I am a PhD student in Machine Learning at University of Cambridge (Computational and Biological Learning Lab). My supervisors are Professor Guillaume Hennequin and Dr Alberto Bernacchia. Prior to my PhD, I completed MPhil in Machine Learning and Machine Intelligence at University of Cambridge, supervised by Prof Richard Turner. I also obtained a B.Sc. in Applied Mathematics from University of Singapore, supervised by Prof Vincent Tan.
My current project studies meta-learning from a dual machine-learning and biological perspective. My research interests also include probabilistic modelling, explainable AI and causal inference. I aim to develop trustworthy algorithms that combine fast and transferable learning, robustness, and interpretability.
PhD in Machine Learning
University of Cambridge
MPhil in Machine Learning and Machine Intelligence, Distinction, 2020
University of Cambridge
BSc in Applied Mathematics, specialised in Mathemtical Modelling and Data Analysis, 2019
National University of Singapore
Inferring the causal structure of a set of random variables is a crucial problem in many disciplines of science. Over the past two decades, various approaches have been proposed for causal discovery from observational data.
Many real-world prediction problems involve modelling the dependencies between multiple different outputs across the input space. Multi-output Gaussian Processes (MOGP) are a particularly important approach to such problems. In this paper, we build on the Gaussian Process Autoregressive Regression (GPAR) model which is one of the best performing MOGP models, but which fails when observation noise is large, when there are missing data, and when non-Gaussian observation models are required.