I am a Research Scientist in the Department of Computer Science at the University of Chicago. I mainly work with Ben Zhao and Nick Feamster. My research focuses on the security of machine learning algorithms. I am also interested in the application of machine learning to problems of societal interest. I completed my Ph.D. under the supervision of Prateek Mittal in the Department of Electrical and Computer Engineering at Princeton University. For an accessible overview of my research, see Research and for a full list of papers, see Publications.
- 09/2022: Two papers accepted to Neurips 2022 on analyzing learned representations from robustly trained networks and finding naturally backdoors in image datasets!
- 05/2022: Paper on poisoning attack forensics is accepted to USENIX 2022. Congratulations Shawn!
- 04/2022: Proposal on Fundamental Limits on the Robustness of Supervised Machine Learning Algorithms was awarded a grant from the C3.ai Digital Transformation Institute.
- 04/2022: Chapter on Adversarial Attacks for Anomaly Detection is forthcoming in the Springer Encyclopedia on Machine Learning and Data Science
- 01/2022: AISTATS 2022 paper on defending against model poisoning attack is up. Congratulations Ashwinee!
- 11/2021: Selected for the UChicago Rising Stars in Data Science.
- 09/2021: Paper on defenses against website fingerprinting attacks is accepted to AISec 2021. Congratulations Shawn!
- 07/2021: Paper on lower bounds on cross-entropy loss for classification with test-time attacks is appearing at IMCL 2021! Follow-up to our NeurIPS 2019 paper which introduced this line of work on fundamental lower bounds on loss in the presence of test-time attackers.
- 06/2021: Our paper on physical backdoor attacks is appearing at CVPR 2021.
- 05/2021: Runner-up for the Bede Liu Best Dissertation award from the Department of Electrical and Computer Engineering at Princeton University for my thesis The Role of Data Geometry in Adversarial Machine Learning.