Dr Keith Harris BSc, MSc, PGDip
Senior Lecturer in Statistics
Summary
I am an experienced academic in the area of applied statistics and mathematics. I am passionate about teaching and engaging in scholarly activities in the areas of statistical modelling, data analytics and machine learning.
About
I joined Sheffield Hallam University in January 2020 as a Lecturer in Statistics in the Department of Engineering and Mathematics. I moved to the School of Computing and Digital Technologies in October 2023, and I am currently the Athena Swan Champion for the School and the Course Leader for the Online MScs in Computer Science, Computer Science with Artificial Intelligence, Computer Science with Cyber Security, Computer Science with Data Analytics and Computer Science with Software Engineering.
Prior to joining Sheffield Hallam University, I worked as a university teaching associate at the University of Sheffield and before that as a postdoctoral research associate at both the University of Glasgow and the University of Sheffield. The research projects I worked on included Classifiers in Medicine and Biology (Advancing Machine Learning Methodology for New Classes of Prediction Problems), Accurately Inferring Microbial Community Structure from Next Generation Sequencing Data and Improved Tools for the Analysis of Metagenomics Data, and Simulation Tools for Automated and Robust Manufacturing.
I am strong advocate for Equality, Equity, Diversity and Inclusion including being a member of the University's LGBT+ network.
Senior Lecturer
Teaching
School of Computing and Digital Technologies
College of Business, Technology and Engineering
Digital Analytics and Technologies
BSc Mathematics, MSc Big Data Analytics
Data Analytics: Tools and Techniques, Statistical Modelling and Theory, Applied Mathematical and Statistical Modelling
Research
Selection of past publications:
K. Harris, K. Triantafyllopoulos, E. Stillman and T. McLeay, "A multivariate control chart for autocorrelated tool wear processes", Quality and Reliability Engineering International 32(6), pp. 2093-2106, 2016.
K. Harris, C. Quince, T. Parsons, L. Lahti, I. Holmes and U. Z. Ijaz, “Linking statistical and ecological theory: Hubbell’s unified neutral theory of biodiversity as a hierarchical Dirichlet process”, Proceedings of the IEEE, PP(99), pp. 1-14, 2016. (DOI: 10.1109/JPROC.2015.2428213).
K. Harris and P. Blackwell, “Flexible continuous-time modelling for heterogeneous animal movement”, Ecological Modelling, 255, pp. 29-37, 2013.
I. Holmes, K. Harris and C. Quince, “Dirichlet multinomial mixtures: Generative models for microbial metagenomics”, PLOS One, 7(2), 2012. (DOI: 10.1371/journal.pone.0030126).
M. Dakna, K. Harris, A. Kalousis, S. Carpentier, W. Kolch, J. P. Schanstra, M. Haubitz, A. Vlahou, H. Mischak and M. Girolami, “Addressing the challenge of defining valid proteomic biomarkers and classifiers”, BMC Bioinformatics, 2010, 11: 594. (DOI: 10.1186/1471-2105-11-594).
L. E. Hopcroft, M. W. McBride, K. Harris, A. K. Sampson, J. D. McClure, D. Graham, G. Young, T. L. Holyoake, M. A. Girolami and A. F. Dominiczak, “Predictive response-relevant clustering of expression data provides insights into disease processes”, Nucleic Acids Research, 38 (20), pp. 6831-6840, 2010.
Publications
Journal articles
Harris, K., Triantafyllopoulos, K., Stillman, E., & McLeay, T. (2016). A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Quality and Reliability Engineering International, 32 (6), 2093-2106. http://doi.org/10.1002/qre.2032
Harris, K., Parsons, T.L., Ijaz, U.Z., Lahti, L., Holmes, I., & Quince, C. (2015). Linking Statistical and Ecological Theory: Hubbell's Unified Neutral Theory of Biodiversity as a Hierarchical Dirichlet Process. Proceedings of the IEEE, 105 (3), 516-529. http://doi.org/10.1109/jproc.2015.2428213
Jakobsson, H.E., Abrahamsson, T.R., Jenmalm, M.C., Harris, K., Quince, C., Jernberg, C., ... Andersson, A.F. (2014). Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by Caesarean section. Gut, 63 (4), 559-566. http://doi.org/10.1136/gutjnl-2012-303249
Coolen, M.J.L., Orsi, W.D., Balkema, C., Quince, C., Harris, K., Sylva, S.P., ... Giosan, L. (2013). Evolution of the plankton paleome in the Black Sea from the Deglacial to Anthropocene. Proceedings of the National Academy of Sciences, 110 (21), 8609-8614. http://doi.org/10.1073/pnas.1219283110
Harris, K.J., & Blackwell, P.G. (2013). Flexible continuous-time modelling for heterogeneous animal movement. Ecological Modelling, 255, 29-37. http://doi.org/10.1016/j.ecolmodel.2013.01.020
Dakna, M., Harris, K., Kalousis, A., Carpentier, S., Kolch, W., Schanstra, J.P., ... Girolami, M. (2010). Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. BMC Bioinformatics, 11 (1). http://doi.org/10.1186/1471-2105-11-594
Hopcroft, L.E.M., McBride, M.W., Harris, K.J., Sampson, A.K., McClure, J.D., Graham, D., ... Dominiczak, A.F. (2010). Predictive response-relevant clustering of expression data provides insights into disease processes. Nucleic Acids Research, 38 (20), 6831-6840. http://doi.org/10.1093/nar/gkq550
Holmes, I., Harris, K., & Quince, C. (n.d.). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE, 7 (2), e30126. http://doi.org/10.1371/journal.pone.0030126
Conference papers
Harris, K., Girolami, M., & Mischak, H. (2009). Definition of Valid Proteomic Biomarkers: A Bayesian Solution. Lecture Notes in Computer Science, 137-149.
Harris, K., McMillan, L., & Girolami, M. (2009). Inferring meta-covariates in classification. Lecture Notes in Computer Science, 150-161.
Other activities
School Governor - Local Academy Trust