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 machine learning systems against adversaries.
In general I'm also interested in transfer/meta learning, information theory, optimisation, and sequential data modelling.
Currently, I am a senior researcher at Microsoft Research Cambridge. I also actively work with academic people, so feel free to contact me for a chat over tea, or see here for collaborating/interning with me.
I read my PhD in machine learning at the University of Cambridge, where I was also a member of Darwin College. I had a great time working closely with Dr. Richard E. Turner (my PhD supervisor), and Dr. José Miguel Hernández-Lobato, Dr. Qiang Liu, Dr. Yarin Gal and Dr. Stephan Mandt.
My PhD thesis is about fast approximations to integrals, and as a side product here's an incomplete list of topics in approximate inference.