Component elimination strategies to fit mixtures of multiple scale distributions

Florence Forbes is director of Research at INRIA and head of the Mistis team that she created in 2003. She received her PhD degree in applied probability from the University Joseph Fourier, Grenoble, France. She has been a research scientist at INRIA in Grenoble Rhone-Alpes in France since 1998.

She has been working on graphical Markov models, classification methods for spatially localized data and statistical image analysis for more than 20 years. Her publications range from 4 main domains showing a balance at the interface of Statistics and Probability, Machine Learning and Pattern recognition, Signal and Image processing and Biology and medicine. Her current interest consists mainly of model-based clustering methods, supervised (learning) or unsupervised (parameter estimation), statistical model selection and Bayesian techniques to integrate various sources of information and a priori.

She had experience with different types of data from domains as diverse as genetics and genomics, computer vision, and planetary science. She has coordinated a number of national projects and participated as co-pi to two European STREP projects.

Two-stage approach for occupancy modelling

Natalie Karavarsamis is a Lecturer in Statistics at La Trobe University. She obtained her PhD in Statistics from the Department of Mathematics and Statistics, University of Melbourne. She has worked in a number for statistical areas such as at the Cancer Council of Victoria, Department of Primary Industries, and research fellow at Department of Food and Land Sciences (University of Melbourne), and post doctoral researcher with School of Biosciences (University of Melbourne) and RMIT. She also is technical sub-editor at the Australian and New Zealand Journal of Statistics.

Her research interests include ecological statistics, analysis and modelling of large biological data and medical statistics. Modern statistical approaches for occupancy using partial, conditional and composite likelihoods along with GAMs. R software is available for these methods. And analysis of and developing models for large time series biological data, that includes functional analysis, HMMs and cylindrical distributions.

More information about Natalie can be found at https://natalie-karavarsamis.github.io.

Codeproof: Prepare for all weather conditions

Charles T. Gray is a proud mathbassador for the Australian Mathematical Sciences Institute’s Choose Maths program. Her role as a math-talking-doing-advocate grrrl is a relatively new career development, after spending almost twenty years working as a classically-trained pianist and music teacher. She studies at La Trobe University, where she is undertaking a PhD in statistical metaresearch.

Charles is committed to open and reproducible science, with an emphasis on facilitating good enough practices in scientific computing, as opposed to oft unattainable best practices. She believes that if we are to ask people to use more complex algorithms, then we must lower the programmatic barrier to their implementation. And thus Charles has fallen in with a motley crew of open science warriors who seek to bridge the toolchain gap between methodology and end-user.

With an interest in outreach, Charles takes an active role in a number of organisations, such as R-Ladies, a global initiative to promote diversity in statistical computing. She likes to think of herself as a data detective who tells stories with data. Charles thinks she has the best job in the world.

Collaborative Learning and Optimisation

Kai Qin is an Associate Professor at Swinburne University of Technology, Melbourne, Australia, currently serving as the Program Leader on “Data Analytics” in Swinburne Data Science Research Institute, the Leader of the Machine Learning and Intelligent Optimisation Research Group of the Institute, the Director of Swinburne’s Intelligent Data Analytics Lab, and the Course Director of Swinburne’s Master of Data Science Program.

Prior to joining Swinburne, he worked at the Nanyang Technological University (Singapore), the University of Waterloo (Canada), INRIA-Grenoble-Rhône-Alpes (France) and RMIT University (Australia). His major research interests include machine learning (particularly deep learning), optimisation (particularly evolutionary optimisation), intelligent data analytics (incl. image, text and time-series data), computer vision, remote sensing, GPU computing and services computing.

As an IEEE Senior Member, he is currently co-chairing the IEEE Computational Intelligence Society (CIS) Task Forces on “Collaborative Learning and Optimisation” and “Multitask Learning and Multitask Optimisation”, and also serving as the Vice-Chair of the IEEE CIS Technical Committee on Neural Networks.

Approximate Bayesian computation with discrepancy measurements

Hien Nguyen is a lecturer and Australian Research Council DECRA fellow, at La Trobe University. His work primarily revolves around the development of computational algorithms for the analysis of highly heterogeneous and large scale data.

Hien is currently serving as an Associate Editor, Handling Editor (Statistical Computing), and Technical Editor for the Australian and New Zealand Journal of Statistics. He is also an Associate Editor at the International Journal of Machine Intelligence and Sensory Signal Processing.

Presently, his work focuses on the development of deep learning techniques for the analysis of time series data, and the theoretical exploration of finite mixture models as a method for learning densities and classification rules. More information about Hien can be found on his website.

Statistical identification of important genes in biological systems

Dr. Pei Wang is currently an Associate Professor and Ph.D. supervisor with the School of Mathematics and Statistics, Henan University, Kaifeng, China. He was a Visiting Research Fellow with the School of Electrical and Computer Engineering, Royal Melbourne Institute of Technology, Melbourne, VIC, Australia. He has authored and co-authored over 30 journal papers with various ranks, including IEEE Trans. On Cybernetics, TBioCAS, TCBB, BMC Plant Biology, Scientific Reports. His current research interests include biostatistics, systems biology, and complex systems and networks. Dr Wang received the M.Sc. and Ph.D. degrees in computational mathematics from the School of Mathematics and Statistics, Wuhan University, Wuhan, China, in 2009 and 2012 respectively. Dr. Wang have been granted two research projects from the National Natural Science Foundation of China. He serves as a Reviewer of American Mathematical Reviews and a Technique Committee Member of the Complex Systems and Complex Networks Society of the Chinese Society for Industrial and Applied Mathematics.