NSF REU Site: Materials Research with Data Science

The NSF REU Site: Materials Research with Data Science (MAT-DAT) will strive to provide undergraduate students with training and hands-on experience in data science through their involvement in cutting-edge materials engineering projects within the NC State University community. Projects will integrate machine learning, materials informatics (MI), statistical and mathematical methods, and other data-science tools in experimental and computational-based materials discovery.

This REU site is focused on improvements of student knowledge and experience in cutting-edge experimental and computational characterization techniques and the application of MI tools that can be used to guide the discovery of novel materials. Through the collection of data and application of MI techniques and principles to their research projects, the students will elucidate new perspectives of engineered materials systems and will be able to directly apply this knowledge to the improvement of system designs and/or the investigation of novel materials applications. The REU site activities are designed to connect students to the data science and materials informatics methods and approaches of their research and to help prepare them for an excellent career in STEM.

THE EXPERIENCE

All of the selected research activities are designed to introduce undergraduates to the application and development of data science techniques for materials engineering problems. The REU students will spend the first week taking a hands-on course in data science and materials informatics and the rest of their time conducting faculty-mentored research.

For each research project, faculty advisors will provide direction of the project, supervise the progress of the project, review and engage with participants during specific professional development sessions. Graduate student mentors will work closely with each participant, providing guidance in the execution of specific tasks related to the research. Through interactions with graduate mentors, participants will gain exposure to the environment and expectations of graduate research, as they will engage with lab-group activities including research group meetings and joint meetings with their faculty advisor and graduate mentor. To further foster relationships among the REU participants, graduate mentors, and faculty advisors, team-building activities will be integrated into the REU experience.