Developing Secure Privacy Preserving and Causal Genetic Alteration Research in Building an Innovative Systematic Pedagogy for Integrated Research and Education: The INSPIRE Model

To meet current keen demand for producing next-generation workforce equipped with skills and expertise in big-data analytics, we developed an innovative systematic pedagogy for integrated research-education (INSPIRE model) that is centered around two great challenges: (1) Transforming multidisciplinary STEM training so that it enhances emerging problem-solving capacity and (2) Training STEM students how to have a bigger hand in performing large-scale scientific work. To help strengthen the problem-solving skills and leadership abilities of STEM graduates, we reform the current STEM research training in Bioinformatics and CIS (Computer and Information Sciences) so that it helps us reach the goal of catalyzing science and research training. Our main research hypothesis is that critical improvement in the way big-data scientists are trained comes not solely from large-scale data mining but, in addition, comes from developing useful machine learning and artificial intelligence techniques that automate intelligent learning derived from big-data. The INSPIRE model was built by enablers in the scientific community, and indeed, by the community at large, to help resolve the scarcity of those Professionally Skilled / Trained in Big Data analytics (PSTBD) issue by equipping students with a versatile cross-disciplinary skill set. There is a dire need for those of us in the scientific and academic community to be able to transfer our own successes into perfecting the feedback-based machine learning - cognitive science INSPIRE model, one that places a heavy emphasis on providing individualized training to individuals from all walks of life, including large populations of minorities and women, so that all efforts are made as collaboratively as possible, and the benefits of the sewn seeds may be reaped by everyone. We integrate our secure privacy preserving and causal genetic alteration research at single-cell resolution to demonstrate the model. On an even grander scale, we enhance the PSTBD research by developing the INSPIRE model so that broader social impacts can be made by such newly created fields as Systems Genomics at single-cell level and fields fostered by creative cross-disciplinary genomic big-data analytics (http://americancse.org/events/csce2017/keynotes-lectures/yang-talk) with catalyzed learning-research synergies.