Yingzhen Li

Yingzhen Li is a Senior Lecturer in Machine Learning at the Department of Computing, Imperial College London, within the Faculty of Engineering. She holds a Turing Fellowship at The Alan Turing Institute (2024). Her research focuses on building reliable machine learning systems through probabilistic methods, emphasizing uncertainty quantification, robustness, explainability, and adaptive techniques. Key areas include trustworthy ML models, generative modeling (especially sequential and spatiotemporal data), and approximate inference applied to Bayesian deep learning.

Education: PhD in Engineering from the University of Cambridge, supervised by Prof. Richard E. Turner. Previously a senior researcher at Microsoft Research Cambridge and intern at Disney Research.

Research interests span uncertainty-aware AI, causal representation learning, and scalable inference techniques. Her work has influenced industrial applications in deep learning frameworks like TensorFlow Probability and Pyro. Notable contributions include tutorials on approximate inference at NeurIPS 2020 and organizing conferences like AABI and AISTATS.

Her recent publications address challenges in generative AI, calibration of vision-language models, and energy-based models. Awards include the Turing Fellowship recognizing her leadership in foundational AI research.

Collaborations include affiliations with The Alan Turing Institute and prior industry experience. She actively mentors students and advises on adaptive learning systems through her academic and industrial networks.