Special Task Force Keynote: Professor Mary Yang

by kaveh@ucmss.com — last modified Dec 07, 2017 07:40 PM
Developing Novel Computational Intelligence Approaches for Innovative
Systematic Pedagogy in Integrated Research and Education (INSPIRE)

Professor Mary Yang
Director of MidSouth Bioinformatics Center
Director of Joint Bioinformatics Ph.D. Program, George Washington
Donaghey College of Engineering & IT, University of Arkansas at Little Rock and
University of Arkansas for Medical Sciences, USA

In collaboration with Professor Wenbing Zhao, Director of Doctoral Program in Electrical Engineering and Computer Science (EECS), Cleveland State University, USA and Professor Hamid R. Arabnia, Editor-in-Chief of Journal of Supercomputing (Springer . Nature), Department of Computer Science, University of Georgia, USA
Date, Time, Location: TBA


To meet current 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.

                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. Our frameworks focus on using integrative research technologies to help solve Education’s Performance Prediction Data Mining Crisis (EPPDMC), by putting to rest issues associated with mining and making best use of big data for educational enhancement, such as multi-source education acquisition, data fusion, and unstructured data analysis. We exploit the uses of deep learning, text classification, and semi-supervised learning approaches to solve challenging problems that educators face when analyzing multiplatform big data involved in education, research and training students.

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 build novel, eclectic, and insightful frameworks based on machine learning and computational intelligence approaches for improving integrated research-education. Based on vast availability of education data available to us, not only can we utilize structured, unstructured, and even multi-media data, but while engaging in leaning intelligent thinking along the way, we can also maximize the utilization of big data by studying the motion and performance of these data. We integrate our secure privacy preserving and single-cell genomics research to demonstrate the effectiveness of the novel computational intelligence approaches. The INSPIRE model investigates class content and student learning status simultaneously to address the EPPDMC issues as it is being deployed to achieve personalized education and precision teaching. The model is also a practicable and earnest attempt at using cutting edge “smart” technologies that set our society apart by fostering opportunities for teaching and learning. 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 resolution and fields fostered by creative cross-disciplinary genomic big-data analytics with catalyzed learning-research synergies