Nicolas Papernot

Nicolas Papernot

I am an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Toronto. 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 machine unlearning, differentially private ML, or adversarial examples.

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 spent a year at Google Brain in Úlfar Erlingsson's group.

Email: [email protected]

Office: Pratt 484E

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

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Selected publications

A complete list of publications is available in my CV.

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.

Tempered Sigmoids for DP-SGD
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:

Reza Shokri and I put together a list of publications on trustworthy ML here. We selected different sub-topics and key related research papers (as starting points) to help a student learn about this research area. There are so many good papers which are being published in this domain, so this list is by no means comprehensive. Papers are selected with the intention of maximizing coverage of the techniques introduced in the literature in as few papers as possible.