DrYingzhen Li

**please see http://yingzhenli.net/home/en/ for details**

I am currently an Associate Professor in Machine Learning at the Department of Computing at Imperial College London. 

I am passionate about building reliable machine learning systems that can generalise to unseen environment, and my approach combines both Bayesian statistics and deep learning.

My current research interests are two-fold and I use probabilistic ML methods to develop them:

1. Trustworthy ML models: uncertainty quantification, robustness, explainable ML, decision-making, adaptive methods (e.g., continual learning, model editing), etc.

2. Generative modelling: sequential generative models on both sequence and static data (e.g., video, spatiotemporal data, general time-series, images, tabular data, etc.), and causal representation learning with generative models.

I have worked (and continue to work) extensively on approximate inference with applications to Bayesian deep learning and deep generative models. My work on this subject has been applied in industrial systems and implemented in deep learning frameworks (e.g. Tensorflow Probability and Pyro). To understand more about this subject, see my tutorial on approximate inference at NeurIPS 2020 and UAI 2025.

I'm also honoured yet humbled to help (and have helped) organise flagship research conferences and workshops in probabilistic ML, including AABI and AISTATS.

Before joining Imperial, I was a senior researcher at Microsoft Research Cambridge, and previously I have interned at Disney Research. I received her PhD in engineering from the University of Cambridge, UK.