I am a Ph.D. student in the Computer Security and Privacy Lab at the Paul G. Allen School of Computer Science at the University of Washington where I am advised by Yoshi Kohno and work closely with Earlence Fernandes.
While I am fascinated by a variety of security and privacy problems, I focus on the intersection of security and machine learning. In particular, I seek to understand how and whether adversarial examples pose a real threat to deployed systems. My research so far has shown that popular computer vision deep learning models are vulnerable to physical attacks with stickers that do not require digital access to the system. Some of that work has been covered by Wired, Ars Technica, IEEE Spectrum, and others.
I also take an active interest in the broader implications of this new adversarial capability. As a member of the Tech Policy Lab, I have co-authored a paper on the legal and policy consequences of tricking an automated system without compromising any traditional security mechanisms. I also enjoy participating in law and policy discussions around technology beyond their connections to my work. That is why I attend the lab's weekly meetings with scholars from different academic departments where we talk about the latest news around tech policy.
Before becoming a graduate student at UW, I completed a Bachelor of Science degree in Computer Science with a minor in Mathematics at Lafayette College in the beautiful city of Easton, Pennsylvania.