I am currently a Ph.D. student in the Department of Bioengineering at the University of Utah where I work in Dr. Orly Alter’s Genomic Signal Processing Lab

We draw from mathematics, biology, and medicine to analyze large-scale genomic datasets using generalizations of the singular value decomposition (SVD). In physics, the SVD describes the activity of a prism, which splits white light into its component colors. In our lab, we use this matrix decomposition to split complex biological signals, such as genomic data measured on a microarray, into its different components. This allows us to separate components that correspond to the driving biological mechanisms of the system from components that correspond to experimental artifacts.  

Since the SVD is limited to a two-dimensional matrix, we develop generalizations of the SVD that allow for the decomposition of the higher-order datasets we often find in biology. With these new mathematical frameworks, we can gain insight into the genomic features of various cancers, such as brain or ovarian cancer, and how they relate to clinical outcomes. 

Our goal is to provide clinicians with a personalized prognostic and diagnostic laboratory test that can be used to predict both the patient's survival and response to treatment.