Research support scheme (RSS)
The numbers
12
new projects funded (26 since inception)
14
Multi-institutional projects
80,000
Just under $80k funding committed in 2019 ($173,305 since inception)
59,000
Over $59k in co-contributions from external sources ($159,192 since inception)
The Research Support Scheme provides financial support for ACEMS Research Fellows and Associate Investigators and aims to increase cross-node collaboration between our early-career researchers.
The Research Support Scheme funds small to medium size research projects aligned with the Centre’s four Research Themes, as well as providing funding for extended research visits around Australia and overseas, funding for media, outreach or stakeholder activities, and for students to undertake these activities. Researchers can request up to $20,000 per annum, per application to support their research project.
ACEMS particularly encourages early-career researchers to apply to gain experience with writing grant applications, developing and monitoring research project budgets, supervising a research assistant and administering successful research projects.
In 2019 ACEMS saw the Research Support Scheme continue to grow with 12 new applications approved for funding worth a total of $79,080, with a total of $59,537 of external co-funding. This brings the Centre’s total investment in the Research Support Scheme to $173,305 (with $159,192 in external co-funding) across 26 projects since the scheme’s establishment in late-2017.
A total of 12 new projects commenced in 2019, bringing the total number of active projects to 17 in 2019. Many of these projects involved investigators from multiple ACEMS nodes as well as other institutions from around Australia and overseas.
| Project Title | Lead Investigator | Co-investigators | Project dates |
|---|---|---|---|
| A novel framework to study dynamic default dependence and network effects of disruption risk across homogeneous customer networks | Laleh Tafakori (RMIT) | Nov 2017 – Mar 2019 | |
| Academic Visit to University of Bordeaux, Paris and London. | Tony Guttmann (UoM) | Mireille Bousquet-Melou (University of Bordeaux), Andrew Elvey-Price (University of Bordeaux), Jean-Marie Maillard (University of Paris), Alan Sokal (University College London) | Apr 2019 – May 2019 |
| Accelerating computationally intensive problems in Bayesian statistics with the Intel Many Integrated Core architecture | Christopher Drovandi (QUT) | Scott Sisson (UNSW), Neil Kelson (QUT) | Mar 2018 – Jun 2019 |
| Anomaly detection in streaming water-quality data* | Catherine Leigh (QUT) | Kerrie Mengersen (QUT), Erin Peterson (QUT), Benoit Liquet (QUT), Rob Hyndman (MU), James McGree (QUT), Sevvandi Kandanaarachchi (MU), Priyanga Dilini Talagala (MU) | Jun 2019 – Jul 2019 |
| Approximate Solutions to Large Markov Decision Processes* | Fred Roosta (UQ) | Ali Eshragh (Newcastle), Michael Mahoney (University of California, Berkeley), Yinyu Ye (Stanford University) | Jan 2019 – Dec 2019 |
| Deep Bayesian inference methods for big data modelling* | Hongbo Xie (QUT) | Xuhui Fan (UNSW) | Jul 2019 – Dec 2019 |
| Efficient Bayesian inference for large and complex data* | Matias Quiroz (UTS) | Mattias Villani (Linkoping) | Dec 2019 – Jan 2020 |
| Finite-size scaling of the Fortuin-Kasteleyn Ising model on high dimensional lattices* | Eric Zhou (MU) | Tim Garoni (MU), Youjin Deng (University of Science and Technology of China), Sheng Fang (University of Science and Technology of China) | May 2019 – Jun 2019 |
| Improved Algorithms for Environmental Monitoring Network Design Problems* | Radislav Vaisman (UQ) | Mar 2020 – Mar 2021 | |
| Ion channel kinetics and enzyme kinetics: beyond the classical paradigm | Shev MacNamara (UTS) | Pamela Burrage (QUT) | Apr 2018 – Nov 2019 |
| Modelling seagrass dynamics using hybrid strategy combining deterministic process-based and DBN models* | Paul Wu (QUT) | Kerrie Mengersen (QUT), Heloise Muller (IFREMER), Martin Marzloff (IFREMER), Benoit Liquet (QUT) | Jul 2019 – Dec 2020 |
| Probabilistic analysis of Markov-modulated diffusion processes and applications* | Matthieu Simon (UoM) | Giang Nguyen (UoA) | Jun 2019 – Aug 2020 |
| Scalable Bayesian inference methods in the problem of matrix factorization | Xuhui Fan (UNSW) | Hongbo Xie (QUT) | Mar 2019 – May 2019 |
| Statistical inference and applied probability project | Azam Asanjarani (UoM/Auckland) | Peter Taylor (UoM), Yoni Nazarathy (UQ) | Aug 2018 – Dec 2019 |
| Study of near extreme eigenvalues in Beta=1,4 random matrix ensembles* | Anthony Mays (UoM) | Anita Ponsaing (UoM), Gregory Schehr (LPTMS, Université de Paris-Sud) | May 2019 – Jun 2019 |
| Study of urban traffic networks using real-time parameter-free data analysis* | Lele (Joyce) Zhang (UoM) | Tim Garoni (MU), Aurore Delaigle (UoM), Nan Zheng (MU) | Jan 2020 – Mar 2020 |
| Study of the geometric properties of a certain class of polytopes to solve the Hamiltonian Cycle Problem* | Asghar Moeini (UoM) | Ali Eshragh (Newcastle) | Aug 2019 – Sep 2019 |
| Subspace Methods for High-Dimensional Bayesian Statistics with Application to Remote Sensing and X-ray Imaging | Tiangang Cui (MU) | May 2018 – May 2019 | |
| The extinction probability of branching processes with countably many types* | Sophie Hautphenne (UoM) | Peter Braunsteins (UoM) | Jul 2019 |
* Applications approved in 2019
Recipients of the Research Support Scheme have organised workshops, provided new students with support, submitted papers to peer-reviewed journals, travelled across Australia and internationally to collaborate, and importantly, undertaken cross-node collaboration, which is what the scheme was designed to encourage.
Some of the journal papers produced by these projects have been accepted for publication or are currently under review – some recent examples are given below; links to pre-print articles are given for articles under review.
Refereed Journal articles
Li, C., Xie, H.-B., Fan, X., Da Xu, R. Yi, Van Huffel, S., Sisson, S. A., & Mengersen, K. (2019). Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator. IEEE Transactions On Image Processing, 28(10), 4899 - 4911. https://doi.org/10.1109/TIP.2019.2912292
Pre-prints:
Eshragh, A., Roosta, F., Nazari, A., & Mahoney, M. W. (2019). LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. Arxiv, arXiv:1911.12321v2. https://arxiv.org/abs/1911.12321v2
Fang, S., Grimm, J., Zhou, Z., & Deng, Y. (2019). Complete graph and Gaussian fixed point asymptotics in the five-dimensional Fortuin-Kasteleyn Ising model with periodic boundaries. Arxiv, arXiv:1909.04328v2. https://arxiv.org/abs/1909.04328v2
Priddle, J. W., Sisson, S. A., & Drovandi, C. C. (2019). Efficient Bayesian synthetic likelihood with whitening transformations. Arxiv, arXiv:1909.04857v1. https://arxiv.org/abs/1909.04857v1
Salomone, R., Quiroz, M., Kohn, R., Villani, M., & Tran, M.-N. (2019). Spectral Subsampling MCMC for Stationary Time Series. Arxiv, arXiv:1910.13627v1. https://arxiv.org/abs/1910.13627v1
Warne, D. J., Sisson, S. A., & Drovandi, C. (2019). Acceleration of expensive computations in Bayesian statistics using vector operations. Arxiv, arXiv:1902.09046v2. https://arxiv.org/abs/1902.09046v2