Nicolas Papernot

Nicolas Papernot

I am an Assistant Professor at the University of Toronto, in the Department of Electrical and Computer Engineering and the Department of Computer Science. 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. I was named an Alfred P. Sloan Research Fellow in Computer Science in 2022 and a Member of the Royal Society of Canada College in 2023.

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 cleverhans.io, for example about proof-of-learning, collaborative learning beyond federation, dataset inference, machine unlearning, differentially private ML, or adversarial examples.

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 currently serve as a Program Committee Chair of the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), which I co-founded in 2023. 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.

2024
2023
2022 & earlier

Research group

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

Research Talks

Upcoming

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.

Randomization in Trustworthy ML
Trustworthy ML
Deepfakes
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:

Teaching