Natalie Dullerud


M.S. Student
Department of Computer Science
University of Toronto, Toronto, ON, CA


I am currently completing my Master’s degree in Computer Science at University of Toronto under supervision by Dr. Nicolas Papernot in the CleverHans Lab and Dr. Marzyeh Ghassemi in the HealthyML lab. Previously, I received my B.S. in Mathematics from University of Southern California, with minors in Chemistry and Computer Science.

Through the past several years, my research interests have spanned statistical learning theory; algorithmic fairness and equity in machine learning; differential privacy; representation learning; optimization; and generalizable applications of machine learning in the healthcare and biological domains. At the moment, I continue to be motivated by a broad range of topics and research questions under the general umbrella of mathematics and machine learning.


Dullerud, N., Roth, K., Hamidieh, K., Papernot, N., Ghassemi, M. (2022). Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. Proceedings of the 10th International Conference on Learning Representations.

** Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L., Correa, R., Dullerud, N., Ghassemi, M., Gichoya, J.W., Huang, S., Kuo, P., Lungren, M. P., Price, B. J., Purkayastha, S., Pyrros, A. A., Oakden-Rayner, L., Okechukwu, C., Seyyed-Kalantari, L., Trivedi, H., Wang, R., Zaiman, Z., Zhang, H. (2022). Reading Race: AI Recognizes Patient’s Racial Identity In Medical Images. The Lancet Digital Health.

Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., Ghassemi, M. (2021). An Empirical Framework for Domain Generalization in Clinical Settings. Proceedings of the 2nd ACM Conference on Health, Inference, and Learning.

Jia, H.*, Yaghini, M.*, Choquette-Choo, C.A.†, Dullerud, N.†, Thudi, A.†, Chandrasekaran, V., Papernot, N. (2021). Proof-of-Learning: Definitions and Practice. Proceedings of the 42nd IEEE Symposium on Security and Privacy.

Cheng, V., Suriyakumar, V., Dullerud, N., Joshi, S., Ghassemi, M. (2021). Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. Proceedings of the 4th ACM Fairness, Accountability, and Transparency Conference.

Choquette-Choo, C.A.*, Dullerud, N.*, Dziedzic, A.*, Zhang, Y.*, Jha, S., Wang, X., Papernot, N. (2021). CaPC Learning: Confidential and Private Collaborative Learning. Proceedings of the 9th International Conference on Learning Representations.

Dullerud, N., Freedman-Susskind, T., Gnanapragasam, P., Snow, C., West, A.P., and Jonsson, V.D. (2020). Feature selection and combinatorial optimization on fitness landscapes to constrain anti-SARS-CoV2 antibody design and address viral escape. Proceedings of Learning Meaningful Representations of Life (LMRL) Workshop at the 34th Conference on Neural Information Processing Systems.

Dullerud, N., Jonsson, V.D. (2020). Cellular Immunotherapy Treatment Scheduling to Address Antigen Escape. Proceedings of the 59th IEEE Conference on Decision and Control.

Jonsson, V.D., Ng, R., Dullerud, N., Wong, R.A., Hibbard, J., Wang, D., Aguilar, B., Starr, R., Weng, L., Alizadeh, D., Forman, S., Badie, B., Brown, C.E. (2022). CAR T cell therapy drives endogenous locoregional T cell dynamics in a responding patient in glioblastoma. [In Review Nature Medicine 2022]

*, † Equal author contribution, authors listed alphabetically by last name
** All authors listed alphabetically

Recorded Presentations

CaPC—Confidential and Private Collaborative Learning, AI Superstream Series: Securing AI, O’Reilly Media Sponsored by Intel, Virtual, 2021

Proof-Of-Learning: Definitions and Practice, Proceedings of the 42nd IEEE Symposium on Security and Privacy, Virtual, 2021

Blog Posts

Here are some blog posts which I have co-authored:


The best way to reach me is via email: natalie [dot] dullerud [at] mail [dot] utoronto [dot] ca