Keynote Speakers
Renate Meyer
Professor at The University of Auckland
Renate is Professor of Statistics at the University of Auckland. After obtaining an MSc and PhD in Mathematics and Statistics from the University of Aachen, Germany, she took up a lectureship at the University of Auckland in 1994. In 2010, 2017, and 2018, she held visiting professorships at the Karlsruhe Institute of Technology, the Otto-von-Guericke University Magdeburg in Germany, and the Observatoire de la Côte d’Azur in France. She was awarded a James Cook Research Fellowship by the Royal Society of NZ in 2018 for research on noise characterization studies for laser-interferometric gravitational wave observatories and the Littlejohn Research Award of the NZ Statistical Society in 2020.
Renate has wide research interests in applied Bayesian inference, in particular time series analysis with applications in astrophysics, state-space modelling in ecology, multivariate modelling using copulas, survival analysis in medical statistics, and stochastic volatility models for financial time series.
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Richard Arnold
Professor at Victoria University of Wellington
Richard Arnold is a Professor of Statistics and Data Science. He started out in astronomy, but later moved into statistics. He worked first in epidemiology, but more recently has worked in reliability theory, statistical seismology and clustering methods for ordinal data.
Geoff Nicholls
Associate Professor at Oxford
Geoff is an Associate Professor in the Statistics Department at the University of Oxford, with research interests in statistical methods, computational statistics and machine learning. He did a PhD in Theoretical Physics at Cambridge (1986-90) followed by a postdoc in the Vision, Speech and Signal Processing lab in Engineering at the University of Surrey. He got into statistics as a postdoc in Oxford 1992-95 working with Peter Clifford. After 10 years in the Mathematics Department at the University of Auckland, Geoff returned to Oxford in 2005.
Alexei Drummond
Professor at Auckland
Alexei Drummond is a Professor in Computational Biology at the University of Auckland, holding positions in both the School of Biological Sciences and the School of Computer Science. He established the Centre for Computational Evolution, where his research focuses on developing mathematical models for molecular evolution and population genetics, implemented in the widely used BEAST software. A leader in Bayesian phylogenetics and coalescent-based population genetics, Professor Drummond has an H-index of 89 and over 120,000 citations. He is a Fellow of the Royal Society of New Zealand, a Clarivate Highly Cited Researcher, and has received numerous awards, including the New Zealand Open Source Award for BEAST2. He has published extensively in top-tier journals such as Science, Nature, and PNAS, and is an active lecturer in international workshops, including the “Taming the BEAST” series.
Ali Mohammad-Djafari
Ali Mohammad-Djafari received the B.Sc. in electrical engineering from Polytechnic of Teheran, in 1975, the M.Sc. from Supélec (Now CentraleSupélec) in 1977, the “Docteur-Ingénieur” (Ph.D.) and “Doctorat d’Etat” in Physics, from the University of Paris Sud 11 (UPS), Orsay, France, respectively in 1981 and 1987.
He has been Research Director at CNRS, and Professor of universities in France and in many other countries until his retirement in 2018. Now, part-time, in China as the Chief Scientist in Shanfeng company, Shaoxing, Zhejiang Province.
His main scientific interests include:
– Developing new methods based on Bayesian inference and Information Theory approaches for Inverse Problems, and in general, in all aspects of data processing, and more specifically in imaging and vision systems.
– Multivariate and multi dimensional data, space-time signal and image processing, data mining, clustering, classification, machine learning and artificial intelligence methods for Diagnostics and preventive maintenance.
The main application domains of his interests are Medical or biological imaging, Computed Tomography, Non Destructive Testing (NDT), fault diagnostic and preventive maintenance in industry.