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Like mathematical modelling and data? Our symposium “Data-Driven Modelling in Mathematical Biology” is coming up on Monday 28th. Registrations closed, but DM me for Zoom link for talks! modellingwithdata.com

Raiha Browning Raiha Browning gave a fantastic talk at the ISBA conference on her work on taking a Hawkes eye view of covid-19. Paper in PLOS journals.plos.org/plosone/articl…

The #Bayesian session includes these ACEMS members: - Luca Maestrini, UTS Science - Boris Beranger, UNSW - Raiha Browning, QUT - Joshua Bon, QUT Starts at 1:30pm AEST

Brain-hungry? We're creating a great program for International Biometrics Conference IBC22 in Riga, Latvia, pearl of the Baltics & UNESCO World Heritage wonder. members.biometricsociety.org/ibc2022/home Share your work - contributed session applications close 30 Sept: bit.ly/2UU0Aph

Here's hoping our Digital Mud Army is taking easy this weekend! For the past 2 days, they worked hard exploring new ways to analyse & visualise #data during a flood crisis like we've seen in QLD & NSW. Well done to Kate Saunders & Kate Helmstedt @katehelmstedt.bsky.social for organising this great hackathon!

On behalf of everyone here at our Centre, we'd like to wish you a Happy International Day of Mathematics... aka, Pi Day! #idm314 #PiDay #mathematics #maths #statistics #piday2022 #DataScience QUT

We love a good stroll and @bora_adriana_e said it best: “if you ask someone to picture 6 future doctors of Math, I bet they wouldn’t picture this”… I guess this makes us ladies of l̴e̴i̴s̴u̴r̴e̴ measure?

Sammie the salamander from QUT’s Faculty of #Science joining our PhD’s for a rainy, Monday afternoon study group Katie Buchhorn @bora_adriana_e Raiha Browning Conor Hassan

PhD student Conor Hassan is developing methods to improve access to confidential spatial data via two directions: (1) generating synthetic tabular and spatial data and (2) federated learning.

🚨 Excited for next weeks QUT Centre for Data Science EC Bayes seminar with @maary_llewellyn talking on accelerating inference for state-space models! Sponsored by Statistical Society Aust QLD branch Register ⬇️

Piggybacking off this piggyback of a piggyback. Assembling the crew for #baysm2022 #isba2022 in Montreal!

Raiha Browning talking about estimating the histogram kernel of discrete time Hawkes processes via RJMCMC j-ISBA. Preprint coming soon! QUT Centre for Data Science

what an amazing group of Aussies (and Kiwis) at #ISBA2022 ! Great to meet you all and thanks to Matt Moores to organise Statistical Society of Australia BayesSSA

• #GSoC Student: Conor Hassan (Conor Hassan) • Mentors: Aruna Fonnesbeck & Bill Engels • Project: Efficient Inference for Latent Gaussian Models Read more here: pymc.io/blog/blog_gsoc…

Our new preprint with Chris Drovandi, Adrianne Jenner @ProfMJSimpson and Ian Turner looking at efficient inference and identifiability analysis for models that incorporate biological variability through random parameters now up on #arxiv arxiv.org/abs/2207.10267 QUT Centre for Data Science

New month, new job! Excited to be starting my postdoc today modelling neglected tropical diseases with Dr Simon Spencer at Warwick Statistics NTD_Modelling

Back in 2018, the ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS) created a podcast called The Random Sample. The ACEMS Twitter account @ACEMathStats is now being switched to the podcast - as it gets ready to relaunch! We hope you'll stay with us.

Really stoked to have this paper out in the wild! We present a straight forward approach to simulation-based inference that can utilise approximate models. Thanks to the great team David James Warne David Nott Chris Drovandi that worked on this! arxiv.org/abs/2211.05357

Interested in sequential Monte Carlo? Looking for a workshop in Australia? Look no further: 👉 SMC Down Under 👉 July 10-13 Sponsored by Australian Mathematical Sciences Institute (AMSI) Australian Mathematical Society QUT Centre for Data Science research.qut.edu.au/qutcds/events/…

Excited to get this work preprinted! We provide a method for federated learning inference of hierarchical models. The approach doesn't require communicating gradient information of private parameters, and we also offer a communication-efficient variant! arxiv.org/abs/2302.03314