I develop and implement statstistical methodology to solve for scientific problems under the advisement of Dr. Vadim Zipunnikov. My major research interest is related to Wearable Computing Device (accelerometers, actigraphs, heart rate monitor, etc.). Recently, these devices have gained their popularity in both academia and industry; however, accurately analyzing data generated by them remains a big scientific chanllange. Understanding this type of data, their distribution within person and in the population can help understand the association between these new measurements and human health.

My research focuses on high dimensional and high throughoutput accleromtry data. I develop novel statistical methogologies, and also conduct data analysis for multiple collaborative projects. Specificly, I am interested in the following directions: 1) feature engineering from raw data to summarize noisy and high dimensional physiological signal; and 2) integrative analysis of features acquired from multiple modalities/devices.

Scientific Interests: wearable devices and their applcations in public health (e.g. mental health and aging), physical activity assessment, sleep, circadian rhythmicity.

Statistical Methods: feature engineering from accelerometry signals, matrix and tensor decompositions, dimension reduction, functional data analysis, integration of multiple modalities.

Selected Research Projects

  • Processed the NHANES 2003 - 2006 Accelerometery Data: Processed high dimensonal and noisy NHANES 2003 - 2006 accelerometery data to a ready-to-use data package. Provided tutorial R examples for future researchers to conduct statistical analysis on NHANES accelerometery data.

  • Novel Statistical Framework to Quantify Activity Fragmentation: Developed a statistical framework to quantify patterns of sedentary/active time accumulation via metrics of fragmentation of bouts and studied their association with mortality in NHANES 2003-2006.

  • Collaborative Research Projects: multiple collaborative research projects including: monitoring hospitalized patients with various activity trackers, sleep fragmetation and its association with cognitive functions, circadian rhythm and its association with mortality etc.

WordCloud Representing My research