Selected work

Representative Papers

Implementation Guidelines for Data Quality Metrics

Oct 07, 2026

This study addresses the lack of implementation guidelines for ISO data quality standards, which hinders their practical adoption. We systematically categorize ISO metrics and formulate executable specifications, developing dqmeasure, an open-source Python library for automated assessment. The core innovation lies in a novel method that automatically learns parameters from reference data, eliminating reliance on manual rules. Experimental results demonstrate that the proposed metrics decrease monotonically as data errors increase and exhibit strong correlation with downstream machine learning performance. Furthermore, the approach supports linearly scalable monitoring. This work provides an effective tool for the automated evaluation of data quality.

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Learning spatially varying regularisation parameters of low regularity for image reconstruction

Aug 25, 2026

本文探讨了通过学习空间变化的正则化参数来改善图像重建中边缘和细节保留的问题，特别是结合深度神经网络的方法。

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Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement

Nov 12, 2025

Ultra-low-field MRI (ULF-MRI) suffers from inherently low signal-to-noise ratio, poor spatial resolution, and contrast distortion. Existing image-to-image translation methods are hindered by the scarcity of paired high-field/low-field 3D volumetric data. To address this, we propose a task-adaptive data augmentation framework that, using only 50 paired 3D volumes, integrates strong geometric and intensity augmentations alongside auxiliary supervision derived from high-field images—specifically structural consistency reconstruction—to enhance model generalizability. Our method significantly improves ULF-MRI image quality, achieving third place in brain mask SSIM and fourth place in overall test score on the ULF-EnC Challenge public leaderboard. The source code is publicly released, establishing a reproducible paradigm for few-shot medical image enhancement.

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Estimating systematic errors in Bayesian inversion using transport maps

Sep 19, 2025

In Bayesian inverse problems, approximation errors in the measurement process model induce systematic bias, distorting the posterior distribution. This paper proposes a unified framework integrating transport maps with Bayesian model error modeling to jointly and adaptively estimate both the corrected posterior and the model error. The key innovation lies in constructing a differentiable transport map from a reference distribution to the bias-corrected posterior, while coupling it with a stochastic process representation of the model error to enable dynamic bias correction. The method is theoretically rigorous and computationally efficient. It is validated on two canonical indirect measurement problems, demonstrating significantly improved correction accuracy and sampling efficiency over conventional approaches. By explicitly accounting for structural model inadequacy, the framework effectively mitigates posterior distortion arising from model misspecification.

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Plans for acceptance sampling by attributes when observations are destructive

Aug 07, 2025

ISO 2859-2 exhibits inaccurate estimation of residual lot quality risk—particularly for small lots—and inflated consumer’s risk in destructive testing due to non-recoverable samples. To address this, this paper proposes a novel Bayesian attribute acceptance sampling method. It innovatively treats the residual lot size as a fixed parameter and integrates a hypergeometric likelihood with a reference prior to construct a decision-theoretic framework that rigorously controls consumer’s risk. The resulting standardized sampling plans are compact and computationally efficient, substantially reducing actual consumer’s risk in small-lot scenarios. This approach extends the applicability of acceptance sampling beyond the limitations of conventional standards in destructive inspection settings. Its theoretical foundation—grounded in objective Bayesian inference—and empirical efficacy support its potential adoption into international standardization frameworks.

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Recent publications

Latest Papers

Implementation Guidelines for Data Quality Metrics

Oct 07, 2026

This study addresses the lack of implementation guidelines for ISO data quality standards, which hinders their practical adoption. We systematically categorize ISO metrics and formulate executable specifications, developing dqmeasure, an open-source Python library for automated assessment. The core innovation lies in a novel method that automatically learns parameters from reference data, eliminating reliance on manual rules. Experimental results demonstrate that the proposed metrics decrease monotonically as data errors increase and exhibit strong correlation with downstream machine learning performance. Furthermore, the approach supports linearly scalable monitoring. This work provides an effective tool for the automated evaluation of data quality.

0 citationsRead paper

Learning spatially varying regularisation parameters of low regularity for image reconstruction

Aug 25, 2026

本文探讨了通过学习空间变化的正则化参数来改善图像重建中边缘和细节保留的问题，特别是结合深度神经网络的方法。

0 citationsRead paper

Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement

Nov 12, 2025

Ultra-low-field MRI (ULF-MRI) suffers from inherently low signal-to-noise ratio, poor spatial resolution, and contrast distortion. Existing image-to-image translation methods are hindered by the scarcity of paired high-field/low-field 3D volumetric data. To address this, we propose a task-adaptive data augmentation framework that, using only 50 paired 3D volumes, integrates strong geometric and intensity augmentations alongside auxiliary supervision derived from high-field images—specifically structural consistency reconstruction—to enhance model generalizability. Our method significantly improves ULF-MRI image quality, achieving third place in brain mask SSIM and fourth place in overall test score on the ULF-EnC Challenge public leaderboard. The source code is publicly released, establishing a reproducible paradigm for few-shot medical image enhancement.

0 citationsRead paper

Estimating systematic errors in Bayesian inversion using transport maps

Sep 19, 2025

In Bayesian inverse problems, approximation errors in the measurement process model induce systematic bias, distorting the posterior distribution. This paper proposes a unified framework integrating transport maps with Bayesian model error modeling to jointly and adaptively estimate both the corrected posterior and the model error. The key innovation lies in constructing a differentiable transport map from a reference distribution to the bias-corrected posterior, while coupling it with a stochastic process representation of the model error to enable dynamic bias correction. The method is theoretically rigorous and computationally efficient. It is validated on two canonical indirect measurement problems, demonstrating significantly improved correction accuracy and sampling efficiency over conventional approaches. By explicitly accounting for structural model inadequacy, the framework effectively mitigates posterior distortion arising from model misspecification.

0 citationsRead paper

Plans for acceptance sampling by attributes when observations are destructive

Aug 07, 2025

ISO 2859-2 exhibits inaccurate estimation of residual lot quality risk—particularly for small lots—and inflated consumer’s risk in destructive testing due to non-recoverable samples. To address this, this paper proposes a novel Bayesian attribute acceptance sampling method. It innovatively treats the residual lot size as a fixed parameter and integrates a hypergeometric likelihood with a reference prior to construct a decision-theoretic framework that rigorously controls consumer’s risk. The resulting standardized sampling plans are compact and computationally efficient, substantially reducing actual consumer’s risk in small-lot scenarios. This approach extends the applicability of acceptance sampling beyond the limitations of conventional standards in destructive inspection settings. Its theoretical foundation—grounded in objective Bayesian inference—and empirical efficacy support its potential adoption into international standardization frameworks.

0 citationsRead paper