Keynote Speakers

Distinguished Professor Kerrie Mengersen has spent more than 30 years advancing Bayesian statistics and its application within data science, helping to bridge statistical theory with machine learning and artificial intelligence. A leader in Australia’s data science ecosystem, she established one of the country’s earliest university-based data science centres and founded the Australian Data Science Network, which connects more than 40 research organisations.

Her research contributions include spatio-temporal modelling, Markov chain Monte Carlo (MCMC) methods, expert prior elicitation, and the integration of sensor and citizen science data, as well as privacy-preserving approaches such as federated learning. These developments have underpinned impactful real-world applications, including the award-winning Australian Cancer Atlas and Virtual Reef Diver, supporting advances in health policy and environmental conservation.

With over 400 publications, a range of national and international honours, and leadership across global scientific communities, Professor Mengersen continues to shape the field of data science. She is also a dedicated mentor, having supervised more than 90 postgraduate researchers.

Marvin N. Wright builds machine learning that doesn't just predict, but explains. His research sits where modern machine learning meets classical statistics, and centers on some of the recurring challenges in health data science: how to make complex models interpretable, how to generate realistic synthetic data, and how to draw valid statistical conclusions from algorithms designed only to predict.

He is the creator of ranger, a widely used random forest implementation, and his work on explainable AI, survival analysis, and generative modeling has appeared in venues including ICML, AISTATS, Statistics in Medicine, and Bioinformatics. A common thread runs through it: pushing machine learning in the health sciences beyond prediction, toward transparency, trust, and valid inference.

Marvin is Professor of Machine Learning in Statistics at the University of Bremen and heads the Department of Statistical Methods in Epidemiology at the Leibniz Institute for Prevention Research and Epidemiology – BIPS, Germany, where he leads an Emmy Noether Research Group funded by the German Research Foundation (DFG).

Sugnet Lubbe obtained her PhD in mathematical statistics at Stellenbosch University in 2001. After spending 13 years in industry, she moved to academia in 2009 as Associate Professor in the Department of Statistical Sciences at UCT. In 2017 she moved to the Department of Statistics and Actuarial Science, Stellenbosch University as Professor of Statistics.

Sugnet’s research is focused on multivariate data analysis and more specifically on visualisation and biplots. She is the Director of the Centre for Multi-Dimensional Data Visualisation (MuViSU), with 28 research members from four South African universities, eleven international universities, and four members from industry.

Sugnet is an elected member of the International Statistics Institute, has served on the executive committee of the International Association of Statistical Computing, and is chair of the Multivariate Data Analysis Special Interest Group (MDAG). She received a B2 NRF rating in 2023.

Rendani Mbuvha is an Associate Professor of Actuarial Science at the University of the Witwatersrand, following previous appointments as an Associate Professor at the University of Manchester and a Google DeepMind Academic Fellow in Machine Learning at Queen Mary University of London. He holds a PhD in Electrical Engineering from the University of Johannesburg, where he was awarded the Google Africa PhD Fellowship in 2019.

His research spans the intersection of machine learning, climate risk, weather forecasting, and actuarial science, specifically targeting decision-making under uncertainty in data-scarce environments. He is the Principal Investigator for FineCast, a Bezos Earth Fund AI for Climate and Nature Grand Challenge project at Wits developing next-generation AI forecasting systems for African National Meteorological Services.

Rendani led the development of the Actuarial Society of South Africa’s (ASSA) Climate Index and co-founded AfriClimate AI, a pan-African research community behind the Google.org supported Forecast4Africa initiative. He is a Fellow of both ASSA and the Institute and Faculty of Actuaries, holding the Chartered Enterprise Risk Actuary (CERA) designation.

Şebnem Er is a Senior Lecturer in the Department of Statistical Sciences at the University of Cape Town, South Africa. Her research lies at the intersection of applied multivariate statistics, Bayesian modelling, spatial and spatio-temporal analysis, and data science, with particular interests in transport safety, public sector decision support. Her work often combines methodological development with real-world applications.

Her recent research has focused on Bayesian spatio-temporal models for traffic crash analysis in Cape Town, including work on collision counts at road intersections and city wide monitoring of crash severity trends. More broadly, she is interested in developing statistical methods that support evidence based policy and planning in complex urban systems. In addition to transport analytics, her academic interests include stratified sampling, spatial statistics, and multivariate analysis applied in many different areas.

At the University of Cape Town, she teaches and convenes undergraduate and postgraduate statistic courses, coordinates the Master’s in Data Science degree, and supervises Master’s and PhD students. Through both her teaching and research, she aims to strengthen the role of statistics in addressing socially important challenges in South Africa and beyond.

Bio coming soon.