Professor Matt Wand is a Distinguished Professor of Statistics at University of Technology Sydney.
He has held faculty appointments at Harvard University, Rice University, Texas A&M University, University of New South Wales and University of Wollongong. In 2008 Professor Wand became an elected Fellow of the Australian Academy of Science. He also has been awarded two Australian Academy of Science honorific awards for statistical research: the Moran Medal in 1997 for outstanding research by scientists under the age of 40 and the Hannan Medal in 2013 for career research in statistical science. In 2013 he was awarded the University of Technology Sydney, Chancellor's Medal for Exceptional Research. He received the 2013 Pitman Medal from the Statistical Society of Australia in recognition of outstanding achievement in, and contribution to, the discipline of Statistics. Professor Wand is an elected fellow of the American Statistical Association, the Institute of Mathematical Statistics and the Australian Mathematical Society.
Professor Wand has co-authored two books and more than 115 papers in statistics journals. He has six packages in the R language on the Comprehensive R Archive Network.
In 2002 Professor Wand was ranked 23 among highly cited authors in mathematics and statistics for the period 1991–2001. He is also a member of the ‘ISI Highly Cited Researchers’ list. Since 2000 Professor Wand has been principal investigator on seven major grants.
Professor Wand is chiefly interested in the development of statistical methodology for finding useful structure in large multivariate data sets. Currently, Matt’s specific interests include: fast approximate statistical inference, message passing algorithms, statistical methods for streaming data, generalised linear mixed models and semiparametric. He is also very interested in Statistical Computing and contributes to the field's main software repository — the ‘Comprehensive R Archive Network’.
Generalised linear mixed models
Message passing algorithms
Monte Carlo Methods
Real-time data analysis
Semiparametric regression modelling
PhD, Australian National University, 1989
Bachelor of Mathematics (Honours), University of Wollongong, 1986
From nearly 17,000 measurements of coral position and growth on a reef in French Polynesia, an international research team teased out which reef-building species were most sensitive to attack from crown-of-thorns starfish and destruction during tropical cyclones, and what times of their life history.
Recently, Chief investigator Wand and his has group published Kim & Wand (2016, Electronic Journal of Statistics), Wand (2017, Journal of American Statistical Association) and Nolan & Wand (2017, Stat) on message passing approaches to semiparametric regression analysis.
Invited talks, refereed proceedings and other conference outputs
Degani, E., Maestrini L., Toczydlowska D., & Wand M.
(2021). Streamlined Variational Inference for High Dimensional Mixed Models with Fixed Effects Selection. Australian and New Zealand Statistical Conference (ANZSC 2021).
Maestrini, L., Aykroyd R. G., & Wand M.
(2019). Compartmentalisation of Variational Approximate Inference for Inverse Problems Models. 3rd International Conference on Econometrics and Statistics (EcoSta 2019).
(2019). Variational Message Passing for Elaborate Response Regression Models. Joint Statistical Meetings 2019.
(2019). Variational Message Passing for Elaborate Response Regression Models. AutoStat Research Week: Frontiers in Research & Practice in Statistics.
Maestrini, L., Tan L. S. L., & Wand M.
(2019). Double-loop expectation propagation for statistical models. 2019 ACEMS Enabling Algorithms Theme Symposium.
Hall, P.., Johnstone I.M.., Ormerod J.T.., Wand M., & Yu J.C.F..
(2019). Frequentist Expectation Propagation. Statistical Methods for the Analysis of High-Dimensional and Massive Data Sets.
Maestrini, L., Aykroyd R. G., & Wand M.
(2019). Streamlined Variational Inference for Inverse Problems Models. The 12th International Conference on Monte Carlo Methods and Applications.
(2019). Streamlined Variational Inference for Random Effects Models. Data Science Down Under 2019.
Menictas, M., & Wand M.
(2015). Variational Inference for Heteroscedastic Semiparametric Regression. Australian & New Zealand Journal of Statistics. 57(1), 119-138. doi: 10.1111/anzs.12105
Kayal, M., Vercelloni J., Wand M., & Adjeroud M.
(2015). Searching for the best bet in life-strategy: A quantitative approach to individual performance and population dynamics in reef-building corals. Ecological Complexity. 23, 73-84. doi: 10.1016/j.ecocom.2015.07.003