I’m currently a Lecturer / Assistant Professor in Predictive Analytics at the University of Edinburgh Business School. Before joining Edinburgh, I was a Lecturer in Computer Science at the University of Exeter. In the previous, I worked as a research fellow in the Department of Informatics, University of Sussex (UK) during the period 2017 - 2019, and as a postdoc in the Department of Computer Science, University of Exeter (UK) during the period 2019 - 2020.
My research interests focus on probabilistic machine modelling and non-parametric Bayesian predictive methods such as Gaussian Process and Student-t Process modelling. As their applications, I also have expertise in time series prediction, data-driven option pricing, financial data analysis, algorithmic fairness and human mobility.
Currently, I am leading an online Research Interest Group on Gaussian Process Modelling (approx. 700 members till February 2023) and the topics include Bayesian non-parametric modelling, Bayesian optimisation, Bayesian inference, Bayesian computation and many related application areas. Join Us via Slack !!!
About PhD application, please keep an eye on the University of Edinburgh Business School Website. If you would like to work with me, please feel free to send me an email to discuss the details or send me a direct message via Slack.
PhD in Mathematics and Statistics, 2017
University of Leicester, UK
BSc in Mathematics and Applied Mathematics, 2013
Shandong University, China
13/04/2022, great work with our team! Think of your privacy, look around your social connections online, and you’ll have interest in this paper, please see link.
Deliver both online teaching and face to face teaching in Business School. Taught modules and programs include
Deliver both online teaching and face to face teaching in Computer Science and Data Science. Taught modules include
In this paper, we propose a precise definition of multivariate Gaussian processes based on Gaussian measures on vector-valued function spaces, and provide an existence proof.
In this paper, we apply entropy and predictability measures to analyse and bound the predictive information of an individual’s mobility pattern and the flow of that information from their top social ties and from their non-social colocators.
we propose that the predictability in human mobility is a state and not a static trait of individuals. First, we show that time (of the week) explains people’s whereabouts more than the sequences of locations they visit. Then, we show that not only does predictability depend on time but also the type of activity an individual is engaged in, thus establishing the importance of contexts in human mobility..
In this paper, we focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g., loans, job interviews) for certain groups than for others. We show that bias in the output can naturally be controlled in probabilistic models by introducing a latent target output.
In this paper, we propose a unified framework which is used not only to introduce a novel multivariate Student-t process regression model (MV-TPR) for multi-output prediction, but also to reformulate the multivariate Gaussian process regression (MV-GPR) that overcomes some limitations of the existing methods.