Webinar Details

Early exposure to STEM outreach experiences is an immensely valuable way to motivate and inspire youth in underserved communities. These programs play an essential role in encouraging students from underrepresented groups to pursue an education and career in challenging areas that might otherwise seem out of reach. GEMS AEOP summer research program is a high school and undergraduate program designed to achieve these goals.

In this webinar, Dr. Michael Frye, Director of the Autonomous Vehicle Systems Research Labs at the University of the Incarnate Word (San Antonio, Texas) and the GEMS AEOP high school summer research program will discuss examples of the incredible research projects that students have tackled and share some important lessons learned. Additionally, he will highlight the unexpected advantages of using complex research-grade systems, such as Quanser platforms, to introduce exciting and challenging topics such as deep learning, reinforcement learning, and model predictive control to high school and undergraduate students that may not have any prior training and experience.

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Presenter’s Bio

Dr. Michael Frye is a Professor of Engineering at the University of the Incarnate Word located in San Antonio, TX. He is also the PI and Director of the Autonomous Vehicle Systems Research Labs and the GEMS AEOP High School Research Summer Program.

Dr. Frye has a background in flight controls and modelling of fixed and rotorcraft vehicles. Dr. Frye earned his Ph.D. in Electrical Engineering from the University of Texas at San Antonio in 2006 and received his tenure-track appointment at UIW in August 2007. The PI teaches courses in electrical engineering, programming, control theory, and artificial intelligence. Prior to joining UIW, the PI worked for Continental Airlines on predictive wind shear systems, Lockheed Martin Skunkworks on autopilot and flight controls, NASA Ames/Army Rotorcraft Directorate on rotorcraft modeling and control, and the Southwest Research Institute on object estimation and detection.