ORCID Profile
0000-0002-6842-2352
Current Organisation
University of Melbourne
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
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.
Applied Statistics | Statistics | Knowledge Representation and Machine Learning
Technological Ethics | Expanding Knowledge in the Mathematical Sciences |
Publisher: IEEE
Date: 03-2018
Publisher: Cold Spring Harbor Laboratory
Date: 16-07-2022
DOI: 10.1101/2022.07.14.500036
Abstract: Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to erse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird’s eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard Maximum Mean Discrepancy (MMD) measure. The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect.
Publisher: Springer Science and Business Media LLC
Date: 02-02-2021
DOI: 10.1038/S41598-021-81011-2
Abstract: Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-s le subset of these images, and found that they showed 89% agreement (95% CI: 87–92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p $$=$$ = 0.009–0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p $$=$$ = 0.001–0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Hindawi Limited
Date: 30-12-2018
DOI: 10.1155/2018/4091497
Abstract: Background . Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods . This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results . Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion . The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.
Publisher: Informa UK Limited
Date: 09-2013
Publisher: Wiley
Date: 05-09-2017
DOI: 10.1111/JOSH.12546
Publisher: Wiley
Date: 27-10-2022
DOI: 10.1002/SAM.11560
Abstract: Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multiobjective optimization and seek the fairness‐accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so‐called linear scalarization scheme, which has severe limitations in recovering Pareto optimal solutions. We instead apply the Chebyshev scalarization scheme which is provably superior theoretically and no more computationally burdensome at recovering Pareto optimal solutions compared to the linear scheme.
Publisher: Springer Science and Business Media LLC
Date: 07-2017
Publisher: Wiley
Date: 11-06-2012
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Science and Business Media LLC
Date: 03-11-2022
DOI: 10.1186/S12859-022-05003-3
Abstract: Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to erse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird’s eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard maximum mean discrepancy measure. The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Institute of Mathematical Statistics
Date: 2018
DOI: 10.1214/17-EJS1382
Publisher: IEEE
Date: 09-2018
Publisher: Informa UK Limited
Date: 02-04-2016
Publisher: Oxford University Press (OUP)
Date: 17-10-2018
Location: United States of America
Location: United States of America
Start Date: 03-2020
End Date: 05-2024
Amount: $349,586.00
Funder: Australian Research Council
View Funded Activity