Dr. Garcia de la Garza's research analyzes high-dimensional datasets in neuroscience, neurology, and psychiatry. He is interested in developing methods for analyzing functional data, including dimension-reduction techniques to derive patterns that explain variability in functional datasets. He has worked on the use of machine learning techniques to analyze epidemiological psychiatric surveys.
He collaborates with investigators at Einstein to determine risk factors that influence short-term and long-term cognitive function derived from wearable devices that monitor physical activity, sleep, and air pollution. We are working on analyzing these novel sources of high-dimensional data using state-of-the-art statistical techniques to answer questions about the aging brain.