With the increasing dependence of biological and biomedical research on large data sets such as those for bioinformatics or CIS systems, it is becoming apparent that the well-trained biologist needs to have a strong background in computation and computer modeling, statistics and other quantitative analysis. In contrast, the stereotypical biology student tends to be the least well mathematically prepared of students in the STEM disciplines, a situation sometimes catered to by traditional Biology courses that all but eliminate quantitative aspects of the field. We propose to begin to reverse this situation by developing a revised Biology curriculum with a strong, quantitative framework and with integral interdisciplinary ties to Mathematical Science and Computer Science. In particular, we will revise and coordinate a consistent analytical approach, including computer modeling, statistical analysis, quantitative representation, and relevant case studies in an active learning environment in all Biology degree required core classes of the first two years in Biology, Mathematics, Statistics, and Computer Science. In addition, we will develop upper division bridging courses to take interested students from each of the three disciplines and provide sufficient cross-training for them to continue graduate studies in an interdisciplinary field such as Bioinformatics. All course revisions will be reviewed by an interdisciplinary panel of faculty for consistency and accuracy in all fields. We will also provide training in active learning and other pedagogical techniques for all involved faculty. We expect these approaches to result in a greater number of students better prepared for research careers in Biology and Biomedical Science.
|Nguyen, Hung T; Kreinovich, Vladik (2014) How to Fully Represent Expert Information about Imprecise Properties in a Computer System - Random Sets, Fuzzy Sets, and Beyond: An Overview. Int J Gen Syst 43:586-609|
|Stein, M; Beer, M; Kreinovich, V (2013) Bayesian Approach for Inconsistent Information. Inf Sci (Ny) 245:96-111|