Zhun Deng

Hi! I am a postdoctoral researcher at Columbia University, hosted by Toniann Pitassi and Richard Zemel. Previously, I completed my Ph.D. in Computer Science at Harvard University under the guidance of Cynthia Dwork. 

My research interests mainly lie in the foundations of reliable and responsible machine learning. In particular, I develop formal frameworks to address algorithmic and societal challenges in modern data science, especially about uncertainty quantification, algorithmic fairness, and model robustness. 

Recent News

• 04/2024 - our paper on distribution-free risk control for large language models is accepted by ICLR 2024. 

• 03/2024 - A new paper on reconciling diverse opinions in reinforcement learning from human feedback is on arXiv now. 

• 02/2024 - I delivered a talk on practical theories in responsible machine learning at Center for Data Science, New York University. 

• 09/2023 - two papers accepted by NeurIPS 2023: distribution-free societal dispersion control (spotlight, top 3% among submissions) and uncertainty quantification in physics-informed nets. 

• 05/2023 - our paper about generalization theory for information bottleneck is accepted by ICML 2023.

• 01/2023 - two papers accepted by AISTATS 2023: reinforcement learning with stepwise fairness constraints and understanding multimodal contrastive learning and incoportate paired data.

• 01/2023 - two papers accepted by ICLR 2023: FIFA: making fairness more generalizable on imbalanced data and distribution-free quantile risk control.