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Dr Guangquan Li

Senior Lecturer

Department: Mathematics, Physics and Electrical Engineering

EE Guangquan Li Staffprofile 255 (1)I am a Lecturer in Statistics at Northumbria University. My research interests lie in developing and applying Bayesian methods to analyze data arising from health and social sciences. My current research areas include developing novel spatiotemporal models for forecasting, policy evaluation and surveillance (with applications to disease and crime) and developing clustering techniques for time-dependent data (e.g. clustering time trends of burglary rates in a study region).

After I obtained my degree in Mathematics from Newcastle University in 2004, I moved to Imperial College London to study for a Ph.D. in medical statistics.  My thesis, entitled “Stochastic models for carcinogenesis”, focuses on developing mathematical models for describing the initiation and progression of cancer at the cellular level. My Ph.D. viva took place in May 2008 with a rather unexpected outcome of “passed without corrections” (many thanks to my PhD supervisors Dr. Mark Little and Prof Paolo Vineis). After the submission of the thesis, I spent three interesting months in Japan, working at the Radiation Effects Research Foundation in Hiroshima. There I worked on a collaborative research project on evaluating impacts, due to errors in dose estimations, on risk assessment of the atomic bomb survivors. Since late 2008 (till mid-2013), I had been a research associate back at Imperial College London. I worked on various projects with a number of national and international collaborators. In July 2013, I joined Northumbria University as a Lecturer in Statistics. I am a fellow of the Royal Statistical Society

Key Publications

  • Please visit the Pure Research Information Portal for further information
  • Observing Data-Driven Approaches to Covid-19: Reflections from a Distributed, Remote, Interdisciplinary Research Project, Allsopp, R., Bessant, C., Ditcham, K., Janjeva, A., Li, G., Oswald, M., Warner, M. 7 Jul 2021, In: Journal of Legal Research Methodology
  • OMDDAC Snapshot Report 1: Data-driven Public Policy, Allsopp, R., Bessant, C., Dawda, S., Emmett, C., Higgs, M., Janjeva, A., Li, G., Oswald, M., Sutton, S., Warner, M. 18 May 2021
  • OMDDAC Snapshot Report 2: Tech-driven approaches to Public Health, Dawda, S., Allsopp, R., Bessant, C., Emmett, C., Higgs, M., Janjeva, A., Oswald, M., Li, G., Sutton, S., Warner, M. 18 May 2021
  • OMDDAC Snapshot Report 3: Policing and Public Safety, Janjeva, A., Allsopp, R., Bessant, C., Dawda, S., Emmett, C., Higgs, M., Li, G., Oswald, M., Sutton, S., Warner, M. 18 May 2021
  • Mind the Step: A More Insightful and Robust Analysis of the Sentencing Process in England and Wales under the New Sentencing Guidelines, Pina-Sánchez, J., Brunton-Smith, I., Li, G. 1 Jul 2020, In: Criminology and Criminal Justice
  • Spatiotemporal Modeling of Correlated Small-Area Outcomes: Analyzing the shared and type-specific patterns of crime and disorder, Quick, M., Li, G., Law, J. 1 Apr 2019, In: Geographical Analysis
  • The ecology of outdoor rape: The case of Stockholm, Sweden, Ceccato, V., Li, G., Haining, R. 1 Mar 2019, In: European Journal of Criminology
  • Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends, Quick, M., Law, J., Li, G. 1 Jul 2019, In: Environment and Planning B: Planning and Design
  • Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale, Quick, M., Li, G., Brunton-Smith, I. Sep 2018, In: Journal of Criminal Justice
  • Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble, Kontis, V., Bennett, J., Mathers, C., Li, G., Foreman, K., Ezzati, M. 1 Apr 2017, In: The Lancet

PGR Supervision

  • Glory Atilola Bayesian Modelling and Mapping of Health Outcomes in Space and Time using Complex National Surveys Start Date: 24/02/2020
  • Mark Hancock Mapping the uncertain future of longevity: an ensemble approach for forecasting mortality Start Date: 01/10/2017


  • Statistics PhD April 01 2008
  • Information not provided Royal Statistical Society (RSS) 2013

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