Student teams will develop a benchmark dataset and explore its efficacy in an in house competition where they will put new innovative techniques such as machine learning to the test through a series of challenges. A team of students will develop benchmark data pertaining to network performance in the presence of intentional and non-intentional degradation, ranging from sensor failure and additive noise to adversarial interference. The students will analyze the baseline performance of the network, and measure performance of the degraded network with and without the inclusion of robust techniques that shore up robustness. Students will have the opportunity to present findings to scientists & engineers from the Air Force Research Laboratory.
Faculty leads: Robert Calderbank, Vahid Tarokh, Ali Pezeshki
Client leads: Dr. Lauren Huie, Dr. Elizabeth Bentley, Dr. Zola Donovan, Dr. Ashley Prater-Bennette, Dr. Erin Trip
Project Manger: Suya Wu