I am a postdoctoral fellow at the University of California, Los Angeles. I investigate how DNA methylation dynamics change in response to aging and health status. DNA methylation has emerged as a useful proxy for measuring the physiological state of an organism. DNA methylation is dynamic, changing over time in response to environmental stimuli, yet can also be stable for relatively long periods. Thus, an individual who continuously exercised and ate a balanced diet for years would have an epigenetic profile that reflected this continued behavior
The dynamic yet stable nature of DNA methylation is ideal for the development of biomarkers as information that is lost with more transitory signals, such as gene expression, is maintained. In my research with Prof. Matteo Pellegrini, I seek to understand how the epigenome is modeled in response to physiological stress and whether altered DNA methylation patterns can be used as biomarkers to predict health outcomes. My primary research is divided into two fundamental parts: building informatics tools to rapidly process and model methylation sequencing data, and using the tools I build to design and explore DNA methylation based predictive models.
In addition to my doctoral work, I am experienced in analyzing all types of genomics using machine learning and statistical methods. I am particularly interested in exploring the physiological response to cancer immunotherapies to understand contributors to relapse and how improved immunotherapies can slow, or prevent, relapse. I have been involved in two exciting projects that looked at epigenetic suppression of T-cell receptor expression during adoptive cell transfer therapy and another project that identified a ubiquitous cancer targeting T-cell.
Outside of work, I enjoy staying up to date on the latest developments in python, and spending time outdoors running, hiking, and skiing.