RESEARCH SUPPORT SCHEME (RSS)
The numbers
new projects funded (32 since inception)
Over
($) funding committed in 2020 ($231,696.33 since inception)
($) in co-contributions from external sources ($168,042 since inception)
multi-institutional projects
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 2020 ACEMS saw the Research Support Scheme continue to grow with six new applications approved for funding worth a total of $58,391.33, with a total of $8,850 of external co-funding. This brings the Centre’s total investment in the Research Support Scheme to $231,696.33 (with $168,042 in external co-funding) across 32 projects since the scheme’s establishment in late-2017.
A total of six new projects commenced in 2020, bringing the total number of active projects to nine in 2020. Many of these projects involved investigators from multiple ACEMS nodes as well as other institutions from around Australia and overseas.
2020 Research Support Scheme Projects
| Project Title | Lead Investigator | Co-investigators | Project dates |
|---|---|---|---|
| Big Time Series Data and Randomised Numerical Linear Algebra* | Fred Roosta (UQ) | Ali Eshragh (University of Newcastle), Michael Mahoney (UC Berkeley), David Woodruff (Carneigie Mellon University), Asef Nazari (Deakin University), Glen Livingston (University of Newcastle) | Feb 2020 – Jan 2021 |
| Efficient Bayesian inference for large and complex data** | Matias Quiroz (UTS) | Mattias Villani (Linkoping) | Dec 2019 – Jan 2020 |
| Exploiting composite likelihoods for likelihood-free problems* | Clara Grazian (UNSW) | Chris Drovandi (QUT) | Jan 2021 – Jun 2021 |
| Finding Minimum Label Spanning Trees using Cross-Entropy Method* | Radislav Vaisman (UQ) | Matthew Roughan (Adelaide) | Apr 2020 – Apr 2021 |
| Improved Algorithms for Environmental Monitoring Network Design Problems** | Radislav Vaisman (UQ) | Mar 2020 – Mar 2021 | |
| Mathematical modelling of COVID19 cluster surveillance* | Andrew Black (Adelaide) | Sophie Hautphenne (UniMelb) | Jun 2020 – Jan 2021 |
| 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 |
| Non-Stationary Basis Decomposition for Discontinuous Signals* | Dorota Toczydlowska (UTS) | Gareth Peters (Heriot-Watt University), Marta Campi (University College London) | Apr 2020 – Jul 2020 |
| Probabilistic analysis of Markov-modulated diffusion processes and applications** | Matthieu Simon (UniMelb) | Giang Nguyen (UoA) | Jun 2019 – Aug 2020 |
| Stochastic Analysis of the COVID-19 Population* | Ali Eshragh (Newcastle) | Saed Alizamir (Yale University), Peter Howley (University of Newcastle), Elizabeth Stojanovski (University of Newcastle), Judy-Anne Osborn (University of Newcastle) | Jul 2020 – Nov 2020s |
* Applications approved in 2020
**Applications active in 2020 but approved in previous years
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.