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. Prior to Harvard, I received a B.Sc. in Mathematics and Applied Mathematics from Chu KoChen Honors College, Zhejiang University. 

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, risk control, algorithmic fairness, and model robustness. 

Recent News

• 05/2024 - our paper on learning and forgetting of LLM is accepted by ICML 2024. 

• 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.