I am Haoting Zhang, a PhD student under the 2020 Cohort of the HDR UK-Turing Wellcome PhD Programme in Health Data Science, currently based at ML@CL, Department of Computer Science and Technology, University of Cambridge. I am supervised by Carl Henrik Ek, Marta Milo and Magnus Rattray. Previously, I attended the University of Oxford for my undergraduate study in Mathematics and Statistics, before studying for a Master's degree in Machine Learning at UCL.

For my PhD thesis, I am working on modelling, optimisation and active learning of drug combinations from a Bayesian perspective. In the first phase of the thesis, we estimated the synergy of the drug combinations using a Bayesian framework so that parameter uncertainty can be estimated and existing knowledge can be inserted through the priors. Another motivation is that, through the uncertainty derived from the model, actionable decisions can be made to guide the subsequent experiments in the pre-clinical drug discovery pipeline. To this end, we developed SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations:

• Zhang, H., Ek, C. H., Rattray, M., & Milo, M. (2023). SynBa: Improved estimation of drug combination synergies with uncertainty quantification. Bioinformatics, 39(Supplement_1), i121-i130.

I am delighted to have presented this work at the ISMB/ECCB 2023 Conference. The code for SynBa will be continuously improved in the near future, with the aim of developing into a general and robust package and eventually being applicable to real-world drug discovery effort.

Prior to my PhD thesis and during the first year of my PhD in the HDR UK programme, I worked jointly on multi-partition clustering of high-dimensional data (in particular images) through variational autoencoders with a hierarchy of latent variables, each with a Mixture-of-Gaussians prior. This work has been accepted at NeurIPS 2021:

• Falck, F.*, Zhang, H.*, Willetts, M., Nicholson, G., Yau, C., & Holmes, C. C. (2021). Multi-facet clustering variational autoencoders. Advances in Neural Information Processing Systems, 34, 8676-8690. (*: Equal Contribution)

Please see About Me for more details about myself :)