His current focus areas in the data sciences are: advanced analytical modeling of real-time processes; elementary AI applications; data fusion and Total quality Data Management (TqDM), statistical visualization; real-time predictive analytics, and real-time statistical process control (SPC). Specific applications in the data sciences are focused on real-time predictions of product attributes for sustainable biomaterials using a host of algorithms that process in parallel. Past research projects have resulted in successful prediction of tensile strength and modulus of sustainable biomaterials in validation within 10% RMSEP. Algorithms currently being studied are Bayesian Additive Regression Trees (BART), deep learning NN with generalized bounds, bootstrap forests and boosted trees. Variable preselection using genetic algorithms (GA) with deep learning NN is also under investigation. Real-time data fusion and TQDM are essential foundations to advanced analytical modeling of real-time processes. Studies are ongoing in the fusion of multiple networked data sources with varying record lengths and time-lagging. Imputation methods are also being studied as a critical component of TQDM. Statistical process control (SPC) applications include: 'control bands for data signatures and footprints'; multivariate control charts; and the development of autocorrelated control charts. He teaches highly successful industry courses in ‘Advanced Analytics and Data Mining,’ ‘Process Analytics, Statistical Process Control | Lean Methods,’ and ‘Design of Experiments.’