David Yang Ph.D.
Principal Investigator & Sceintist
As a doctoral graduate from UCLA – Dept of Biostatistics (2002), Dr. Yang has primarily worked as a Principal Investigator in developing data & computation solutions for biostatistics, bioinformatics and health information technologies (HIT). He has developed Bayesian inferential methods to serve various needs in Evidence-Based Medicine and/or Personalized Medicine. This includes Markov-Chain Monte Carlo (MCMC) algorithms for linear regression models with missing covariates (Yang, Belin, and Boscardin, 2005) , and for incomplete longitudinal data analysis (Yang, Li, & Shoptaw, 2008; Li, Yang, & Wu, 2007; Yang et al, 2007; Yang & Shoptaw, 2005), parametric and semi-parametric hierarchical modeling methods for profiling/ranking healthcare providers (Yang et al, 2013), an integrative Bayesian variable selection (iBVS) strategy for biomarker discovery with informative priors (Peng, et al., 2013), and etc. Recently, funded by National Institutes of Health (NIH), he is leading a team at Bayessoft to develop a set of software tools for EMA (ecological momentary assessment) and PHM (Personal Health Management) over the Cloud Computing and iOS/Android platforms.