Bio
Runze (Patrick) is a research associate in probabilistic programming at the Alan Turing Institute, where his role aims to develop scalable and composable inference methodologies to enhance the performance and usability of the general-purpose probabilistic programming language Turing.jl, as well as to extend its routine use cases.
Runze holds a visiting position at the Cambridge university Engineering Department, where he obtained his Ph.D. in Information Engineering and served as a research associate before joining the Alan Turing Institute. His research interests include theories of approximate Bayesian inference methods, particularly variational inference and Monte Carlo methods, as well as stochastic modelling for time series. He applies these methodologies to areas such as multi-object tracking, intent inference, and decentralised data fusion. Runze’s work in these fields has earned him an industry fellowship in human-computer interaction and a best student paper award. He also reviewed for journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Aerospace and Electronic Systems, and IEEE Sensors Journal.