Selected work

Representative Papers

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Apr 11, 2025

In multidisciplinary design optimization—particularly aircraft design—the presence of high-dimensional mixed variables (continuous, integer, and categorical) causes combinatorial explosion in the hyperparameter space of Bayesian optimization surrogate models. To address this, we propose a Partial Least Squares (PLS)-based adaptive dimensionality reduction framework. It dynamically learns variable coupling structures to significantly compress the surrogate hyperparameter space, while integrating mixed-variable encoding with an adaptive hyperparameter selection mechanism to balance modeling accuracy and tuning efficiency. Evaluated on analytical benchmarks and two real-world aircraft design cases, our method achieves >30% faster convergence and improves optimal solution quality by 12–18% compared to genetic algorithms, while reducing hyperparameter count by ~60%. Our core contribution is the first application of PLS for hyperparameter pruning in Bayesian optimization, enabling efficient and robust optimization under high-dimensional mixed-variable settings.

8 citations3 influentialRead paper

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper

Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

Sep 08, 2026

本文针对心肌追踪中的漂移问题，通过改进的深度学习框架TAS-Net及教师-学生微调策略，实现了更准确的心肌应变估计。

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Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

Jul 29, 2026

This study addresses the challenge that traditional anomaly detection methods struggle to identify samples near the boundary between normal and anomalous states, thereby failing to enable early fault warnings. To overcome this limitation, the paper introduces the novel concept of “near-anomalies” and proposes CANARI, an unsupervised method grounded in Christoffel function theory to model such borderline cases. By moving beyond conventional dual-threshold mechanisms, CANARI proactively identifies unlabeled samples likely to evolve into failures. Experimental results on both synthetic and real-world industrial printed circuit board in-circuit test data demonstrate that CANARI significantly outperforms existing baselines, offering a robust foundation for predictive maintenance and quality control.

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Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement

Jul 22, 2026

This study addresses the challenge of limited labeled data in electroencephalography (EEG)-based motor imagery (MI) classification by proposing a novel class-conditional variational autoencoder (CVAE) framework. The approach explicitly preserves the Riemannian geometry of EEG signals during generation—not by mimicking raw waveforms, but through a covariance matrix constraint and a cycle-consistent decoder refinement strategy. This enables the synthesis of label-consistent and geometrically plausible EEG samples for data augmentation. Experimental results demonstrate that, under both cross-subject and within-subject settings, the proposed method yields modest yet consistent performance gains for covariance-structure-dependent classifiers such as Minimum Distance to Mean (MDM), thereby validating the efficacy and potential of structure-aware generative modeling in EEG data augmentation.

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Recent publications

Latest Papers

Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

Sep 08, 2026

本文针对心肌追踪中的漂移问题，通过改进的深度学习框架TAS-Net及教师-学生微调策略，实现了更准确的心肌应变估计。

0 citationsRead paper

Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach

Jul 29, 2026

This study addresses the challenge that traditional anomaly detection methods struggle to identify samples near the boundary between normal and anomalous states, thereby failing to enable early fault warnings. To overcome this limitation, the paper introduces the novel concept of “near-anomalies” and proposes CANARI, an unsupervised method grounded in Christoffel function theory to model such borderline cases. By moving beyond conventional dual-threshold mechanisms, CANARI proactively identifies unlabeled samples likely to evolve into failures. Experimental results on both synthetic and real-world industrial printed circuit board in-circuit test data demonstrate that CANARI significantly outperforms existing baselines, offering a robust foundation for predictive maintenance and quality control.

0 citationsRead paper

Generative Augmentation for EEG Motor Imagery Classification: A Class-Conditional VAE with Cycle-Consistent Decoder Refinement

Jul 22, 2026

This study addresses the challenge of limited labeled data in electroencephalography (EEG)-based motor imagery (MI) classification by proposing a novel class-conditional variational autoencoder (CVAE) framework. The approach explicitly preserves the Riemannian geometry of EEG signals during generation—not by mimicking raw waveforms, but through a covariance matrix constraint and a cycle-consistent decoder refinement strategy. This enables the synthesis of label-consistent and geometrically plausible EEG samples for data augmentation. Experimental results demonstrate that, under both cross-subject and within-subject settings, the proposed method yields modest yet consistent performance gains for covariance-structure-dependent classifiers such as Minimum Distance to Mean (MDM), thereby validating the efficacy and potential of structure-aware generative modeling in EEG data augmentation.

0 citationsRead paper

Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets

Jul 05, 2026

This work addresses the challenge of identifying optimal adjustment sets in data-scarce settings, where local causal discovery is hindered by insufficient samples, incomplete neighborhoods, and unresolved Markov equivalence classes. To overcome these limitations, the authors propose b-LOAD, a novel method that uniquely integrates structured prior knowledge directly into the local causal discovery process. By leveraging constrained edge information and dynamically expanding the local graph boundary via Meek rules, b-LOAD constructs a knowledge-constrained local partially directed acyclic graph. This approach monotonically refines the admissible equivalence class, substantially broadening the scope of identifiable causal queries and recovering optimal adjustment sets that are otherwise unidentifiable from observational data alone. Experimental results demonstrate that b-LOAD significantly outperforms purely data-driven and conventional knowledge-enhanced baselines under data scarcity and structural complexity, with validation on real biological networks confirming its efficacy and robustness.

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Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions

Jun 10, 2026

This work addresses a critical yet overlooked issue in knowledge distillation when combined with mixup: the teacher model is queried on out-of-distribution neighborhoods, leading to supervision signals corrupted by distributional confusion and impairing knowledge transfer. The authors propose a distillation mechanism that applies mixup exclusively to the student, thereby revealing for the first time the distributional mismatch between teacher and student. They demonstrate that calibration capability can be transferred independently of accuracy. By integrating temperature scaling with calibration-aware evaluation, the method’s universality across teachers of varying capacities is validated on CIFAR and ImageNet. Results show that the student not only achieves significantly higher accuracy but also exhibits an order-of-magnitude reduction in overconfidence, along with improved uncertainty estimation and representation geometry.

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