This interdisciplinary research group brings together researchers involved in the analysis of data from medical and biomedical data. The focus is on advanced methodology applicable to the field including bioinformatics, infectious disease modelling, longitudinal and time-to-event modelling, growth curve modelling, causal modelling, methods for incidence estimation and multivariate analysis. The group meets on a monthly basis.
Data science is an exciting new field that uses computer-intensive statistical methods to identify patterns and make predictions using large volumes of data. The applications of data science are diverse, from predicting fraudulent transactions before they occur to extracting marketing insights from unstructured social media data. This research group is interested in the statistical and machine learning techniques that are used in data science applications.
Current members include Dr Miguel Lacerda, Dr Ian Durbach, Dr Sebnem Er, Assoc Prof Francesca Little, Assoc Prof Sugnet Lubbe, Dr Juwa Nyirenda, Mr Neil Watson, Assoc Prof Tim Gebbie, Dr Melvin Varughese, Mr Stefan Britz, Mr Mzabalazo Ngwenya, Mr William Msemburi and Dr. Etienne Pienaar.
This interdisciplinary research group, which involves professionals from various fields and departments, is aimed at promoting scholarly output and collaborations in financial modelling and applications. Research topics may encompass, but are not restricted to quantitative analysis, portfolio management and optimisation, financial risk modelling and asset pricing.
This is an interdisciplinary research group working on the intersection of the statistical analysis of financial market transaction and order-book data, agent-based modelling, machine and statistical learning for trading and investing, and Econophysics. The research is focused on the data driven enquiry of the structure, function and interrelationships between various components of financial markets towards deepening our understanding of markets and our ability to safely control and automate trading and investment decision making.
Collaborators: Assoc Prof Tim Gebbie, Assoc Prof Diane Wilcox (University of the Witwatersrand), Dr Dieter Hendricks (University of Oxford) and graduate students.
The Modelling and Simulation Hub, Africa (MASHA) is a research group at the University of Cape Town. MASHA’s research focus is the development and application of mathematical modelling and computer simulation to predict the dynamics and control of infectious diseases to evaluate the impact of policies aimed at reducing morbidity and mortality. Based in the Faculty of Science, MASHA’s research is closely integrated with other disciplines resulting in policy-driven and impactful scientific research.The group also hosts an annual short course on introductory concepts on Infectious Disease Modelling.
Current members: Dr Sheetal Silal, Dr Chacha Issarow, Jared Norman, Wanja Chabaari and graduate students.
This departmental research group focuses on the development of appropriate decision modelling and support tools, including both “hard” (e.g. optimization and simulation) and “soft” (e.g. problem structuring and systems modelling) approaches, relevant to critical national decision and policy making (e.g. in health, water, energy, agriculture, public sector monitoring and evaluation).
This interdepartmental research group is hosted in the Department of Statistical Sciences but also includes core team members from Biological Sciences and organisations outside UCT. We work at the intersection between statistics, ecology and environmental sciences, with an overarching theme of structured decision support. Our research focuses on conservation, animal and plant demography, climate change, understanding of biodiversity patterns, evolutionary ecology and macroecology.
Current core team members: Greg Distiller, Allan Clark , Birgit Erni, Ian Durbach, Mzabalazo Ngwenya, Vernon Visser, Res Altwegg, Astrid Jarre (Biological Sciences), Jasper Slingby (South African Environmental Observation Network), Jonathan Colville (South African National Biodiversity Institute), Guy Midgley (Stellenbosch University), David Borchers (University of St Andrews).