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Nicolas Papernot is an Assistant Professor at the University of Toronto, in the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Faculty of Law. He also holds a Canada CIFAR AI Chair at the Vector Institute, and is a faculty affiliate at the Schwartz Reisman Institute. His research interests span the security and privacy of machine learning. Some of his group’s recent projects include generative model collapse, cryptographic auditing of ML, private learning, proof-of-learning, and machine unlearning. Nicolas is an Alfred P. Sloan Research Fellow in Computer Science and a Member of the Royal Society of Canada’s College of New Scholars, and an AI2050 Schmidt Sciences Early Career Fellow. His work on differentially private machine learning was awarded an outstanding paper at ICLR 2022 and a best paper at ICLR 2017. He co-created the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) and is co-chairing its first two editions in 2023 and 2024. He previously served as an associate chair of the IEEE Symposium on Security and Privacy (Oakland), and an area chair of NeurIPS. Nicolas earned his Ph.D. at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, he spent a year at Google Brain where he still spends some of his time.