Yi Li is a Professor of Biostatistics and Director of the Kidney Epidemiology and Cost Center. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. He is developing methodologies for analyzing large-scale andhigh-dimensional datasets, with direct applications inobservational studies as well in genetics/genomics. His methodologic research is funded by various federal grants starting from year 2003. Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics.
Research in my lab occurs within a multidisciplinary and translational space that promotes greater understanding of issues in public health, clinical rehabilitation, human performance, and physiology. My specific research interests have been devoted to physical activity epidemiology and behavioral interventions for the treatment/prevention of obesity and related cardiometabolic diseases, frailty, functional motor declines, and early mortality. Although predictive models based on healthy cohorts have a certain degree of generalizability, it is necessary to better understand populations at heightened risk. Our current and future research efforts are therefore directed at understanding and identifying precision strategies to prevent metabolic dysregulation and secondary musculoskeletal pathology among children and adults with neuromuscular impairments, as well as a variety of frailty syndromes. Our primary data collection occurs through typical clinical and basic laboratory studies, high throughput imaging, and remote sensing/tracking of human movement and various biomarkers. Numerous secondary, large-data analysis efforts are also incorporated for epidemiologic studies utilizing nationally-representative samples.
Data science applications: Connecting statistical models and disease data; design of behavioral interventions with remote sensing and messaging; understanding pediatric cardiometabolic and muscle health; sports analytics and human performance; population-based epidemiology/surveillance; secondary disease disparities among acquired and chronic frailty syndromes
I am currently developing scalable methods for the estimation and inference of large covariance and precision matrices from temporally dependent data, focusing on the voxel-level brain connectivity. I am also involved in analyzing imaging data for Alzheimer’s disease, large healthcare data for the end stage renal disease, large epidemiological cohort data, and data from radiology studies.