Data Science Research Group
The Data Science Research Group investigates computational advances to the fields of machine learning, computational intelligence, cloud computing, and e-Science. It is directed by Dr. Paul Anderson (PI). The following are examples of ongoing projects:
We introduce Learn2Mine, an education and analysis platform that integrates state-of-the-art data mining tools with effective feedback and training mechanisms in order to lower the barrier for domain experts and computer scientists to learn data science. Data science is the combination of statistical and computer science techniques in order to extract meaningful information from domain-specific datasets. Learn2Mine is a platform where students learn and practice techniques commonly used by data scientists. The Learn2Mine platform is a novel environment for teaching data science without requiring prerequisite knowledge, and with the idea that all knowledge bases can be enhanced by data science. It applies the principles of gamification, making the learning process more engaging and rewarding. Learn2Mine has been piloted by undergraduates, which, through the ability to retry lessons and receive instant feedback, has allowed them to engage in more sophisticated data science concepts than previous semesters. The next step for Learn2Mine, which will be continuously extended with new algorithms and lessons and completely open to the public beginning January 2014 (http://learn2mine.appspot.com), is the completion of an extension framework giving international institutions and organizations of higher learning the ability to create their own lessons for students to perform.
Machine Learning Algorithms for Biomedical Informatics
More information coming soon
Director: Paul Anderson, Ph.D.
2014 - present: Clayton Turner, Jacob Dierksheide, Thomas Nash, and John Lloyd
2013 - 2014: Clayton Turner and Jacob Dierksheide
2012 - 2013: Clayton Turner