Yingzhen Li

Yingzhen Li is an Associate Professor in Machine Learning at the Department of Computer Science, Imperial College London. Her research interests include machine learning, Bayesian statistics, representation learning, generative models, robustness in learning, and AI-assisted decision making. She can be contacted via email at yingzhen.li@imperial.ac.uk or through her profile page at https://profiles.imperial.ac.uk/yingzhen.li.

Yingzhen Li is a researcher at Imperial College London specializing in Artificial Intelligence, Machine Learning, and Statistics. Their work focuses on developing advanced methods for approximate inference, variational learning, and robustness in deep learning models. Li's research includes topics such as variational continual learning, Bayesian neural networks, and generative models. They have explored the use of divergence measures like Rényi divergence and α-divergence in variational inference and have contributed to the development of techniques for improving generalization in reinforcement learning and robustness against adversarial attacks. Their publications also address challenges in generative AI and the integration of Bayesian methods in deep learning.