Dr. Nicolas Papernot is an Assistant Professor of Electrical and Computer Engineering at the University of Toronto and a Faculty Member at the Vector Institute where he holds a Canada CIFAR AI Chair. His research interests are broadly at the intersection of computer security, privacy, and machine learning.
Professor Papernot earned his PhD degree in Computer Science and Engineering from the Pennsylvania State University supported by a Google PhD Fellow in Security and Privacy. His PhD research focused on characterizing the attack surface of machine learning systems and inventing defense mechanisms to improve their security and privacy. Prior to joining the University of Toronto, Nicolas spent a year as a research scientist at Google Brain.
His work has been applied in industry and academia to evaluate and improve the robustness of machine learning models, to input perturbations known as adversarial examples, as well as to deploy machine learning with privacy guarantees for training data at industry scale. He was invited to write technical articles for the CACM and IEEE Security and Privacy Magazine. Nicolas is a program committee member of ACM CCS, IEEE S&P, and USENIX Security. He also reviews for ICML, ICLR, and NeurIPS. He has chaired or co-organized workshops on security and privacy for machine learning at ICML, DSN, and NeurIPS.