This project is aimed at developing a data-driven machine learning research program accompanied by an integrated undergraduate educational curriculum in data science and machine learning. The research team, consisting of faculty from Benedict College, a historically black college and university (HBCU), University of South Carolina, and North Carolina State University with complementary expertise in optimization, control theory, statistics, applied and computational mathematics, and engineering will develop new methods in machine learning and employ these methods in a variety of applications, including modeling malaria epidemics and diabetes; discovering constitutive laws and mechanisms for some selected materials and life science problems such as modeling heart tissues and designing synthetic ion separating membranes; incorporating machine learning modules into a hybrid multiscale model for simulating angiogenesis (angiogenesis is the physiological process through which new blood vessels form from preexisting vessels, formed in the earlier stage of formation and development of vascular system) of various organs and tissue constructs in 3D biofabrication. Additionally this project will create an innovative training program to educate undergraduate STEM students and to retool affiliated faculty in Benedict College to prepare them for data science and artificial intelligence related jobs and for conducting research using data-driven approaches in the future. The computing and learning laboratory will provide the necessary computing facility for participating faculty and students to carry out the research as well as educational activities.
The project team will focus on several application problems that can be solved and improved using data science and machine learning tools: (1) using a multi-objective optimization approach to improve machine learning outcome in clustering, feature selection, knowledge extraction, and ensemble generation in modeling malaria epidemics and diabetes disease; (2) using Grey models to improve predictions in financial analysis; (3) developing forecasting models for disease epidemics including malaria and other diseases; (4) discovering constitutive laws and mechanisms in selected materials and life science problems such as stress-strain constitutive relations based on deep neural networks for heterogeneous heart tissues and organs and ion transport mechanisms in synthetic ion separating membranes; (5) coupling machine and deep learning tools to calibrate interaction energies and accelerate Monte Carlo simulations in a hybrid multiscale model for angiogenesis.
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.
