Welcome!

Hi there! My name is Yingzhen (映真) and I hope you enjoy this website. 😀

I'm interested in building reliable machine learning systems which can generalise to unseen environments. I approach this goal using probabilistic modelling and representation learning, some of my research topics include:

1. (deep) probabilistic graphical model design;
2. fast and accurate (Bayesian) inference/computation techniques;
3. uncertainty quantification for computation and downstream tasks;
4. robust and adaptive machine learning systems.

In general I'm also interested in transfer/meta learning, information theory, optimisation, and sequential data modelling.

I am currently a Senior Lecturer (=US Associate Professor) in Machine Learning at the Department of Computing at Imperial College London. See the info for prospective students or info for collaborating with me. Since Mar 2024 I am also a Turing Fellow at The Alan Turing Institute. Before coming back to academia, I've spent 2.5 wonderful years as a senior researcher at Microsoft Research Cambridge. 

I read my PhD with Prof. Richard E. Turner in machine learning at the University of Cambridge, where I was also a member of Darwin College. My PhD thesis is about approximate inference. If you want to know more about approximate inference & probabilistic ML, check out the following materials:

• NeurIPS 2020 tutorial on "Advances in Approximate Inference" (with Cheng Zhang)
• ProbAI 2022 tutorial on "Introduction to Bayesian Neural Networks"
• GeMSS 2023 tutorial on "Sequential Generative Models"
• AAAI 2023 new faculty highlight "Robust and Adaptive Deep Learning via Bayesian Principles" [talk video]
• An incomplete list of topics in approximate inference (still updating)