Planning: HBCU-UP: Strengthening Data Science Research Capacity and Education Programs through Academia-Industry Partnership

The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) provides support to strengthen STEM undergraduate education and research at HBCUs. To fill a gap between academia and industry in the field of data science, the project team at the Florida A&M University-Florida State University (FAMU-FSU) College of Engineering will develop an academia-industry partnership model in data science research and education in collaboration with the Operations Research and Advanced Analytics (ORAA) Group at American Airlines (AA). The project activities across research and education are designed to benefit both undergraduate and graduate students who are interested in pursuing a career related to data science. The existing industrial engineering curriculum at the FAMU-FSU College of Engineering is expected to be strengthened, especially two core courses, namely Big Data Analytics in Engineering and Introduction to Machine Learning. This project will also support faculty investigators in expanding their research portfolio to aviation, a sector critical to the prosperity and competitiveness. The following specific objectives will be pursued under this project: (1) To design and launch an annual case competition focusing on aviation data analytics for undergraduate and graduate students; (2) To create an aviation data science pathway for underrepresented engineering students through industry-led and student-centered mentoring and professional development training; and (3) To advance the practice of airline operations through joint efforts by graduate students, engineering faculty, and industry professionals, using advanced data analytics. A few methodological contributions in machine learning and mathematical programming would be made after successfully addressing a few key research topics on prediction of checked baggage at flight level, aircraft configuration and load planning, and long-term demand prediction for spare parts, among others. 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.