ORCID Profile
0000-0002-4840-8545
Current Organisations
Universiti Putra Malaysia Fakulti Sains
,
University of Sydney
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Numerical Solution of Differential and Integral Equations | Numerical Analysis | Numerical and Computational Mathematics | Stochastic Analysis and Modelling | Structural Geology | Geology | Geophysics not elsewhere classified
ANZSRC Socio-Economic Objective (SEO)
Mineral Exploration not elsewhere classified | Primary Mining and Extraction of Mineral Resources not elsewhere classified | Expanding Knowledge in the Mathematical Sciences | Energy Exploration not elsewhere classified |
Related Links
Publications
Proteome analysis of Campylobacter jejuni poultry strain 2704 survival during 45 min exposure to peracetic acid
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.IJFOODMICRO.2022.110000
Abstract: Peracetic acid (PAA) applied to whole poultry carcasses can reduce the number of C ylobacter, a leading cause of human gastroenteritis. However, previous modelling experiments indicated that C ylobacter survived in greater numbers when pre-treated with a thermal stress equivalent to poultry processing scalding prior to chilling with PAA than when subject to chilling with PAA only. To better understand how C ylobacter responds to PAA, proteomes of C. jejuni poultry strain 2704 were measured after exposure to PAA (60 ppm, pH 4.0) for 45 min under laboratory ambient conditions (approximately 23 °C) to establish a foundational map of survival mechanism before combining with other stresses. Analysis of 580 quantified proteins did not indicate a triggered "peroxide shock" response, nor were common heat shock responses detected. Thioredoxin, iron homeostatic, peroxiredoxins and cytochrome c peroxidases became more abundant suggesting that PAA disturbed cytoplasmic redox homeostasis resulting in antioxidant activation and increased prioritisation of iron homeostasis. The PAA treatment led to responses that included an increased priority for oxidative phosphorylation and a simultaneous decrease in central metabolism associated protein abundances. Lon protease was induced suggesting it has a role in maintaining homeostasis during non-thermal stress. Proteins in flagella and chemotaxis became more abundant though whether PAA has a chemorepellent effect requires further investigation. Overall, the proteome data suggests there was a rapid cellular response to applied PAA stress in the first 15 min with the adaptation to the stress completing between 30 and 45 min. The findings will help guide PAA implementation in commercial poultry processing in terms of processing location and length of application.
Critical current density and pinning potential in YBa2Cu3O7−δthick films ablated from a BaZrO3-doped nanocrystalline target
Publisher: IOP Publishing
Date: 17-03-2009
USING PARALLEL MARKOV CHAIN MONTE CARLO TO QUANTIFY UNCERTAINTIES IN GEOTHERMAL RESERVOIR CALIBRATION
Publisher: Begell House
Date: 2019
Optimization Methods for Inverse Problems
Publisher: Springer International Publishing
Date: 2019
Uncertainty Quantification for Stream Depletion Tests
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2013
Characterization of Parameters for a Spatially Heterogenous Aquifer from Pumping Test Data
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2014
Pragmatic Approach to Calibrating Distributed Parameter Groundwater Models from Pumping Test Data Using Adaptive Delayed Acceptance MCMC
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2016
Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2017
DOI: 10.1137/16M1082123
Improved Critical Current Densities in YBa2Cu3O7−δ Multilayer Films Interspaced with Palladium Nano-dots
Publisher: Springer Science and Business Media LLC
Date: 02-10-2010
Integrated nanotechnology of pinning centers in YBa2Cu3Oxfilms
Publisher: IOP Publishing
Date: 29-10-2010
Certified dimension reduction in nonlinear Bayesian inverse problems
Publisher: American Mathematical Society (AMS)
Date: 27-04-2022
DOI: 10.1090/MCOM/3737
Abstract: We propose a dimension reduction technique for Bayesian inverse problems with nonlinear forward operators, non-Gaussian priors, and non-Gaussian observation noise. The likelihood function is approximated by a ridge function, i.e., a map which depends nontrivially only on a few linear combinations of the parameters. We build this ridge approximation by minimizing an upper bound on the Kullback–Leibler ergence between the posterior distribution and its approximation. This bound, obtained via logarithmic Sobolev inequalities, allows one to certify the error of the posterior approximation. Computing the bound requires computing the second moment matrix of the gradient of the log-likelihood function. In practice, a s le-based approximation of the upper bound is then required. We provide an analysis that enables control of the posterior approximation error due to this s ling. Numerical and theoretical comparisons with existing methods illustrate the benefits of the proposed methodology.
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2021
DOI: 10.1137/20M1318365
Dimension-independent likelihood-informed MCMC
Publisher: Elsevier BV
Date: 2016
Network Structure Change Point Detection by Posterior Predictive Discrepancy
Publisher: Springer International Publishing
Date: 2020
Self-assembled artificial pinning centres in thick YBCO superconducting films
Publisher: IOP Publishing
Date: 06-2010
Extraction of high-resolution structural orientations from digital data: A Bayesian approach
Publisher: Elsevier BV
Date: 05-2019
Multifidelity importance sampling
Publisher: Elsevier BV
Date: 03-2016
Effect of Thermo-Mechanical Treatment on the Current Transport Properties of Bi(Pb)-Sr-Ca-Cu-O Superconductor Tapes with Nanopowder CoFe2O4 Addition
Publisher: AIP
Date: 2011
DOI: 10.1063/1.2739831
Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells
Publisher: IEEE
Date: 12-2018
Increased critical current density and pinning in thick Ag/YBa2Cu3O7− multilayers
Publisher: Elsevier BV
Date: 11-2010
Improvement of Pinning Force and Critical Current Density in Thick YBa 2 Cu 3 O 7−δ Films Grown on SrTiO 3 Substrates Decorated with LaNiO 3 Nanodots
Publisher: Springer Science and Business Media LLC
Date: 03-03-2009
Likelihood-informed dimension reduction for nonlinear inverse problems
Publisher: IOP Publishing
Date: 29-10-2014
Data-free likelihood-informed dimension reduction of Bayesian inverse problems
Publisher: IOP Publishing
Date: 17-03-2021
Abstract: Identifying a low-dimensional informed parameter subspace offers a viable path to alleviating the dimensionality challenge in the s led-based solution to large-scale Bayesian inverse problems. This paper introduces a novel gradient-based dimension reduction method in which the informed subspace does not depend on the data. This permits online–offline computational strategy where the expensive low-dimensional structure of the problem is detected in an offline phase, meaning before observing the data. This strategy is particularly relevant for multiple inversion problems as the same informed subspace can be reused. The proposed approach allows to control the approximation error (in expectation over the data) of the posterior distribution. We also present s ling strategies which exploit the informed subspace to draw efficiently s les from the exact posterior distribution. The method is successfully illustrated on two numerical ex les: a PDE-based inverse problem with a Gaussian process prior and a tomography problem with Poisson data and a Besov- B 11 2 prior.
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
Publisher: Elsevier BV
Date: 06-2016
Improved critical current densities in thick YBa2Cu3O7−δ multilayer films interspaced with non-superconducting YBa2Cu3Ox nanodots
Publisher: Elsevier BV
Date: 11-2010
Rapid near-atomic resolution single-particle 3D reconstruction with SIMPLE
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.JSB.2018.08.005
Abstract: Cryogenic electron microscopy (cryo-EM) and single-particle analysis enables determination of near-atomic resolution structures of biological molecules. However, large computational requirements limit throughput and rapid testing of new image processing tools. We developed PRIME, an algorithm part of the SIMPLE software suite, for determination of the relative 3D orientations of single-particle projection images. PRIME has primarily found use for generation of an initial ab initio 3D reconstruction. Here we show that the strategy behind PRIME, iterative estimation of per-particle orientation distributions with stochastic hill climbing, provides a competitive approach to near-atomic resolution single-particle 3D reconstruction. A number of mathematical techniques for accelerating the convergence rate are introduced, leading to a speedup of nearly two orders of magnitude. We benchmarked our developments on numerous publicly available data sets and conclude that near-atomic resolution ab initio 3D reconstructions can be obtained with SIMPLE in a matter of hours, using standard over-the-counter CPU workstations.
Nano Techniques for Enhancing Critical Current in Superconducting YBCO Films
Publisher: Springer Science and Business Media LLC
Date: 25-09-2010
Synergetic Pinning Centers in YBa$_{2}{\hbox{Cu}}_{3}{\hbox{O}}_{\rm x}$ Films Through a Combination of Ag Nano-Dot Substrate Decoration, Ag/YBCO Quasi-Multilayers, and the Use of BaZrO$_{3}$-Doped Ta
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
Optimal Low-rank Approximations of Bayesian Linear Inverse Problems
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2015
DOI: 10.1137/140977308
Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm
Publisher: American Geophysical Union (AGU)
Date: 10-2011
DOI: 10.1029/2010WR010352
Scalable Optimization-Based Sampling on Function Space
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2020
DOI: 10.1137/19M1245220
Randomized reduced forward models for efficient Metropolis–Hastings MCMC, with application to subsurface fluid flow and capacitance tomography
Publisher: Springer Science and Business Media LLC
Date: 21-09-2020
On dimension reduction in Gaussian filters
Publisher: IOP Publishing
Date: 07-03-2016
Artificial pinning in thick YBCO films: Pinning potential and c-axis correlation
Publisher: Elsevier BV
Date: 10-2010
Combination of Ag Substrate Decoration with Introduction of BaZrO3 Nano-Inclusions for Enhancing Critical Current Density of YBa2Cu3O7 Films
Publisher: Springer Science and Business Media LLC
Date: 06-10-2010
Pinning Centers Induced in YBCO Films by Nano-Dots in Substrate Decoration and Quasi-Superlattice Approaches
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
Bayesian Inverse Problems with $l_1$ Priors: A Randomize-Then-Optimize Approach
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2017
DOI: 10.1137/16M1080938
A posteriori stochastic correction of reduced models in delayed‐acceptance MCMC, with application to multiphase subsurface inverse problems
Publisher: Wiley
Date: 12-03-2019
DOI: 10.1002/NME.6028
A non-linear reverse-engineering method for inferring genetic regulatory networks
Publisher: PeerJ
Date: 29-04-2020
DOI: 10.7717/PEERJ.9065
Abstract: Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.
A Metropolis-Hastings-Within-Gibbs Sampler for Nonlinear Hierarchical-Bayesian Inverse Problems
Publisher: Springer International Publishing
Date: 2019
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction
Publisher: No publisher found
Date: 2023
A unified performance analysis of likelihood-informed subspace methods
Publisher: Bernoulli Society for Mathematical Statistics and Probability
Date: 11-2022
DOI: 10.3150/21-BEJ1437
Identification of community structure-based brain states and transitions using functional MRI
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.NEUROIMAGE.2021.118635
Abstract: Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct networks. Characterizing the way in which brain regions reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this paper, we propose a Bayesian method for characterizing community structure-based latent brain states and showcase a novel strategy based on posterior predictive discrepancy using the latent block model to detect transitions between community structures in blood oxygen level-dependent (BOLD) time series. The set of estimated parameters in the model includes a latent label vector that assigns network nodes to communities, and also block model parameters that reflect the weighted connectivity within and between communities. Besides extensive in-silico model evaluation, we also provide empirical validation (and replication) using the Human Connectome Project (HCP) dataset of 100 healthy adults. Our results obtained through an analysis of task-fMRI data during working memory performance show appropriate lags between external task demands and change-points between brain states, with distinctive community patterns distinguishing fixation, low-demand and high-demand task conditions.
Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports
Publisher: Springer Science and Business Media LLC
Date: 21-09-2021
DOI: 10.1007/S10208-021-09537-5
Abstract: Characterising intractable high-dimensional random variables is one of the fundamental challenges in stochastic computation. The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables. This paper generalises the functional tensor-train approximation of the inverse Rosenblatt transport recently developed by Dolgov et al. (Stat Comput 30:603–625, 2020) to a wide class of high-dimensional non-negative functions, such as unnormalised probability density functions. First, we extend the inverse Rosenblatt transform to enable the transport to general reference measures other than the uniform measure. We develop an efficient procedure to compute this transport from a squared tensor-train decomposition which preserves the monotonicity. More crucially, we integrate the proposed order-preserving functional tensor-train transport into a nested variable transformation framework inspired by the layered structure of deep neural networks. The resulting deep inverse Rosenblatt transport significantly expands the capability of tensor approximations and transport maps to random variables with complicated nonlinear interactions and concentrated density functions. We demonstrate the efficiency of the proposed approach on a range of applications in statistical learning and uncertainty quantification, including parameter estimation for dynamical systems and inverse problems constrained by partial differential equations.
Data‐driven model reduction for the Bayesian solution of inverse problems
Publisher: Wiley
Date: 15-08-2014
DOI: 10.1002/NME.4748
Related Organisations
Massachusetts Institute Of Technology
Location: United States of America
Related Funding Activities
Enabling Three Dimensional Stochastic Geological Modelling
Start Date: 2018
End Date: 2020
Funder: Australian Research Council
View Funded ActivityInterface-aware Numerical Methods For Stochastic Inverse Problems
Start Date: 2021
End Date: 2024
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP210103092
Start Date: 10-2021
End Date: 10-2024
Amount: $475,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP170100985
Start Date: 11-2018
End Date: 06-2024
Amount: $711,000.00
Funder: Australian Research Council
View Funded Activity