University of Queensland, Brisbane
- Geoff McLachlan: Professor, University of Queensland, Brisbane. His interests in the project are related to classification, clustering and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition.
- Sharon Lee: Senior Lecturer, University of Queensland, Brisbane, since 2021. Her interests in the project are related to flexible data modelling via skew mixture models.
- Hien Nguyen: Senior Lecturer, University of Queensland, Brisbane, since 2021. Hien’s research focuses on construction of computationally efficient and feasible algorithms for the estimation of mixture-type models, such as via minorization-maximization algorithms or maximum pseudolikelihood estimation.
Queensland university of technology, Brisbane
- Darren Wraith : Senior Lecturer, Queensland University of Technology, Brisbane. His interests in the project are related to latent variable modelling and Bayesian statistics with application to medical imaging.
La Trobe University, Melbourne
- Hien Nguyen: Lecturer and Research Fellow until 2020, Department of Mathematics and Statistics, La Trobe University, Melbourne. Hien’s research focuses on construction of computationally efficient and feasible algorithms for the estimation of mixture-type models, such as via minorization-maximization algorithms or maximum pseudolikelihood estimation.
- Luke Prendergast: Associate Professor, Head of Department of Mathematics and Statistics, La Trobe University, Melbourne. Luke is an expert in dimensionality reduction techniques such as sliced inverse regression and single-index modelling.
- Natalie Karavarsamis: Lecturer and Research Fellow, Department of Mathematics and Statistics,
La Trobe University, Melbourne. Her research interests include ecological statistics, analysis and modelling of large biological data and medical statistics.
- Jessica Bagnall: PhD student, La Trobe University. Jessica is supervised by H. Nguyen until 2020 and is currently working on a project revolving around the development of Boltzmann machine networks for statistical inference, and the use of archetypal analysis and similar techniques for data quantisation, especially in neuroimaging. Jessica also provides insight into the interpretation of neuroimaging results, from her previous research and studies in psychology.
- Mason Terrett: PhD student, La Trobe University. Mason is supervised by H. Nguyen until 2020. He is currently working on data mining and machine learning techniques for the analysis of satellite and radar imaging, for the purpose of invasive vegetation remote sensing. This project is in collaboration with the Commonwealth Scientific and Industrial Research Organisation of Australia (CSIRO).
Swinburne university, Melbourne
- Kai Qin: Associate Professor and Director of the Intelligent Data Analytics Lab. at Swinburne University of Technology, Melbourne. Kai is currently interested in large-scale distributed learning and optimization on the basis of transfer learning and transfer optimization. In addition to his knowledge of deep neural networks, Kai will also bring to the project his expertise in optimization and computational techniques.
University of Adelaide, Adelaide
- Sharon Lee: Senior Lecturer, University of Adelaide until 2020. Her interests in the project are related to flexible data modelling via skew mixture models.
University of Caen
- Faicel Chamroukhi: Professor at University of Caen, his research interests include statistical learning, latent data models, multivariate and functional data analysis, pattern recognition, statistical signal and image processing, and their applications. F. Chamroukhi spent a year at Inria Lille (MODAL) in 2015/16 and is since an associate member of the team. Some of his projects include Inria partners (such as the upcoming ANR SMILES, 2018-2022).
- Trung Tin Nguyen: PhD Student, University of Caen, now post-doc fellow at Inria Statify since 2022. Tin started his PhD in October 2018 on a doctoral contract and is supervised by F. Chamroukhi. His thesis research is on unsupervised learning of feature hierarchies using deep mixtures of experts.
Inria Grenoble Rhone-Alpes
- Julyan Arbel: Junior researcher, Statify team. His interests in the project are related to Bayesian parametric and nonparametric statistics.
- Jean-Baptiste Durand: Senior lecturer, Statify team. His interests in the project are related to probabilistic graphical models, count data and Bayesian nonparametrics.
- Florence Forbes: Senior researcher, Head of the Statify team. Her interests in the project are related to 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.
- Stephane Girard: Senior researcher, Statify. His interests in the project are related to nonparametric statistics, regression and statistical learning in high dimension.
- Antoine Usseglio-Carleve: Post-doc fellow, Mistis until 2020. His research interests include risk measures properties, extreme risk measures estimation, multivariate distributions, regression models and high-dimensional statistics.
- Benoit Kugler: PhD student, Statify and Grenoble Planetology Institute (IPAG). Benoit started in September 2018 and is co-advised by Sylvain Doute (IPAG) and F. Forbes (Mistis). The objective is to implement a statistical learning technique capable of solving a complex inverse problem in planetary remote sensing.
- Veronica Munoz-Ramirez: PhD student, Statify and Grenoble Institute of Neuroscience (GIN). Veronica started in October 2017. She is co-advised by M. Dojat (GIN), J. Arbel and F. Forbes (Statify). The goal is to develop a pipeline based on robust non Gaussian clustering and model selection tools to exploit quantitative multiparametric MR data that integrate several microvascular MRI parameters and can be used to design new biomarkers for Parkinson’s disease.
- Fabien Boux: PhD student, Mistis and Grenoble Institute of Neuroscience (GIN). Fabien started in September, 2017. He is co-advised by E. Barbier (GIN), J. Arbel and F. Forbes (Statify). In his thesis we target the analysis of MR fingerprint data with regression and inversion techniques.