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:
- (deep) probabilistic graphical model design;
- fast and accurate (Bayesian) inference/computation techniques;
- uncertainty quantification for computation and downstream tasks;
- 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 lecturer (=US assistant professor) at the Department of Computing at Imperial College London. See the info for prospective students or info for collaborating with me. Before that 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, 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"
- An incomplete list of topics in approximate inference (still updating)