Selected work

Representative Papers

Nonfundamentalness or missing information ? Evidence from causal-noncausal VARs in macro-finance

Jul 30, 2026

This study investigates whether the noncausal dynamics observed in macroeconomic VAR models stem from genuine non-fundamentalness or from omitted common information that is available to economic agents but unobserved by econometricians. To address this, the paper proposes a hybrid causal–noncausal VARX framework integrated with factor filtering and employs the generalized covariance (GCov) estimator to effectively identify and correct noncausal components. Empirical application to the Stock–Watson monetary policy SVAR demonstrates that the proposed approach substantially attenuates spurious noncausal signals, yielding impulse responses that align more closely with theoretical priors and notably alleviating the “price puzzle.” This refinement enables a more accurate recovery of the underlying causal structure of the economy.

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Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance

Jul 28, 2026

This work addresses the challenge of effectively leveraging privileged state information to improve observation representation learning in model-based reinforcement learning, particularly under asymmetric observation settings. Building upon the Dreamer framework, the authors propose a novel asymmetric world model training approach that introduces a latent guidance mechanism and a lightweight asymmetric representation learning objective. This design enhances the model’s capacity to exploit privileged information without requiring complex architectural modifications. Experimental results demonstrate that the proposed method consistently outperforms both the original Dreamer and existing asymmetric approaches across multiple benchmark tasks, achieving significant and stable performance gains.

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Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

Jul 28, 2026

This work addresses the challenge of eddy current–induced distortions in diffusion MRI, which cause misalignment across multi-shell images and compromise the accuracy of microstructural analysis. The authors propose the first end-to-end deep learning framework for joint correction of eddy currents and subject motion. In the first stage, a supervised image translation network harmonizes image contrast across shells; in the second stage, an unsupervised registration module incorporating physical constraints simultaneously estimates distortion and motion parameters, enabling full correction in a single forward pass. By circumventing conventional iterative optimization, the method achieves correction accuracy comparable to FSL Eddy while offering substantially faster inference. Trained on UK Biobank data, the approach is well-suited for large-scale studies and clinical deployment.

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An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning

Jul 23, 2026

This work addresses inverse problems in science and engineering—such as parameter inference and detector response unfolding—by proposing a unified simulation-based inference (SBI) framework that systematically integrates Bayesian and frequentist perspectives. Leveraging machine learning techniques, including neural posterior estimation and neural likelihood estimation, the framework enables efficient and general-purpose parameter inference, with extensions to empirical Bayes and unfolding tasks. The paper provides a comprehensive review of SBI methodologies and their application paradigms, while also offering a thorough analysis of validation strategies and inherent limitations. By clarifying best practices and pitfalls, this study advances the reliable deployment and innovative application of SBI in scientific domains.

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Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network

Jul 15, 2026

This study addresses the high computational cost of high-fidelity finite element simulation for laminated electromagnetic devices, which must simultaneously account for hysteresis and eddy current effects. The authors propose a general-purpose surrogate model based on a recurrent neural network (RNN), trained for the first time on diverse synthetic magnetic field sequences to jointly capture the coupled hysteresis–eddy current behavior. This RNN-based model is seamlessly embedded into a two-dimensional magnetic vector potential finite element framework. The approach achieves accuracy closely matching that of reference laminated models while incurring only approximately twice the computational overhead of simulations neglecting hysteresis—dramatically improving efficiency. The work also provides open-source, reusable standalone components to facilitate efficient engineering simulations.

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Recent publications

Latest Papers

Nonfundamentalness or missing information ? Evidence from causal-noncausal VARs in macro-finance

Jul 30, 2026

This study investigates whether the noncausal dynamics observed in macroeconomic VAR models stem from genuine non-fundamentalness or from omitted common information that is available to economic agents but unobserved by econometricians. To address this, the paper proposes a hybrid causal–noncausal VARX framework integrated with factor filtering and employs the generalized covariance (GCov) estimator to effectively identify and correct noncausal components. Empirical application to the Stock–Watson monetary policy SVAR demonstrates that the proposed approach substantially attenuates spurious noncausal signals, yielding impulse responses that align more closely with theoretical priors and notably alleviating the “price puzzle.” This refinement enables a more accurate recovery of the underlying causal structure of the economy.

0 citationsRead paper

Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance

Jul 28, 2026

This work addresses the challenge of effectively leveraging privileged state information to improve observation representation learning in model-based reinforcement learning, particularly under asymmetric observation settings. Building upon the Dreamer framework, the authors propose a novel asymmetric world model training approach that introduces a latent guidance mechanism and a lightweight asymmetric representation learning objective. This design enhances the model’s capacity to exploit privileged information without requiring complex architectural modifications. Experimental results demonstrate that the proposed method consistently outperforms both the original Dreamer and existing asymmetric approaches across multiple benchmark tasks, achieving significant and stable performance gains.

0 citationsRead paper

Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

Jul 28, 2026

This work addresses the challenge of eddy current–induced distortions in diffusion MRI, which cause misalignment across multi-shell images and compromise the accuracy of microstructural analysis. The authors propose the first end-to-end deep learning framework for joint correction of eddy currents and subject motion. In the first stage, a supervised image translation network harmonizes image contrast across shells; in the second stage, an unsupervised registration module incorporating physical constraints simultaneously estimates distortion and motion parameters, enabling full correction in a single forward pass. By circumventing conventional iterative optimization, the method achieves correction accuracy comparable to FSL Eddy while offering substantially faster inference. Trained on UK Biobank data, the approach is well-suited for large-scale studies and clinical deployment.

0 citationsRead paper

An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning

Jul 23, 2026

This work addresses inverse problems in science and engineering—such as parameter inference and detector response unfolding—by proposing a unified simulation-based inference (SBI) framework that systematically integrates Bayesian and frequentist perspectives. Leveraging machine learning techniques, including neural posterior estimation and neural likelihood estimation, the framework enables efficient and general-purpose parameter inference, with extensions to empirical Bayes and unfolding tasks. The paper provides a comprehensive review of SBI methodologies and their application paradigms, while also offering a thorough analysis of validation strategies and inherent limitations. By clarifying best practices and pitfalls, this study advances the reliable deployment and innovative application of SBI in scientific domains.

0 citationsRead paper

Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network

Jul 15, 2026

This study addresses the high computational cost of high-fidelity finite element simulation for laminated electromagnetic devices, which must simultaneously account for hysteresis and eddy current effects. The authors propose a general-purpose surrogate model based on a recurrent neural network (RNN), trained for the first time on diverse synthetic magnetic field sequences to jointly capture the coupled hysteresis–eddy current behavior. This RNN-based model is seamlessly embedded into a two-dimensional magnetic vector potential finite element framework. The approach achieves accuracy closely matching that of reference laminated models while incurring only approximately twice the computational overhead of simulations neglecting hysteresis—dramatically improving efficiency. The work also provides open-source, reusable standalone components to facilitate efficient engineering simulations.

0 citationsRead paper