Ph.D., Clemson University
Dr. Albright is a research statistician developer at SAS Institute where he has been a key contributor to the development of the SAS Enterprise Miner/Text Miner product. His experience includes algorithm development for matrix factorizations, collocation detection, and large-scale inverted index manipulation. Recently, he has made contributions to a massively parallel, gridded approach to mining big data. Albright also has authored several applied papers on using SAS Enterprise Miner/Text Miner in the business setting. The publications have focused on both supervised learning—including such topics as building predictive models for topic categorization and sentiment classification—and unsupervised learning, such as web page clustering and topic discovery.
Ph.D. candidate, University of South Dakota
Professor Wallinga is the director of institutional research at Northwestern College, where he analyzes the college’s data using the R and SQL programming languages, SPSS, Excel, and other tools. He teaches in Northwestern's math and computer science departments and is completing his doctorate with a dissertation that involves applying interval analysis to neural networks. Wallinga has industry experience as a database programmer and systems analyst, with a focus on data analysis, visualization, and dashboard development.