Nicholas Carlini is a research scientist at Google Brain working at the intersection of machine learning and computer security. His most recent line of work studies the properties of neural networks from an adversarial perspective. He received his Ph.D. from UC Berkeley in 2018, and his B.A. in computer science and mathematics (also from UC Berkeley) in 2013.
Generally, Nicholas is interested in developing attacks on machine learning systems; most of his work develops attacks demonstrating security and privacy risks of these systems. He has received best paper awards at ICML and IEEE S&P, and his work has been featured in the New York Times, the BBC, Nature Magazine, Science Magazine, Wired, and Popular Science. Previously he interned at Google Brain, evaluating the privacy of machine learning; Intel, evaluating Control-Flow Enforcement Technology (CET); and Matasano Security, doing security testing and designing an embedded security CTF.