I am passionate about using data and analytics to improve how people make decisions in a variety of industries, from finance to medicine to advertising to search.
Previously, I led teams at Goldman Sachs that centralized, analyzed, and presented insights from large financial data sets in order to reduce cost, mitigate risk, improve client service, and maximize profitability.
As part of my training, I completed a dual-doctorate degree (MD/PhD) as part of the Medical Scientist Training Program at Stanford. During my electrical engineering PhD, I worked to create cutting-edge machine learning algorithms to find trends in large quantities of parallel data and make predictions in real-time.
I am interested in learning more about any opportunity that allows me to lead people equally excited to leverage the burgeoning field of machine learning to change the face of industry.
Management: leading multiple global teams (>50 HC) through periods of great change and expansion;
Medicine: firsthand experience in healthcare delivery; understanding of doctors' and patients' desires and needs;
Engineering: machine learning, Bayesian inference, prediction methods, expert programmer (C/C++, Matlab, etc.); signal processing; systems analysis, including feedback and control;