IOE-STAGE SITE
The U-M IOE Department in partnership with the School of Education welcomed the Early College Alliance to a day of learning about the field of engineering. The program was organized to promote learning and interest in machine learning and applied math within the local community.
Earlier this month the University of Michigan Industrial and Operations Engineering (U-M IOE) Department in partnership with the U-M School of Education (SOE) welcomed the Early College Alliance (ECA) to a day of learning about the field of engineering.
The program was organized by Salar Fattahi, U-M IOE Assistant Professor, and Tiffany Wu, U-M SOE PhD student to promote learning and interest in machine learning and applied math within the local community. ECA students encountered a full day of activities, including a visit from the U-M IOE Department Chair, Brian Denton, a Q&A session with current undergraduate students, a Human-AI Interaction Workshop, and a tour of the new Ford Robotics Building.
“It was a fantastic experience working with such a young and bright group of students on emerging topics in machine learning, optimization, and matrix algebra,” said Fattahi. “By drawing connections to Netflix and its recommendation algorithm, we hope to have shown these young students the importance of applied mathematics and optimization in modern data-driven applications. Our ultimate goal is to foster cross-disciplinary collaboration between the U-M IOE, SOE, and local high schools around Michigan.”
The ECA is an educational program that aims to immerse high school students into a postsecondary learning environment. Located on the campus of Eastern Michigan University the ECA is an early/middle college program that partners with schools in Van Buren, Washtenaw, and Wayne County.
“Our hope is to establish a lasting partnership with ECA and to extend our machine learning workshop to other schools in the future, particularly schools that serve students from low-income families who may not have access to these types of opportunities,” said Wu. “We hope to broaden students’ perspectives on the real-world applications of machine learning and inspire future innovators on how machine learning methods can be used to address the most pressing challenges of our time.”