students may be interested in learning and working on. Currently, I am advising several medical students and Neurosurgery residents in conducting research, which includes: SEER-Medicare data-based surgery outcome analysis, mutation profiling for lung cancer using Stanford EMR database, and meta-analysis of
substance use disorders.

Heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. He works on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. His interests span computational geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics, and discrete algorithms — all areas in which he has published and lectured extensively.

My lab is interested in leveraging the power of high-throughput sequencing methods to 1) understand rare genomic and epi-genomic heterogeneity at the level of cellular subpopulations and even single cells 2) investigate chromatin structure at the level of the 30-nm fiber, and 3) probe the relationship between primary sequence and functionality of macromolecules (RNA and protein). All of these research endeavors lie at the intersection of physics, engineering, and biology, and require the analysis of large, novel data sets.

https://profiles.stanford.edu/william-greenleaf

We study the regulation and evolution of gene expression using a combination of experimental and computational approaches. Our work brings together quantitative genetics, genomics, epigenetics, and evolutionary biology to achieve a deeper understanding of how genetic variation within and between species affects genome-wide gene expression and ultimately shapes the phenotypic diversity of life. Some of our long-term goals are to better understand: 1) How new mutations affect gene expression, 2) What selective pressures act on these mutations, 3) How (and how often) changes in gene expression affect other phenotypes, including human disease