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Lily H. Zhang

Machine Learning and Statistics

Hi! I'm a PhD candidate at New York University and fortunate to be advised by Professor Rajesh Ranganath and Professor Kyle Cranmer. I'm grateful to be a DeepMind scholar.

I'm interested in deep learning, generative modeling, and out-of-distribution detection and generalization. I'm particularly interested in applications of machine learning for health and science.

Previously, I worked on machine learning problems in industry, notably in NLP, information extraction, and document processing.

Feel free to contact me for research collaborations or other engagements.

Publications

  1. Lily H. Zhang, Mark Goldstein, Rajesh Ranganath. Understanding Out-of-distribution Detection with Deep Generative Models. ICML, 2021.
  2. Aahlad Puli, Lily H. Zhang, Eric Oermann, Rajesh Ranganath. Out-of-Distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. ICLR, 2022.
  3. Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath. Set Norm and Equivariant Residual Connections: Putting the Deep in Deep Sets. ICML, 2022.

Workshop Papers

  1. Lily H. Zhang, Mark Goldstein, Rajesh Ranganath. Understanding Out-of-distribution Detection with Deep Generative Models. ICLR RobustML Workshop, 2021.
  2. Lily H. Zhang, Michael Hughes. Rapid Model Comparison by Amortizing Across Models. Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, 2020.

Education

  • New York University, New York, NY. Candidate for Doctor of Philosophy in Data Science. Advisors: Rajesh Ranganath, Kyle Cranmer. Aug. 2020 – Present
  • Harvard College, Cambridge, MA. Bachelor of Arts in Statistics and Computer Science. Magna Cum Laude with High Honors. Aug. 2013 – May 2017

Honors & Awards

  • DeepMind Fellow, 2020.
  • Phi Beta Kappa, 2017.

Patents