I am a Ph.D Candidate (2018-2022) of Machine Learning Group, CBL at the University of Cambridge, supervised by Prof. José Miguel Hernández-Lobato, and advised by Prof. Richard Turner. I have a broad research interest in the field of probabilistic machine learning, especially approximate inference, Bayesian deep learning, and generative models. More recently, I am interested in variational inference for stochastic processes. Apart from Bayesian ML, I am also thinking about problems related to missing data and causal inference. I am always open to collaboration, so if you are interested in my research, feel free to contact me!

During my PhD, I have also worked as a intern researcher at Microsoft Reserach Cambridge (MSRC), under the supervision of Dr. Cheng Zhang. Currently I am a part-time researcher at MSRC, working on data-efficient machine learning.

Before joining the University of Cambridge, I obtained the MRes degree in Computational Statistics and Machine Learning from the Depertment of Computer Science, University College London, supervised by Prof. David Barber. During my time at UCL, my research focused on Stein methods for Bayesian inference on doubly intractable models and Gaussian Processes. You can find my master thesis here.