Tom Chen is an Instructor in the Department of Population Medicine at the Harvard Pilgrim Health Care Institute. His research areas encompass topics in missing data, correlated data, and statistical computing. These three topics are becoming increasing intertwined in cluster randomized trials and EHR data, which are often afflicted with complex inter-dependencies and missing observations. When not accounted for, missing data and correlated data lead to parameter bias and inefficiency. Furthermore, statistical estimation procedures for such complex data are often computationally expensive, and algorithms which are fast and accurate are greatly desirable. His previous research includes missing data adjustments in estimating the intraclass correlation coefficient (ICC) in cluster randomized trials, missing data adjustments for recurrent event data, computationally efficient algorithms in fitting generalized estimating equations (GEE), and flexible linear mixed models.