My helpful screenshot I am a senior researcher at Microsoft Research AI4Science where I work on machine learning methods for chemistry with Frank Noé and Jan Hermann, focusing on Quantum Monte Carlo. I also have a strong interest in Bayesian experimental design and active learning. I am driven by the desire to understand how machine learning can help us to solve critical problems in the sciences and to build new, sustainable technology. Previously, I did my PhD in Statistical Machine Learning at the University of Oxford, supervised by Yee Whye Teh and Tom Rainforth in the Computational Stats and Machine Learning Group in the Department of Statistics. Before starting my PhD, I studied mathematics at Cambridge where my Director of Studies was Julia Gog.

A large part of my PhD work was on Bayesian experimental design: how do we design experiments that will be most informative about the process being investigated? One approach is to optimize the Expected Information Gain (EIG), which can be seen as a mutual information, over the space of possible designs. In my work, I have developed variational methods to estimate the EIG, stochastic gradient methods to optimize over designs, and how to obtain unbiased gradient estimators of EIG. In more recent work, we have studied policies that can choose a sequence of designs automatically. These two talks (Corcoran Memorial Prize Lecture and SIAM minisymposium) offers introductions to experimental design and my research in this area.

I am also keen on open-source code: highlights include experimental design tools in deep probabilistic programming language Pyro, forward Laplacians, Redis<->Python interfacing, reproducing SimCLR.

Recent work