Research paper accepted by IEEE Transactions on Reliability

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In this paper, we develop a data-efficient learning-based approach for accurately estimating the time-varying kinematic reliability of mechanisms. To improve data efficiency, we develop an adaptive sampling scheme that is guided by the prediction uncertainty of the resultant deep learning model. Overall, the proposed methodology follows a four-step procedure. First, a spectral-normalized neural Gaussian process (SNGP) is employed to quantify the prediction uncertainty of the deep learning model used to evaluate the time-varying kinematic reliability of mechanisms. Unlike other uncertainty quantification methods, SNGP has two unique characteristics: SNGP normalizes the weights of hidden layers of neural network to preserve the relative distances among samples during data transformation; SNGP replaces the conventional dense output layer of neural network with a random Fourier features-based Laplace approximation of Gaussian process for distance-aware uncertainty quantification. Next, based upon the uncertainty estimated by SNGP, we devise an active learning function to adaptively identify and select the most valuable next data sample to add to the training set. Thirdly, based on a predefined data acquisition metric, we iteratively add data points that maximize the active learning function to the training set and continue updating the deep learning model until a termination condition is met. Finally, we benchmark the developed approach for time-varying reliability analysis of mechanisms against Monte Carlo simulation, PHI2, and the Monte Carlo dropout-based uncertainty quantification method in terms of estimation accuracy and computational efficiency using three numerical examples. Computational results suggest that the developed method substantially reduces the number of performance function evaluations while achieving reliability estimates that are more accurate than those from MC dropout and comparable to those from MCS and PHI2.

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Research paper accepted by Decision Support Systems

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Credit card fraud detection remains a significant challenge due to extreme class imbalance, evolving fraud patterns, and asymmetric misclassification costs. While existing machine learning methods often prioritize fraud detection performance, they frequently overlook model risk management, which is crucial for reliable decision making in high-stakes environments. To address this issue, we introduce DISCO — a framework that combines discriminative and robust representation learning with a conformal prediction-based mechanism for provable risk control. In essence, DISCO is established upon the principle of decoupling representation learning from risk-controlled decision-making. To this end, we first leverage deep metric learning (DML) to construct an embedding space that is inherently robust to class imbalance and resilient to concept drift. Subsequently, we employ a conformal risk control (CRC) mechanism to provide a formal statistical guarantee on the false negative rate (FNR) of the resulting model according to a user-specified FNR target. Computational experiments on a real-world dataset demonstrate that DISCO outperforms state-of-the-art methods in both classification performance and operational efficiency. To the best of our knowledge, this is the first work to integrate a DML-based representation learning with a formal risk control mechanism for reliable credit card fraud detection. The proposed framework therefore offers a trustworthy and practical solution that addresses the dual imperatives of predictive performance and reliability.

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Research paper accepted by Neural Networks!

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Deep learning has exhibited a promising performance in a series of task-specific bearing fault diagnosis problems. However, the current literature lacks a versatile model that could serve as a general-purpose feature extractor for a wide range of downstream fault diagnosis tasks. To fill this gap, this paper develops a theoretically well-grounded approach to build an \bl{enhanced} backbone model for various key downstream tasks. The proposed methodology follows a three-step procedure. In the first step, using approximation theory, we prove that incorporating neural heterogeneity by combining linear and quadratic neurons leads to a more efficient approximation of any univariate real coefficient polynomial. Compared to conventional neural networks, HNNs exhibit enhanced representation capability while using fewer model parameters. Secondly, building upon this theoretical foundation, we design an \bl{enhanced} feature extractor termed as heterogeneous neural blind deconvolution (HBD). At a high level, HBD is comprised of two time domain blind deconvolution branches in parallel: one using regular convolutional networks and the other using quadratic convolutional network. The inclusion of diverse neurons in HBD facilitates to learn discriminative features for reliable fault diagnosis that neither neuron type could achieve independently. Following the dual time domain blind deconvolution branches, a frequency-domain BD module complements the feature extraction capability of the time domain blind deconvolution by performing signal filtering in the frequency domain. Finally, to illustrate the general-purpose nature of HBD, we explore the application of HBD across various downstream fault diagnosis tasks, including anti-noise fault diagnosis, cross-domain fault diagnosis, and lightweight model for fault diagnosis on edge devices. Extensive experiments and comparisons with state-of-the-art baselines clearly show the advantage of HBD in enhancing the accuracy and interpretablity for bearing fault diagnosis.

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Research paper accepted by Nature Biomedical Engineering!!!

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Ensuring trustworthiness is fundamental in cancer diagnostics, where a misdiagnosis can have dire consequences. Current pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. Here, we introduce TRUECAM, a framework designed to ensure both data and model trustworthiness for non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs, 2) an ambiguity-guided tile elimination to filter out highly ambiguous regions, addressing data trustworthiness, and 3) conformal prediction to ensure controlled error rates. We systematically evaluated TRUECAM across multiple cancer datasets using both task-specific and foundation models. Computational experiments suggest that models wrapped with TRUECAM consistently outperformed their unwrapped counterparts in classification accuracy, robustness, interpretability, data efficiency, and fairness. These findings establish TRUECAM as a versatile framework for the responsible deployment of pathology AI in real-world settings.

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Congratulations on Long XUE to pass his PhD oral defense!!!

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Principled quantification of predictive uncertainty in deep neural networks is crucial for ensuring reliable performance and trustworthy deployment in open-world environments. This thesis presents three complementary methodologies that advance uncertainty quantification and facilitate responsible utilization of deep learning in open-world settings.

First, we propose a continuous optimization framework for constructing neural network (NN)–based prediction intervals (PIs). The proposed method formulates PI construction as a differentiable optimization problem that explicitly prioritizes target coverage while minimizing the width of PIs. By incorporating distance-based differentiable constraints, a shared-bottom architecture, gradient-conflict mitigation, and a coverage–width–aware early stopping mechanism, the approach yields significantly tighter intervals than state-of-the-art PI methods.

Second, we develop an uncertainty-informed risk management framework for open-world environments using spectral-normalized neural Gaussian processes. Our method combines distance-preserving representation learning with a distance-aware output layer to yield Gaussian process-like, distance-sensitive uncertainty estimates. Through a well-defined thresholding mechanism based on Youden’s index, uncertainty estimates are translated into actionable risk levels, enabling reliable uncertainty-aware decision making across normal, shifted, and Out-of-Distribution (OOD) conditions.

Third, we propose a unified framework that integrates conformal prediction with distance-based OOD detection. By filtering OOD inputs and optimizing prediction set size, the method seek to preserve the statistical guarantee of conformal prediction while yielding tighter, more informative prediction sets. Computational experiments demonstrate competitive OOD detection performance and substantial reductions in the average prediction-set size, all achieved efficiently within a single forward pass through the neural network.

Collectively, this thesis advances uncertainty estimation from isolated modeling techniques to an end-to-end framework for reliable deep learning in open-world settings. The proposed methodologies provide practical pathways for the responsible and dependable deployment of deep learning models in real-world applications.

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Research paper accepted by IEEE Transactions on Reliability

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Deep learning shows great potential for bearing fault diagnosis, but its effectiveness is severely limited by the prevalent issue of highly imbalanced data in real-world industrial settings, where fault events are extremely rare. This paper proposes a novel method for imbalanced bearing fault diagnosis that combines class-aware supervised contrastive learning with a quadratic network backbone. This integrated approach, named CCQNet, is designed to counter the effects of highly skewed data distributions by improving feature representation and classification fairness. Comprehensive experiments show that CCQNet substantially outperforms existing methods in handling imbalanced data, particularly at high imbalance ratios like 50:1. This study provides an effective and innovative solution for imbalanced bearing fault diagnosis. Source codes of this paper are available at https://github.com/yuweien1120/CCQNet for public evaluation.

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Research paper accepted by Reliability Engineering and Systems Safety

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Although machine learning (ML) and deep learning (DL) methods are increasingly used for anomaly detection in industrial cyber-physical systems, their adoption is hindered by concerns about model trustworthiness, especially high false alarm rates (FARs). Excessive false alarms overwhelm operators, cause unnecessary shutdowns, and reduce operational efficiency. This study addresses these challenges by proposing a novel framework that integrates ML-based anomaly detectors with conformal prediction (CP), a model-agnostic uncertainty quantification technique. To handle distribution shifts in time-series data, our framework incorporates a temporal quantile adjustment method with a sliding calibration set, ensuring statistical guarantees on predefined FARs. A rejection mechanism is further integrated by excluding significant anomalies from the calibration set, improving detection capability while maintaining FAR guarantees. For real-time anomaly monitoring, two P-value-based indicators generated from CP are developed to track anomalous trends and enhance model interpretability. The framework is evaluated by comparing several baseline ML and DL methods to their conformalized counterparts using a public ICPS dataset. Comparative results based on Precision, Recall, F1, and AUROC validate the framework’s compatibility with various ML models and its effectiveness in improving anomaly detection performance by reducing false alarms and guaranteeing FARs across a range of predefined values.

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