Dr Peng Liu

Lecturer in Statistics
+44 (0)1227 823508
Dr Peng Liu


Peng Liu is a Lecturer in Statistics (03/2019-Now). Previously he was a Postdoc Fellow at Department of Mathematical and Statistical Sciences, University of Alberta in Edmonton, Canada (11/2017-01/2019), a Senior Postdoc Fellow at Department of Biostatistics, University of Washington and Fred Hutchinson Cancer Research Centre in Seattle, United States (08/2015-10/2017), a Research Assistant at Department of Mathematics, Hong Kong Baptist University (11/2013-02/2015). He obtained his PhD degree in Biostatistics from Academy of Mathematics and Systems Science, Chinese Academy of Science (07/2015) and BSc degree in Applied Mathematics from Central China Normal University (07/2010).

Research interests

His research interest lies in the intersection of machine learning and statistics. His general focus is to apply machine learning techniques to improve traditional statistical methodologies/overcome the existing bottleneck of statistical procedures and introduce statistical ideas in machine learning.

Machine Learning (matrix factorization, differential privacy), Reinforcement Learning, High Dimensional Data, Bridging Study, Functional Data Analysis, Biostatistics, Semiparametric Modelling, Causal Inference, Rank Estimation 


MAST5010, MACT7290, MAST9420


Current PhD student: Maxine Hua (2024/09-), EPSRC Studentship

Currently, Peng is interested in the following research directions:

1. Privacy protection in medical imaging data analysis, natural language processing

2. Data integration from various parties

3. Federated Learning

4. Application of machine learning in causal inference

5. Application of mixed integer programming algorithm to statistical/machine learning models, to obtain exact solutions


Sa (Sarah) Ren, PhD (2018-2022). First job: PDRA in Statistics, School of Health and Related Research, University of Sheffield (Thesis Topic: Inferring Network Structures Using Hierarchical Exponential Random Graph Models)


2022, Federated Learning for Neuroimaging Data Analysis, Research and Innovation Fund, Division of Computing, Engineering, and Mathematical Sciences, University of Kent

2023, Expectile Threshold Autoregressive Regression (TAR) model, UK Research and Innovation General Funds, UKRI

2023, Improving the statistical analysis of brain imaging data to generate more accurate descriptions of brain activity, UK Research and Innovation, Knowledge Transfer Partnerships (KTP) Fund

2023, Federated Learning based Quantile Regression Model for Credit Card Fraud Detection, Cyber Security Seedcorn Funding, Institute of Cyber Security for Society, University of Kent

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