- Computer Security
- Machine Learning
Nicolas Papernot has joined the Department of Electrical & Computer Engineering (ECE) at the University of Toronto as an Assistant Professor in Fall 2019. He is currently a research scientist at Google Brain working on the security and privacy of machine learning in Úlfar Erlingsson’s group. Nicolas received his PhD in Computer Science and Engineering at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship in Security. Prior to that, he received an M.S. and B.S. in Engineering Sciences from the Ecole Centrale de Lyon. He serves on the program committees of several conferences, including CCS, PETS and USENIX Security. He is also the Chair of the NeurIPS 2018 Workshop on Security in Machine Learning.
Professor Papernot’s research interests span the areas of computer security, privacy, and machine learning. Together with his collaborators, he demonstrated the first practical black-box attacks against deep neural networks. His work on differential privacy for machine learning, involving the development of a family of algorithms called Private Aggregation of Teacher Ensembles (PATE), has made it easy for machine learning researchers to contribute to differential privacy research. He also co-authored with Ian Goodfellow an open-source library called CleverHans, now widely adopted in the technical community to benchmark machine learning in adversarial settings..
- Google PhD Fellowship in Security
- Best Paper Award (5th International Conference on Learning Representations)