I am Haoting Zhang, a Postdoctoral Researcher at the National Heart and Lung Institute (NHLI), Imperial College London. I am working on a number of areas in machine learning for healthcare, including developing machine learning methods for drug discovery and analysing disease trajectories in patients with pulmonary hypertension. More generally, I am interested in developing interpretable and uncertainty-aware machine learning methods for healthcare applications, with an emphasis on improving their translational impact in clinical practice.
Previously, I completed my PhD as part of the 2020 cohort of the HDR UK–Turing Wellcome PhD Programme in Health Data Science, based at ML@CL in the Department of Computer Science and Technology, University of Cambridge. I was supervised by Carl Henrik Ek, Marta Milo, and Magnus Rattray. Prior to that, I studied Mathematics and Statistics as an undergraduate at St Hilda’s College, University of Oxford, followed by a Master’s degree in Machine Learning at UCL.
My interest lies in approaching healthcare challenges with probabilistic and/or generative models in machine learning. Such types of models have the potential to be utilised in healthcare because they provide routes to inserting prior knowledge, interpreting results and estimating uncertainty, which are all crucial aspects in healthcare applications. However, crucial gaps remain to be filled, and I am generally interested in tackling these challenges. More broadly, my research focuses on bridging the gap between machine learning for healthcare and its deployment in clinical practice.
Outside of research, I am passionate about motorsports. I follow Formula One, MotoGP and World Rally Championship, and I enjoy doing some indoor karting in a casual manner from time to time. Running has also become my passion. I ran the Big Half (2025) and the London Landmark Half Marathon (2026), and I am looking forward to the next one.
I am also owned by a ginger cat (yes, you read it right). Here is a picture of my boss:
