Nicolas Papernot

Faculty Member

Assistant Professor, Department of Electrical & Computer Engineering, University of Toronto

Assistant Professor, Department of Computer Science, University of Toronto

Canada CIFAR Artificial Intelligence Chair

Nicolas Papernot is an Assistant Professor of Computer Engineering and Computer Science at the University of Toronto. 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. 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.

Research Interests

  • Computer Security
  • Machine Learning
  • Privacy


  • Faculty Affiliate, Schwartz Reisman Institute for Technology and Society
  • Member of the College of New Scholars of the Royal Society of Canada in 2023.
  • Co-founded the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).
  • Outstanding Paper Award (10th International Conference on Learning Representations)
  • Named an Alfred P. Sloan Research Fellow in Computer Science in 2022.
  • Canada CIFAR AI Chair in 2019
  • Connaught New Researcher Award
  • Early Research Award from the Ministry of Colleges and Universities
  • Google PhD Fellowship in Security
  • Best Paper Award (5th International Conference on Learning Representations)


Dataset Inference: Ownership Resolution in Machine Learning

Pratyush Maini and Mohammad Yaghini and Nicolas Papernot


Bad characters: Imperceptible nlp attacks

Nicholas Boucher and Ilia Shumailov and Ross Anderson and Nicolas Papernot


Manipulating SGD with data ordering attacks

Ilia Shumailov and Zakhar Shumaylov and Dmitry Kazhdan and Yiren Zhao and Nicolas Papernot and Murat A Erdogdu and Ross Anderson


Hyperparameter Tuning with Renyi Differential Privacy

Nicolas Papernot and Thomas Steinke


On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning

Anvith Thudi and Hengrui Jia and Ilia Shumailov and Nicolas Papernot


Markpainting: Adversarial Machine Learning meets Inpainting

David Khachaturov and Ilia Shumailov and Yiren Zhao and Nicolas Papernot and Ross Anderson


Unrolling sgd: Understanding factors influencing machine unlearning

Anvith Thudi and Gabriel Deza and Varun Chandrasekaran and Nicolas Papernot


Increasing the Cost of Model Extraction with Calibrated Proof of Work

Adam Dziedzic and Muhammad Ahmad Kaleem and Yu Shen Lu and Nicolas Papernot


Accelerating Symbolic Analysis for Android Apps

Mingyue Yang and David Lie and Nicolas Papernot


Losing Less: A Loss for Differentially Private Deep Learning

Ali Shahin Shamsabadi and Nicolas Papernot


A Zest of LIME: Towards Architecture-Independent Model Distances

Hengrui Jia and Hongyu Chen and Jonas Guan and Ali Shahin Shamsabadi and Nicolas Papernot


Private Multi-Winner Voting For Machine Learning

Adam Dziedzic and Christopher A Choquette-Choo and Natalie Dullerud and Vinith Menon Suriyakumar and Ali Shahin Shamsabadi and Muhammad Ahmad Kaleem and Somesh Jha and Nicolas Papernot and Xiao Wang


Context-invariant, multi-variate time series representations

Stephan Rabanser and Tim Januschowski and Kashif Rasul and Oliver Borchert and Richard Kurle and Jan Gasthaus and Michael Bohlke-Schneider and Nicolas Papernot and Valentin Flunkert


Fourth International Workshop on Dependable and Secure Machine Learning–DSML 2021

Hui Xu and Guanpeng Li and Homa Alemzadeh and Rakesh Bobba and Varun Chandrasekaran and David E Evans and Nicolas Papernot and Karthik Pattabiraman and Florian Tramer


Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning

Natalie Dullerud and Karsten Roth and Kimia Hamidieh and Nicolas Papernot and Marzyeh Ghassemi