Thought-Provoking Research from our Training Centre

Landscape dynamics and the Phanerozoic diversification of the biosphere

Tristan Salles, Laurent Husson, Manon Lorcery, Beatriz Hadler Boggiani
Nature, November 2023;

DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling

Arpit Kapoor, Sahani Pathiraja, Lucy Marshall, Rohitash Chandra
Environmental Modelling & Software, Volume 169, November 2023;

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Maryam Zeinolabedini Rezaabad, Heather Lacey, Lucy Marshall, Fiona Johnson
Water Research, Volume 244, October 2023;

Effects of pressure on the structure of partially premixed turbulent diffusion flames: Calculations using MMC-LES

Saeed Aldawsari, Sebastian Galindo-Lopez, Matthew J. Cleary, Assaad R. Masri
Combustion and Flame, Volume 256, October 2023;

Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations

Connor Duffin, Paul Branson, Matt Rayson, Mark Girolami, Edward Cripps, Thomas Stemler
arXiv preprint, July 2023;

Non-stationarity in extreme rainfalls across Australia

Lalani Jayaweera, Conrad Wasko, Rory Nathan, Fiona Johnson
Journal of Hydrology
, Volume 624, September 2023;

Extreme events in the multi-proxy South Pacific drought atlas

Philippa Higgins, Jonathan Palmer, Martin Andersen, Christian Turney, Fiona Johnson
Climatic Change, Volume 176, July 2023;

An application of copulas to OPEC’s changing influence on fossil fuel prices

Clara Grazian, A. McInnes
Econometric Reviews, Volume 42, July 2023;

Do Derived Drought Indices Better Characterize Future Drought Change?

Ze Jiang, Fiona Johnson, Ashish Sharma
Earth’s Future
, Volume 11, Issue 7, July 2023;

A review of globally available data sources for modelling the Water-Energy-Food Nexus

Jack Lodge, Andrew Dansie, Fiona Johnson
Earth-Science Reviews
, Volume 243, August 2023;

A New Method for Postprocessing Numerical Weather Predictions Using Quantile Mapping in the Frequency Domain

Ze Jiang, Fiona Johnson
Monthly Weather Review
, April 2023;

Particle Mean Field Variational Bayes

Minh-Ngoc Tran, Paco Tseng, Robert Kohn
arXiv preprint, May 2023;

Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood

Nhat-Minh Nguyen, Minh-Ngoc Tran, Christopher Drovandi, David Nott
arXiv preprint, May 2023;

Verifying model performance using validation of Pareto solutions

N. Harvey, L. Marshall, R.W. Vervoort
Journal of Hydrology
, Volume 621, June 2023;

Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks

Arpit Kapoor, Anshul Negi, Lucy Marshall, Rohitash Chandra
Environmental Modelling & Software, Volume 162, April 2023;

Hundred million years of landscape dynamics from catchment to global scale

Tristan Salles, Laurent Husson, Patrice Rey, Claire Mallard, Sabin Zahirovic, Beatriz Hadler Boggiani, Nicolas Coltice, Maëlis Arnould
SCIENCE, Vol 379, Issue 6635;

Copula modelling with penalized complexity priors: the bivariate case

Diego Battagliese, Clara Grazian, Brunero Liseo & Cristiano Villa
TEST, 2023;

The co-determination of home and workplace relocation durations using survival copula analysis

Maryam Bostanara, Taha Hossein Rashidi, Nazmul Arefin Khan, Joshua Auld, Milad Ghasri, Clara Grazian
Computers, Environment and Urban Systems, Volume 99, January 2023;

Remote sensing to detect harmful algal blooms in inland waterbodies

Shuang Liu, William Glamore, Bojan Tamburic, Abigail Morrow, Fiona Johnson
Science of The Total Environment, Vol. 851, Part 1, December 2022;

Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning

Shikun Li, Xiaobo Xia, Hansong Zhang, Yibing Zhan, Shiming Ge, Tongliang Liu
Proceedings of the 39th International Conference on Machine Learning, 2022

Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions

Mark D. Lindsay, Agnieszka M. Piechocka, Mark W Jessell, Richard Scalzo, Jeremie Giraud, Guillaume Pirot, Edward Cripps
Geoscience Frontiers, Vol. 13, Issue 6, November 2022;

Towards Lightweight Black-Box Attacks against Deep Neural Networks

Chenghao Sun, Yonggang Zhang, Wan Chaoqun, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian
Proceedings of the 39th International Conference on Machine Learning, 2022;

Preserved intercratonic lithosphere reveals Proterozoic assembly of Australia

Yongjun Lu, Michael T.D. Wingate, Robert H. Smithies, Klaus Gessner, Simon P. Johnson, Anthony I.S. Kemp, David E. Kelsey, Peter W. Haines, David McB. Martin, Laure Martin, Mark Lindsay
Geology, Vol. 50, Issue 10, August 2022;

Sedimentary basins reduce stability of Antarctic ice streams through groundwater feedbacks

Lu Li, Alan R. A. Aitken, Mark D. Lindsay, and Bernd Kulessa
Nature Geoscience, 15, 645–650, 2022;

Estimating instance-dependent bayes-label transition matrix using a deep neural network

Shuo Yang, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
Proceedings of the 39th International Conference on Machine Learning, 2022;

Modeling Adversarial Noise for Adversarial Training

Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu
Proceedings of the 39th International Conference on Machine Learning, 2022;

A synthetic likelihood approach for intractable markov random fields

Wanchuang Zhu, Yanan Fan
Computational Statistics, Vol. 37, Issue 3, July 2022;

loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification

Guillaume Pirot, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell
Geosci. Model Dev., 15, 4689–4708, 2022;

The Unexpected Oceanic Peak in Energy Input to the Atmosphere and Its Consequences for Monsoon Rainfall

Nandini Ramesh, William R. Boos
Geophysical Research Letter, Jun 2022;

Understanding Robust Overfitting of Adversarial Training and Beyond

Chaojian Yu, Bo Han, Li Shen, Jun Yu, Chen Gong, Mingming Gong, Tongliang Liu
Proceedings of the 39th International Conference on Machine Learning, 2022;

Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: A perspective

Saman Razavi, David M. Hannah, Amin Elshorbagy, Sujay Kumar, Lucy Marshall, Dimitri P. Solomatine, Amin Dezfuli, Mojtaba Sadegh, James Famiglietti
Hydrological Processes, May 2022;

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 2022, 15, 3641–3662;

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, 2022;

An Introduction to Quantum Computing for Statisticians and Data Scientists

Anna Lopatnikova, Minh-Ngoc Tran, Scott A. Sisson
Apr 2022;

Bayesian optimization with informative parametric models via sequential Monte Carlo

Rafael OliveiraRichard ScalzoRobert KohnSally CrippsKyle HardmanJohn CloseNasrin Taghavi and Charles Lemckert
Data-Centric EngineeringVolume 3, 2022, e5;

Spatial and Temporal Global Patterns of Drought Propagation

Ignacio Fuentes, José Padarian and R. Willem Vervoort
Front. Environ. Sci., 01 March 2022;

A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Shirmard H; Farahbakhsh E; Heidari E; Pour AB; Pradhan B; Müller D; Chandra R
Remote Sensing, 2022, vol. 14, pp. 819 – 819;

Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing

Rutger Willem Vervoort, Ignacio Fuentes, Joost Brombacher, Jelle Degen, Pedro Chambel-Leitão and Flávio Santos
Sustainability, 2022, 14, 2732;


Detecting Poisoning Nodes in Federated Learning by Ranking Gradients

Wanchuang Zhu, Benjamin ZH Zhao, Simon Luo, Ke Deng
Paper accepted in NeurIPS 2021 workshop

Distributed Bayesian optimisation framework for deep neuroevolution

Rohitash Chandra, Animesh Tiwari
Neurocomputing, Vol. 470, January 2022;

Bayesian LSTM with Stochastic Variational Inference for Estimating Model Uncertainty in Process-based Hydrological Models

Dayang Li, Lucy Marshall, Zhongmin Liang, Ashish Sharma, Yan Zhou
Water Resources Research, 09 September 2021;

‘You’ll Never Get Cultural Competence in Science’: An Australian Perspective on Integrating Cultural Competence into Science Teaching Via Cultural Accountability

Rebecca Cross, Rosanne Quinnell, Tina Bell, Paul Rhodes, Zsuzsanna Dancso, Thomas Hubble, Glenda Wardle, Melinda Lewis, Alice Motion, Dominic Murphy, Jaime Gongora
International Journal of Innovation in Science and Mathematics Education. Vol 29, No 3 (2021);

Transboundary river basins: Scenarios of hydropower development and operation under extreme climate conditions

Kongmeng Ly, Graciela Metternicht, Lucy Marshall
Science of The Total Environment. 23 August 2021;

Blockworlds 0.1.0: A demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, Sally Cripps
Geoscientific Model Development. Preprint, currently under review;

SMOTified-GAN for class imbalanced pattern classification problems

Anuraganand Sharma, Prabhat Kumar Singh, Rohitash Chandra
arXiv:2108.03235 [cs.LG]

Daily time series of river water levels derived from a seasonal linear model using multisource satellite products under uncertainty

Hung T. Pham, Lucy Marshall, Fiona Johnson
Journal of Hydrology. 5 August 2021;

Phenotypic plasticity masks range- wide genetic differentiation for vegetative but not reproductive traits in a short- lived plant

Jesus Villellas, Johan Ehrlén, Elizabeth E. Crone, Glenda M. Wardle, et al.
Ecology Letters. 2021;00:1–16;

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Julian Diaz-Rodriguez, R. Dietmar Müller, Rohitash Chandra
Ore Geology Reviews. Volume 137, October 2021, 104300;

Partition-Mallows Model and Its Inference for Rank Aggregation

Wanchuang Zhu, Yingkai Jiang, Jun S. Liu, Ke Deng
Journal of the American Statistical Association. 28 May 2021;

Volume and uncertainty estimates of on-farm reservoirs using surface reflectance and LiDAR data

Ignacio Fuentes, Richard Scalzo, Willem Vervoort
Environmental Modelling and Software. Volume 143, 2021;

Quantifying input error in hydrologic modeling using the Bayesian error analysis with reordering (BEAR) approach

Xia Wu, Lucy Marshall, Ashish Sharma
Journal of Hydrology. Volume 598, July 2021;

Dynamic models using score copula innovations

Landan Zhang, Michael K. Pitt, Robert Kohn
April 2021;

Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

Jeremie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, Mark Lindsay
Geoscientific Model Development. Discuss. [preprint],, in review, 2021.

Affordance Transfer Learning for Human-Object Interaction Detection

Zhi Hou, Baosheng Yu, Yu Qiao, Xiaojiang Peng, Dacheng Tao
Accepted to 2021 Conference on Computer Vision and Pattern Recognition

Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning

Rohitash Chandra, Sally Cripps, Nathaniel Butterworth, R. Dietmar Muller
Environmental Modelling & Software. Volume 139, May 2021;

Generalization Bounds of Multitask Learning From Perspective of Vector-Valued Function Learning

Chao Zhang , Dacheng Tao, Tao Hu, Bingchen Liu
IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No.5, May 2021; DOI: 10.1109/TNNLS.2020.2995428

Revisiting Bayesian Autoencoders with MCMC

Rohitash Chandra, Mahir Jain, Manavendra Maharana, Pavel N. Krivitsky
arXiv:2104.05915 [cs.LG]

Generalization of level-set inversion to an arbitrary number of geological units in a regularized least-squares framework

Jérémie Giraud, Mark Lindsay, Mark Jessell
Geophysics. 13 April 2021;

3D geological structure inversion from Noddy-generated magnetic data using deep learning methods

Jiateng Guo, Yunqiang Li, Mark Walter Jessell, Jeremie Giraud, Chaoling Li, Lixin Wu, Fengdan Li, Shanjun Liu,
Computers & Geosciences. Volume 149, April 2021;

Effect Estimates of COVID-19 Non-Pharmaceutical Interventions are Non-Robust and Highly Model-Dependent

Vincent Chin, John P.A. Ioannidis, Martin A. Tanner, Sally Cripps
Journal of Clinical Epidemiology. March 26, 2021;

Targeted Attention Attack on Deep Learning Models in Road Sign Recognition

Xinghao Yang , Weifeng Liu, Shengli Zhang, Wei Liu, Dacheng Tao
IEEE Internet of Things Journal, Vol. 8, No.6, March 15 2021; DOI: 10.1109/JIOT.2020.3034899

dh2loop 1.0: an open-source python library for automated processing and classification of geological logs

Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, Guillaume Pirot
Geoscientific Model Development. [preprint],, in review, 2021.

Deep learning via LSTM models for COVID-19 infection forecasting in India

Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan
arXiv:2101.11881 [cs.LG]

Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers

Sally Cripps, Hadi Afshar
To appear in the Proceedings of the 2021 International Conference on Artificial Intelligence and Statistics

Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes

Viet-Hung Dao, David Gunawan, Minh-Ngoc Tran, Robert Kohn, Guy E. Hawkins, Scott D. Brown

The Australian bushfire disaster: How to avoid repeating this catastrophe for biodiversity

Danielle Celermajer, Rosemary Lyster, Glenda M. Wardle, Rachel Walmsley, Ed Couzens
WIREs Climate Change. March 03, 2021; e704.

Statistical finite elements for misspecified models

Connor Duffin, Edward Cripps, Thomas Stemler, Mark Girolami
Proceedings of the National Academy of Sciences. January 2021, 118 (2) e2015006118; DOI: 10.1073/pnas.2015006118

Automated geological map deconstruction for 3D model construction

Mark Jessell, Vitaliy Ogarko, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, Guillaume Pirot
Geoscientific Model Development. [preprint],, in review, 2021.

Recursive Context Routing for Object Detection

Zhe Chen, Jing Zhang, Dacheng Tao
International Journal of Computer Vision (2021), 129:142–160;

Climate change and other trends in streamflow observations in Australian forested catchments since 1970

R. Willem Vervoort, Michaela M. Dolk, Floris F. van Ogtrop
Hydrological Processes. 2021; 35:e13999.

A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection

Zhe Chen, Wanli Ouyang, Tongliang Liu, Dacheng Tao
International Journal of Computer Vision (2021), 129:1121–1138;

Genetic differentiation can be predicted from observational data for reproductive but not vegetative traits in a widespread short-lived plant

Jesus Villellas, Johan Ehrlén, Elizabeth Crone, et al.
Authorea. January 04, 2021. DOI: 10.22541/au.160975628.85388662/v1

Mineral systems prospectivity modelling for gold and nickel in the Halls Creek Orogen, Western Australia

Fariba Kohanpour, Sandra Occhipinti, Mark Lindsay, Weronika Gorczyk, Fred Jourdan, Marc Poujol
Ore Geology Reviews. Volume 127, December 2020;

The evolution from plate margin to intraplate mineral systems in the Capricorn Orogen, links to prospectivity

Sandra Occhipinti, Václav Metelka, Mark Lindsay, Alan Aitken, Franco Pirajno, Ian Tyler
Ore Geology Reviews. Volume 127, December 2020;

Cooperative Inversion of Seismic and Gravity Data Using Weighted Structure-Based Constraints

Mahtab Rashidifard, Jeremie Giraud, Vitaliy Ogarko, Mark Jessell, Mark Lindsay
Proceedings of NSG2020 3rd Conference on Geophysics for Mineral Exploration and Mining. December 2020;

Convex Optimization for Blind Source Separation on Statistical Manifolds

Simon Luo, Lamiae Azizi, Mahito Sugiyama
NeurIPS 2020 workshop on Differential Geometry meets Deep Learning. December 2020.

A Deep Architecture for Log-Linear Models

Simon Luo, Sally Cripps, Mahito Sugiyama
NeurIPS 2020 workshop on Differential Geometry meets Deep Learning. December 2020.

Building trust in SWAT model scenarios through a multi-institutional approach in Uruguay

Flora Mer, Walter Baethgen, R. Willem Vervoort
Socio-Environmental Systems Modelling. Volume 2, 2020;

Quantifying input uncertainty in the calibration of water quality models: reshuffling errors via the secant method

Xia Wu, Lucy Marshall, Ashish Sharma
Hydrology and Earth System Sciences. November 12, 2020;

Improving the Combination of Satellite Soil Moisture Data Sets by Considering Error Cross Correlation: A Comparison Between Triple Collocation (TC) and Extended Double Instrumental Variable (EIVD) Alternatives

Seokhyeon Kim, Hung T. Pham, Yi Y. Liu, Lucy Marshall, Ashish Sharma
IEEE Transactions on Geoscience and Remote Sensing. November 02, 2020; doi: 10.1109/TGRS.2020.3032418.

Short-Term and Long-Term Context Aggregation Network for Video Inpainting

Ang Li, Shanshan Zhao, Xingjun Ma, Mingming Gong, Jianzhong Qi, Rui Zhang, Dacheng Tao, Ramamohanarao Kotagiri
European Conference on Computer Vision. Computer Vision – ECCV 2020 pp 728-743;

Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging

Jérémie Giraud, Mark Lindsay, Mark Jessell, Vitaliy Ogarko
Solid Earth, 11, 419–436, 2020;