My background is biomedical informatics and machine learning applied to physiological time series data.
I work with the Department of Anesthesiology at UCSF to identify pre- and intra-operative predictors of postoperative cognitive dysfunction, as well as associated patterns of gene expression.
I did my PhD at Georgia Tech under the supervision of Gari Clifford and Shamim Nemati. We extracted features from heart rate and accelerometer data using signal processing, estimated illness from these features with machine learning, and improved performance by analyzing information during specific times, over several time scales, and between signals.
Application areas included sepsis, heart failure, atrial fibrillation, mental illness, and neurodegenerative disease.
We patented and licensed an algorithm to monitor PTSD via heart rate variability. We also patented an approach to convert physiological time series into graphical models.
My papers are available via Google Scholar.