Nicolas Papernot

Nicolas Papernot
Photo by Matthew Tierney.

I am 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. I am also a faculty member at the Vector Institute where I hold a Canada CIFAR AI Chair, and a faculty affiliate at the Schwartz Reisman Institute.

My research interests are at the intersection of security, privacy, and machine learning. If you would like to learn more about my research, I recommend reading the blog posts I co-authored on, for example about proof-of-learning, collaborative learning beyond federation, dataset inference, machine unlearning, differentially private ML, or adversarial examples.

I was named an Alfred P. Sloan Research Fellow in Computer Science in 2022, a Member of the Royal Society of Canada College in 2023, an AI2050 Early Career Fellow By Schmidt Sciences in 2024, and received the McCharles Prize for Early Career Research Distinction in 2024.

My research has been cited in the press, including the BBC, New York Times, Popular Science, The Atlantic, the Wall Street Journal and Wired. I co-founded and served as a Program Committee Co-Chair of the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) in 2023 and 2024. I earned my Ph.D. in Computer Science and Engineering at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, I joined Google Brain for a year; I continue to spend time at Google DeepMind.

Email: [email protected]

Office: Pratt 484E and SRIC (the Vector Institute lobby is on the 11th floor)

Mail/Packages: 10 King's College Road, Room SFB540, Toronto, ON M5S 3G4, Canada

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Recent & selected older publications

A complete list of publications is available in my CV.

2022 & earlier

Research group

Current students and postdocs
Past students and postdocs
Information for prospective graduate students and postdocs

Research Talks


Here is a list of talks I will be giving. Feel free to reach out if you will be attending one of these events and would like to meet.

Past Recorded Talks

These video resources are a good overview of my research interests.

Machine Unlearning
Randomization in Trustworthy ML
Trustworthy ML
Lecture on ML security and privacy
Privacy-preserving ML
Adversarial examples

Blog Posts

Here is a list of blog posts discussing some of the research questions I'm interested in: