Staff profile

Prior to moving to Durham University, Sarah was an Associate Professor at Newcastle University where she was Assistant Co-Director of the EPSRC CDT in Cloud Computing for Big Data and Deputy Degree Programme Director for the MSc in Data Science (with Specialisation in Statistics). She has led work packages on a number of RCUK grants, most notably the £1.5M NERC grant "Flood-PREPARED: Predicting Rainfall Events by Physical Analytics of REaltime Data" and the £358K project funded by the Alan Turing Institute "Streaming data modelling for real-time monitoring and forecasting". Sarah has given invited seminars at research institutions across the world, including Duke, Columbia and Maynooth Universities and the Flatiron Institute in New York. She has a keen interest in probabilistic programming languages and has developed a short-course on the Stan language and software, which she has delivered to a mix of academics and businesses across the UK. Sarah is a Fellow of the Alan Turing Insitute and serves on the editorial board of the new journal ACM Transactions on Probabilistic Machine Learning. She has co-supervised three PhD students to completion, with two more in the final year of their PhD, in addition to five Postdoctoral researchers.

• Bayesian inference
• Spatio-temporal modelling
• Statistical bioinformatics
• Time series analysis

• 2021: Fellow of the Alan Turing Institute (2021-2023):
• 2017: Fellow of the Higher Education Academy (2017-present):
• 2011: Royal Statistical Society North Eastern Local Group (Chair, 2018-2020; Secretary, 2015-2018; Ordinary Member, 2011-2015): Chair (2018-2020), Secretary (2015-2018), Ordinary Member (2011-2015)
• 2010: Fellow of the Royal Statistical Society (2010-present):

Chapter in book

• Williams, T. A., & Heaps, S. E. (2014). An Introduction to Phylogenetics and the Tree of Life. In M. Goodfellow, I. Sutcliffe, & J. Chun (Eds.), New Approaches to Prokaryotic Systematics (13-44). Academic Press. https://doi.org/10.1016/bs.mim.2014.05.001

Conference Proceeding

• (2011, June). Discussion of ''Modelling multivariate counts varying continuously in space'' by A. M. Schmidt and M. A. Rodriguez. Presented at Bayesian Statistics 9: The Ninth Valencia International Conference on Bayesian Statistics, Benidorm, Spain

Journal Article

• Heaps, S. E., & Jermyn, I. H. (2024). Structured prior distributions for the covariance matrix in latent factor models. Statistics and Computing, 34(4), Article 143. https://doi.org/10.1007/s11222-024-10454-0
• Johnson, S. R., Heaps, S. E., Wilson, K. J., & Wilkinson, D. J. (2023). A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields. Environmetrics, 34(8), https://doi.org/10.1002/env.2824
• Hannaford, N., Heaps, S., Nye, T., Curtis, T., Allen, B., Golightly, A., & Wilkinson, D. (2023). A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. Computational Statistics & Data Analysis, 179, https://doi.org/10.1016/j.csda.2022.107659
• Heaps, S. E. (2023). Enforcing Stationarity through the Prior in Vector Autoregressions. Journal of Computational and Graphical Statistics, 32(1), 74-83. https://doi.org/10.1080/10618600.2022.2079648
• Tyler, A. R., Ragbirsingh, R., McMonagle, C. J., Waddell, P. G., Heaps, S. E., Steed, J. W., Thaw, P., Hall, M. J., & Probert, M. R. (2020). Encapsulated Nanodroplet Crystallization of Organic-Soluble Small Molecules. Chem, 6(7), 1755-1765. https://doi.org/10.1016/j.chempr.2020.04.009
• Hannaford, N. E., Heaps, S. E., Nye, T. M., Williams, T. A., & Embley, T. M. (2020). Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference. Annals of Applied Statistics, 14(4), 1964-1983. https://doi.org/10.1214/20-aoas1369
• Heaps, S. E., Farrow, M., & Wilson, K. J. (2020). Identifying the effect of public holidays on daily demand for gas. Journal of the Royal Statistical Society: Series A, 183(2), 471-492. https://doi.org/10.1111/rssa.12504
• Heaps, S. E., Nye, T. M., Boys, R. J., Williams, T. A., Cherlin, S., & Embley, T. M. (2020). Generalizing rate heterogeneity across sites in statistical phylogenetics. Statistical Modelling, 20(4), 410-436. https://doi.org/10.1177/1471082x18829937
• Cherlin, S., Heaps, S. E., Nye, T. M., Boys, R. J., Williams, T. A., & Embley, T. M. (2017). The Effect of Nonreversibility on Inferring Rooted Phylogenies. Molecular Biology and Evolution, 35(4), 984-1002. https://doi.org/10.1093/molbev/msx294
• Williams, T. A., Szöllősi, G. J., Spang, A., Foster, P. G., Heaps, S. E., Boussau, B., Ettema, T. J., & Embley, T. M. (2017). Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proceedings of the National Academy of Sciences, 114(23), E4602-E4611. https://doi.org/10.1073/pnas.1618463114
• Heaps, S. E., Boys, R. J., & Farrow, M. (2015). Bayesian modelling of rainfall data by using non-homogeneous hidden Markov models and latent Gaussian variables. Journal of the Royal Statistical Society: Series C, 64(3), 543-568. https://doi.org/10.1111/rssc.12094
• Williams, T. A., Heaps, S. E., Cherlin, S., Nye, T. M., Boys, R. J., & Embley, T. M. (2015). New substitution models for rooting phylogenetic trees. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1678), Article 20140336. https://doi.org/10.1098/rstb.2014.0336
• Heaps, S. E., Boys, R. J., & Farrow, M. (2014). Computation of marginal likelihoods with data-dependent support for latent variables. Computational Statistics & Data Analysis, 71, 392-401. https://doi.org/10.1016/j.csda.2013.07.033
• Heaps, S. E., Nye, T. M., Boys, R. J., Williams, T. A., & Embley, T. M. (2014). Bayesian modelling of compositional heterogeneity in molecular phylogenetics. Statistical Applications in Genetics and Molecular Biology, 13(5), 589-609. https://doi.org/10.1515/sagmb-2013-0077