Dr Jonathan Gillard
Reader in Statistics
Director of Admissions
I am a Reader in Statistics but I also work at the interface of optimization, linear algebra and operational research. I pursue a number of research interests. My main current research topic is in matrix optimization problems and their statistical application but maintain active in interdisciplinary and collaborative work. I am a member of the Statistics research group at the School of Mathematics and of the Data Innovation Research Institute.
I am always interested to hear from interested PhD candidates. I currently have students working in topics such as machine learning for optimal decisions in healthcare (joint with the NHS), and anomaly detection (joint with the Office for National Statistics). I am interested in supervising students in any of my research areas.
- Member of Board of Studies
- Examinations Officer for Statistics and Operational Research
- MPhil/PhD contact for Statistics and Operational Research
- MATHS theme lead for the Data Innovation Institute
- Senior Statistics Support Tutor for the Maths Support Service
- Director of Admissions
- Member of the European Network for Business and Industrial Statistics
- Member of the British Society for Research into Learning Mathematics
- Senior Fellow of the Higher Education Academy
I am presently a module leader for two modules:
- MAT002 Statistical Methods
- MA1501 Statistical Inference
- Henry Wilde (with Vincent Knight)
- Emily O' Riordan (with Anatoly Zhigljavsky)
- Megan Scammel (with Anatoly Zhigljavsky)
- Julie Vile (2013): Time-Dependent Stochastic Modelling for Predicting Demand and Scheduling of Emergency Medical Services.
- Gareth Davies (2018): Examination of Approaches to Calibration in Survey Sampling.
- Matrix low-rank approximation and its interface in statistical methodologies such as time series analysis
- Measurement error models
- Development of novel statistical techniques for applied and interdisciplinary research
- Innovative teaching methods and the analysis of survey data generated from student evaluations