Lily H. Zhang
Scholar

Lily H. Zhang

Google Scholar ID: fmCi9ZQAAAAJ
New York University
machine learningdeep learninggenerative modeling
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Citations & Impact
All-time
Citations
451
 
H-index
9
 
i10-index
9
 
Publications
19
 
Co-authors
0
 
Contact
Emaillily.h.zhang@nyu.eduTwitterOpen ↗GitHubOpen ↗LinkedInOpen ↗
Publications
1 items
Preference learning made easy: Everything should be understood through win rate
2025
Cited
0
Resume
Academic Achievements
  • - Publications:
  • - Preference Learning Algorithms Do Not Learn Preference Rankings, NeurIPS 2024
  • - Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning, MLST 2024
  • - Towards Minimal Targeted Updates of Language Models with Targeted Negative Training, TMLR 2024
  • - Don't Blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy, NeurIPS 2023
  • - When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations, MLHC 2023
  • - Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection, AAAI 2023
  • - Set Norm and Equivariant Residual Connections: Putting the Deep in Deep Sets, ICML 2022
  • - Out-of-Distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations, ICLR 2022
  • - Understanding Out-of-Distribution Detection with Deep Generative Models, ICML 2021
  • - Rapid Model Comparison by Amortizing Across Models, AABI 2020
  • - Awards: JP Morgan PhD Fellow (2024), Meta AI Mentorship Fellow (2024), DeepMind Fellow (2020), Phi Beta Kappa (2017)
  • - Patent: Graphical user interface systems for generating hierarchical data extraction training dataset.
Research Experience
  • - Collaborated with Professors Kyle Cranmer (Physics), Kyunghyun Cho (Computer Science), Don Rubin (Statistics), Gary King (Quantitative Social Science), Jukka-Pekka “JP” Onnela (Biostatistics), John M. Higgins (Pathology, Systems Biology), and Dustin Tingley (Government, Political Science).
  • - Worked for several machine learning start-ups and conducted LLM research at Google.
Education
  • - New York University: Candidate for Doctor of Philosophy in Data Science, Aug. 2020 – Summer 2025 (projected), Advisor: Professor Rajesh Ranganath.
  • - Harvard College: Bachelor of Arts in Statistics and Computer Science, Magna Cum Laude with High Honors, Aug. 2013 – May 2017.
Background
  • - Research Interests: Advancing the reliability of machine learning models, including controllable generation and alignment of generative models, out-of-distribution detection, and generalization.
  • - Application Areas: Health and science.
  • - Honors: DeepMind Scholar, Visiting Researcher at Facebook AI Research, JP Morgan Chase PhD Fellow.