Probabilistic machine learning is something that Haoting Zhang finds “very neat and beautiful” thanks to his background in maths and statistics.

Now in his second year he is pursuing a thesis study into active learning and the optimisation of drug combinations.

Haoting feels that spending a year on foundation studies ensures that students are well-prepared for their 3-year doctoral thesis. It has also given him a better understanding of the wider challenges that exist in carrying out effective health data science that yields the results clinicians need in order to treat patients more effectively.

This was emphasised by one project he was involved with that aimed to use NHS data to carry out multimorbidity clustering in order to improve preventative interventions.

It was invaluable in a number of ways, one was that he had the chance to work with data at a very large scale on a real-world problem and another was about the practicalities of good research – especially the fundamental importance of pre-processing data.

Haoting’s background

A first degree was in Mathematics and Statistics at the University of Oxford was followed by a Master’s in Machine Learning at University College London (UCL).

Outside his research studies Haoting enjoys reading and jogging and is a fan of motorsport, especially Formula One, who occasionally does some Go Karting.