Tim G. J. RudnerView profile
Researcher
Tim G. J. Rudner is a Faculty Fellow at New York University , an AI Fellow at the Georgetown Center for Security & Emerging Technology (CSET) , and an incoming Junior Research Fellow at Trinity College, University of Cambridge . He holds a PhD in computer science from the University of Oxford (advised by Yee Whye Teh and Yarin Gal), a master’s in statistics from Oxford, and a bachelor’s in mathematics and economics from Yale University . Rudner’s research focuses on robust and transparent machine learning models for safety-critical and high-stakes applications. His work spans probabilistic machine learning , uncertainty quantification , generative LLMs , Bayesian neural networks , AI safety , and biomedical discovery . He has developed methods like group-aware priors for subpopulation robustness, function-space regularization techniques, and uncertainty-aware fine-tuning approaches. His recent publications include work on 2025 papers about AI governance frameworks, 2024 studies on robust generative models and uncertainty quantification, and influential studies on vision-language control and diabetic retinopathy detection . His research has been recognized with awards such as the AISTATS Notable Paper Award and the GenAI4DM Outstanding Paper Award . Scientific Awards: 2024 Rising Star in Generative AI Qualcomm Innovation Fellow Rhodes Scholar AISTATS Notable Paper Award (2024) GenAI4DM Outstanding Paper Award (2024) Grants: $30,000 Apple Seed Grant (2024) $700,000 Foundational Research Grant to improve LLM trustworthiness (2024) Rudner actively mentors first-generation low-income students and collaborates with institutions like University of Oxford , Yale University , and New York University . His work bridges theoretical advancements with real-world deployment challenges, emphasizing reliable uncertainty estimation and robust generalization in high-stakes domains.
