PhD Proposal: The Limitations of Deep Learning Methods in Realistic Adversarial Settings
Yigitcan Kaya is a fifth-year PhD student in the Computer Science department, advised by Tudor Dumitras. His research broadly focuses on studying the properties of machine learning methods in adversarial settings. He has published his work in top machine learning conferences (ICML, NeurIPS, ICLR) and has interned twice as an applied scientist at AWS. He is also a Fellow in the Future Faculty program organized by The Clark School.
This talk is organized by Tom Hurst