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Australian partners

University of Queensland, Brisbane

Geoff McLachlan: Professor, University of Queensland, Brisbane. Geoff’s research primarily consists of inferential methods of mixture models and their applications in computer science, engineering, genetics, and medical settings. He is particularly known for his popular books: The EM Algorithm and Extensions (with T. Krishnan), Finite Mixture Models (with D. Peel), Analyzing Micro Gene Expression Data (with K-A Do, and C. Ambroise), Discriminant Analysis and Statistical Pattern Recognition, and Mixture Models: Inference and Applications to Clustering (with K. Basford). In 2015, Geoff was honoured with a Fellowship of the Australian Academy of Science for his academic achievements and service.

Sharon Lee: Senior Lecturer, University of Queensland, Brisbane. Sharon’s research consists largely of the development of novel distributions for modeling complex heterogeneous data, with particular emphasis on the application and estimation of such models. Her primary output consists of the characterization of skewness and heavy tail phenomena via parametric probability models. Sharon was previously awarded a prestigious ARC DECRA Fellowship in 2015.

Hien Duy Nguyen:  Senior Lecturer, University of Queensland, Brisbane. Hien focuses on optimization and computational techniques for estimation and inference in complex data models. He is particularly devoted to the study of majorization–minimization algorithms and stochastic variants, finite mixture models and related approaches, and non-asymptotic methods for constructing hypothesis tests and confidence sets. Hien previously was awarded a prestigious ARC DECRA Fellowship in 2016.

TrungTin Nguyen: Post-doc fellow. Previously a post-doc at Inria, Statify, Tin started his post-doc at UQ in December 2023. His interests in the project are related to ABC, mixtures of expert models, neural
networks and statistical model selection.

Chris van der Heide: Post-doc Fellow, University of Queensland, Brisbane. Since completing his PhD in 2016 at the University of Queensland, on the topic of partial differential equations, Chris has conducted research as part of the nationwide ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research focuses on simulation and optimization problems in large scale AI systems such as neural networks and normalizing flow models.

Daniel Ahfock: Post-doc Fellow, University of Queensland, Brisbane. Daniel obtained his PhD in 2019 at the University of Cambridge. He has since conducted research at the University of Queensland, under the guidance of Prof. Geoff McLachlan on the topic of semi-supervised learning via finite mixture models and ML methods. He also has strong research interests in Bayesian methods and matrix sketching approaches.

Queensland university of technology, Brisbane

Chris Drovandi: Professor in Statistics and Data Science, Queensland University of Technology, Brisbane. Chris currently holds an ARC Future Fellowship for his current research in scalable and robust Bayesian methods for implicit statistical models. He has particular interest in likelihood-free Bayesian approaches such as Approximate Bayesian Computation and Bayesian Synthetic Likelihood, as well as sampling approaches, such as sequential Monte Carlo methods. Chris was previously a recipient of an ARC DECRA Fellowship in 2016.

Leah South (also known as Leah Price): Lecturer at Queensland University of Technology. Leah’s work is directed towards the Bayesian inference topics of Monte Carlo variance reduction techniques, applications of Stein’s method, scalable Monte Carlo methods, Approximate Bayesian Computation, and Sequential Monte Carlo sampling. Leah has previously conducted research as a Senior Research Associate at Lancaster University and is the Secretary of the Computation Section of the International Society for Bayesian Analysis.

Darren Wraith: Associate Professor, Queensland University of Technology, Brisbane. Darren received his PhD in 2008 and spent five years working internationally, as a Postdoc at Universite Paris-Dauphine and as a Research Engineer at Inria Grenoble Rhone-Alpes. His research is spread across topics in statistical methodology, with particular focus on mixture models, Bayesian inference, and high dimensional analysis, as well as applied statistics and biostatistics, including research in problem gambling, environmental sciences, and public health. Darren’s current research is funded by a prestigious ARC
Linkage Project grant, and he has previously been funded by the NHMRC: Australia’s primary health research funding agency.

Mitchell O’Sullivan PhD student, co-advised by C. Drovandi and L. South. Mitchell aims to develop new adaptive sequential Monte-Carlo ABC (SMC ABC) algorithms and create a Julia package for SMC ABC methods, potentially including new methods developed by the partners, to make them more accessible for practitioners.

Ryan Kelly PhD student, co-advised by C. Drovandi and L. South on scalable and robust Likelihood-Free Bayesian Inference. Ryan aims to consider scalable approaches for likelihood-free inference, such as Bayesian synthetic likelihood (BSL) and neural network based approaches, and advance them to make them robust to model misspecification. One possible added value to the project is a similar approach to make the other partners’ methods (ABC) more robust.

Monash University, Melbourne

David Frazier: Associate Professor in Econometrics and Business Statistics at Monash University, and a current Australian Research Council (ARC) Discovery Early Career Research Award (DECRA) Fellow. David’s research primarily focuses on the theoretical and computational aspects of simulation-based Bayesian inference, where he has made useful contributions to the underlying theory of simulation-based inference procedures such as approximate Bayesian computation and Bayesian synthetic likelihood.

Swinburne university, Melbourne

Kai Qin: Professor, Swinburne University of Technology, Melbourne. Kai currently serves as the Director of the Swinburne Intelligent Data Analytics Lab, the Deputy Director of the Space Technology and Industry Institute, and the Program Lead for Data Analytics in the Data Science Research Institute. His primary research interests revolves around the field of Computational Intelligence, where he is largely interested in computational paradigms for solving complex real-world problems for which traditional methods are infeasible or ineffective. Notably, he is interested in the study of neural networks and deep learning systems, as well as remote sensing and computer vision applications. Kai is the chair of the IEEE Neural Networks Technical Committee and the Vice-Chair of the IEEEEmergent Technologies Task Force on “Multitask Learning and Multitask Optimisation”.

Griffith University, Brisbane

Shu-Kay Angus Ng: Professor of Biostatistics, Griffith University, Brisbane. Angus serves as a senior biostatistician in the Griffith University School of Medicine and Dentistry, where he applies his expertise in mixture modeling, random effects models, and machine learning and survival analysis to develop inferential approaches for bioinformatics, medical imaging, health economics, and clinical trials. Angus was appointed to the ARC College of Experts in 2022, where he provides advice and guidance on the targets of national research funding.

French partners

University of Toulouse

Gersende Fort: CNRS senior researcher, IMT, University of Toulouse. Her interests in the project are related to stochastic approximation and optimization, online and federated learning, sampling and Monte-Carlo Markov Chain (MCMC) techniques.


Guillaume Kon Kam King: INRAE senior researcher. His interests in the project
are related to approximate Bayesian computation, Bayesian nonparametrics and mixture
models. Guillaume is also the co-advisor of Louise Alamichel.


Jean-Baptiste Durand: CIRAD Senior researcher, AMAP laboratory, Montpellier. Jean-Baptiste has been in the Statify and the former Mistis team at Inria since their creation and until 2022. His interests in the project are related to approximate Bayesian inference and statistical model selection.

Inria Grenoble Rhone-Alpes

Julyan Arbel: INRIA Senior researcher. His interests in the project are related to Bayesian neural networks, Bayesian parametric and nonparametric statistics, and more specifically approximate Bayesian computation (ABC).

Florence Forbes: INRIA Senior researcher, Head of the Statify team. Her interests in the project are related to Bayesian parametric and nonparametric statistics, simulation-based inference (SBI), ABC, clustering and regression techniques; in particular in non standard cases, including high dimensionality, heavy tail modeling and missing observations. She has been also working on medical imaging applications for many years.

Pedro Rodrigues: INRIA Junior researcher. His interests in the project are related to Bayesian inverse problems, SBI, invertible neural networks and medical imaging applications.

Kostas Pitas: Post-doc fellow. Kostas started his post-doc in the team in February 2022. His interests in the project are related to Bayesian neural networks and theoretical tools to study their properties.

Pierre Wolinski: Post-doc fellow. Pierre started his post-doc in the team in January 2022. His interests in the project are related to Bayesian and approximate inference, in particular in combination with neural networks.

Louise Alamichel: PhD student, Statify and INRAE. Louise is supervised by J. Arbel and G. Kon Kam King since October 2021. Her works deals with Bayesian nonparametric models for genetic data.

Geoffroy Oudoumanessah: PhD student, Statify, Grenoble Institute of Neuroscience (GIN) and Creatis lab. Geoffroy has been an intern in the team since Spring 2022 and has started his PhD in October 2022. He is co-advised by F. Forbes, M. Dojat (GIN) and C. Lartizien (Creatis). His work deals with online algorithms for non Gaussian clustering and learning techniques for brain magnetic resonance (MR) imaging and anomaly detection.

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