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Professor Matt Wand
Professor
University of Technology Sydney
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.
For more information visit his personal website http://matt-wand.utsacademics.info.
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’.
Research Interests:
Analytics
Computational statistics
Data Science
Expectation propagation
Generalised linear mixed models
Graphical models
Message passing algorithms
Monte Carlo Methods
Nonparametric regression
Real-time data analysis
Semiparametric regression modelling
Spatial statistics
Variational approximations
Qualifications:
PhD, Australian National University, 1989
Bachelor of Mathematics (Honours), University of Wollongong, 1986
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
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.
Wand, M.
(2019). Streamlined Variational Inference for Random Effects Models.
Data Science Down Under 2019.
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).
Wand, M.
(2019). Variational Message Passing for Elaborate Response Regression Models.
Joint Statistical Meetings 2019.
Wand, M.
(2019). Variational Message Passing for Elaborate Response Regression Models.
AutoStat Research Week: Frontiers in Research & Practice in Statistics.
Michaelson, G., Roughan M., Tuke J., Wand M., & Bush R.
(2018). Rasch analysis of HTTP reachability.
Proceedings of International Federation for Information Processing Networking 2018. 1, 1-9. doi: 10.23919/IFIPNetworking.2018.8696525
Journal Articles
Menictas, M.., Nolan T.H.., Simpson D.G.., & Wand M.
(2021). Streamlined variational inference for higher level group-specific curve models.
Statistical Modelling. 21(6), 479-519. doi: 10.1177/1471082X20930894
Nolan, T. H., & Wand M.
(2020). STREAMLINED SOLUTIONS TO MULTILEVEL SPARSE MATRIX PROBLEMS.
The ANZIAM Journal. 62(1), 18 - 41. doi: 10.1017/S1446181120000061
Hall, P.., Johnstone I.M.., Ormerod J.T.., Wand M., & Yu J.C.F..
(2020). Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation.
Journal of the American Statistical Association. 115(532), 1902-1916. doi: 10.1080/01621459.2019.1665529
Chen, W. Y., & Wand M.
(2020). Factor graph fragmentization of expectation propagation.
Journal of the Korean Statistical Society. 49(3), 722 - 756. doi: 10.1007/s42952-019-00033-9
McLean, M.. W., & Wand M.
(2019). Variational Message Passing for Elaborate Response Regression Models.
Bayesian Analysis. 14(2), 371 - 398. doi: 10.1214/18-BA1098,10.1214/18-BA1098SUPP
Maestrini, L., & Wand M.
(2018). Variational message passing for skew t regression.
Stat. 7(1), e196. doi: 10.1002/sta4.196
Luts, J., Wang S. S. J., Ormerod J. T., & Wand M.
(2018). Semiparametric Regression Analysis via Infer.NET.
Journal of Statistical Software. 87(2), doi: 10.18637/jss.v087.i02
Liu, S. H., Bobb J. F., Henn B. Claus, Schnaas L., Tellez-Rojo M. M., Gennings C., et al.
(2018). Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression.
Environmetrics. 29(4), e2504. doi: 10.1002/env.2504
Kim, A. S. I., & Wand M.
(2018). On expectation propagation for generalised, linear and mixed models.
Australian & New Zealand Journal of Statistics. 60(1), 75 - 102. doi: 10.1111/anzs.12199
Pham, T., & Wand M.
(2018). Generalised additive mixed models analysis via.
Australian & New Zealand Journal of Statistics. 60(3), 279 - 300. doi: 10.1111/anzs.2018.60.issue-3,10.1111/anzs.12241
Michaelson, G., Roughan M., Tuke J., Wand M., & Bush R.
(2017). Rigorous statistical analysis of HTTPS reachability.
arXiv preprint arXiv:1706.02813.
Wand, M.
(2017). Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing.
Journal of the American Statistical Association. 112(517), 137 - 168. doi: 10.1080/01621459.2016.1197833
Nolan, T. H., & Wand M.
(2017). Accurate logistic variational message passing: algebraic and numerical details.
Stat. 6(1), 102-112. doi: 10.1002/sta4.139
Y. Y Lee, C., & Wand M.
(2016). Variational methods for fitting complex Bayesian mixed effects models to health data.
Statistics in Medicine. 35(2), 165-188. doi: 10.1002/sim.6737
Kim, A. S. I., & Wand M.
(2016). The explicit form of expectation propagation for a simple statistical model.
Electronic Journal of Statistics. 10(1), 550-581. doi: 10.1214/16-EJS1114
Ryan, L. M., Wand M., & Malecki A. A.
(2016). Bringing coals to Newcastle.
Significance. 13(6), 32-37. doi: 10.1111/j.1740-9713.2016.00982.x
Delaigle, A., & Wand M.
(2016). A conversation with Peter Hall.
Statistical Science. 31(2), 275-304. doi: 10.1214/16-STS554
Rohde, D., & Wand M.
(2016). Semiparametric mean field variational Bayes: General principles and numerical issues.
Journal of Machine Learning Research. 17(172),
Lee, C. Y. Y., & Wand M.
(2016). Streamlined mean field variational Bayes for longitudinal and multilevel data analysis.
Biometrical Journal. 58(4), 868-895. doi: 10.1002/bimj.201500007
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
Dubossarsky, E., Friedman J. H., Ormerod J. T., & Wand M.
(2014). Wavelet-based gradient boosting.
Statistics and Computing. 26(1-2), 93-105. doi: 10.1007/s11222-014-9474-0
Publicly available softwares
Wand, M., & Yu J.
(2019). glmmEP: Generalized Linear Mixed Model Analysis via Expectation Propagation.
Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation.
Wand, M., & Yu J.F.C..
(2019). Wand, M.P. and Yu, J.F.C. (2019). glmmEP 1.0. Generalized linear mixed model analysis via expectation propagation. R package. https://CRAN.R-project.org/package=glmmEP.
Wand, M., & Pham T.
(2019). gammSlice: Generalized Additive Mixed Model Analysis via Slice Sampling.
Generalised additive mixed models analysis via gammSlice.
Wand, M., & Yu J.F.C..
(2018). glmmEP 1.0.
Harezlak, J.., Ruppert D.., & Wand M.
(2018). HRW 1.0. Datasets, functions and scripts for semiparametric regression supporting Harezlak, Ruppert & Wand (2018). R package. https://CRAN.R-project.org/package=HRW.
