This REU Site award to the University of Tennessee at Chattanooga, located in Chattanooga, TN, will support the training of 10 students for 10 weeks during the summers of 2019 to 2021. This REU focuses on interdisciplinary computational biology (iCompBio) training for college sophomores and juniors in STEM majors. The student application includes an application form, transcripts, a personal statement, a resume, and 2 letters of recommendations. Applications will be reviewed by a team of faculty research mentors. Students will learn various computational methods and apply them to address biological questions. Each student will be jointly mentored by a computer science mentor and a biology mentor. A one-week computing bootcamp will be provided to teach students essential data science using R, and advanced data processing and deep learning methods using Python. The biological research topics include aging and longevity, ecological networks, biodiversity, microbial pathway evolution, aquatic ecology, environment sustainability, nanoparticle fertilizers, field ecology, herbarium digitization, protein structures, and cell membrane structures. The computing training includes coding, modeling, simulation, deep learning neural networks, computer vision, parallel computing, statistics, data visualization, and mobile App development. Students will also go through training in ethics and responsible conduct for research.
It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in iCompBio. Students will learn how to conduct reproducible computational research, and will make their coding projects publicly available through GitHub. Students will also be encouraged to present their research results in scientific meetings.
A common web-based assessment tool, SALG URSSA, used by all REU Site programs funded by the Division of Biological Infrastructure will be used to determine the effectiveness of the training program. Students will be tracked after the program in order to determine their career paths. Students will be asked to respond to an automatic email sent via the NSF reporting system. More information about the program is available at http://utc.edu/icompbio or by contacting the PI (Dr. Hong Qin at hong-qin@utc.edu ) or the co-PI (Dr. Soubantika Palchoudhury at soubantika-palchoudhury@utc.edu).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
