文艺小资的数学博士，目前就职于爱丁堡大学商学院，英国版本的讲师，很多时候也被称作是助理教授。持有英国Global Talent Visa，在英国已有七八年，先后在莱斯特，布莱顿，布里斯托停留过，目前生活在埃克塞特，即将前往爱丁堡。 喜欢一些数学，懂一些统计，时而写两篇统计的科普文；了解一些金融，时常看看全球金融市场走势； 做过一些算法研究，非参贝叶斯的支持者；会一些代码，时而用Python和R处理一些数据画一些图；近期，研究内容基本集中在人口流动与城市规划。 未来的主要研究应该会回归非参贝叶斯模型，与金融时间序列的分析，社交网络的分析等等，非常欢迎有相同研究兴趣的小伙伴交流，讨论与合作相关课题和论文！
目前跟一些喜欢高斯过程与贝叶斯机器学习相关领域的小伙伴组建了Slack兴趣小组，Gaussian Process Models。有兴趣的小伙伴，也非常欢迎加入哦！ Join Us: Link!!!
关于对于想要申请博士的小伙伴们，可以重点关注爱丁堡大学商学院的官网，每年10月和5月会开放申请以及发布一些Funding和Scholarship的信息哦！ 当然如果你对本人做的一些东西有兴趣的话，非常欢迎直接在Slack里面给我发Direct Message或者直接给我发邮件讨论具体博士申请内容哦！
数学与统计 博士, 2017
数学与应用数学 本科, 2013
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 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.
Gaussian process regression (GPR) is a kernel-based nonparametric method that has been proved to be effective and powerful in many areas, including time series prediction. In this thesis, we focus on GPR and its extensions and then apply them to financial time series prediction. We first review GPR, followed by a detailed discussion about model structure, mean functions, kernels and hyper-parameter estimations. After that, we study the sensitivity of hyper-parameter and performance of GPR to the prior distribution for the initial values, and find that the initial hyper-parameters’ estimates depend on the choice of the specific kernels, with the priors having little influence on the performance of GPR in terms of predictability. Furthermore, GPR with Student−t process (GPRT) and Student−t process regression (TPR), are introduced. All the above models as well as autoregressive moving average (ARMA) model are applied to predict equity indices. We find that GPR and TPR shows relatively considerable capability of predicting equity indices so that both of them are extended to state-space GPR (SSGPR) and state-space TPR (SSTPR) models, respectively. The overall results are that SSTPR outperforms SSGPR for the equity index prediction. Based on the detailed results, a brief market efficiency analysis confirms that the developed markets are unpredictable on the whole. Finally, we propose and test the multivariate GPR (MV-GPR) and multivariate TPR (MV-TPR) for multi-output prediction, where the model settings, derivations and computations are all directly performed in matrix form, rather than vectorising the matrices involved in the existing method of GPR for multi-output prediction. The effectiveness of the proposed methods is illustrated through a simulated example. The proposed methods are then applied to stock market modelling in which the Buy&Sell strategies generated by our proposed methods are shown to be profitable in the equity investment.