The objective of this proposal is to expose bright and motivated undergraduates who aspire to pursue advanced careers in Computer Science to active research experience early in their careers. This proposal seeks to develop an REU site to broaden the intellectual horizon of participants through exposure to opportunities available in university research. Students will be involved in research projects in deep learning and its applications, in particular, natural language processing, bioinformatics and computational medicine. Students will be exposed to the fundamentals as well as latest advances in deep learning and its applications via lectures and personalized readings. They will perform hands-on research on novel problems, conduct experiments, and communicate their results through written papers and presentations. The REU students will be involved in research natural language processing and deep learning and its applications, alongside faculty mentors and graduate students in a university environment. The project will involve undergraduate students in cutting-edge research where they will write software to solve interesting and timely problems and write papers for publication. The proposal seeks to increase the number of American citizens and permanent resident undergraduates who are attracted to careers in research and advanced studies in Computer Science. Training in theoretical and empirical deep learning and its applications will enable participants to contribute to ubiquitous software-based technologies at the highest levels in innovative ways. The proposal will also focus on training future computer scientists from institutions with limited research opportunities including community colleges, women and under-represented minorities. The research experience will encourage these students to be productive researchers in academic and non-academic environments during their future careers.
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.