Selected work

Representative Papers

Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization

Aug 03, 2026

This work addresses the high computational cost and constraint-handling challenges in gray-box optimization arising from the coupling between black-box and white-box variables. To this end, it proposes a bilevel decomposition approach that exploits variable separability: the outer loop employs Bayesian optimization to optimize only the black-box variables, while the inner loop solves the white-box subproblem to global optimality using deterministic global optimization. Notably, this method is the first in gray-box Bayesian optimization to rigorously enforce white-box constraints without resorting to penalty functions, chance constraints, or moment approximations, thereby significantly enhancing both efficiency and robustness. Empirical results across 13 benchmark problems demonstrate that the proposed approach achieves lower regret with fewer iterations and less runtime, while exhibiting strong robustness to variations in initialization size, exploration parameters, and choice of inner-loop solver.

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A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields

Jul 28, 2026

This study addresses the challenge of economically viable real-time gas lift optimization in unconventional reservoirs, where reliance on costly downhole pressure gauges or multi-rate testing hinders widespread implementation. To overcome this limitation, the authors propose a data-driven automated workflow that leverages only historical production time-series data to train machine learning models for predicting gas lift performance curves. Coupled with Bayesian optimization, the framework identifies optimal gas injection rates while respecting operational constraints, eliminating the need for additional downhole instrumentation or complex well tests. Validated in the Bakken region across 30 pilot wells, the approach achieved an average production uplift exceeding 5% and has since been successfully scaled to over 200 gas-lift and plunger-assisted gas-lift wells, demonstrating both effectiveness and scalability in constrained asset environments.

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Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels

Jul 25, 2026

This work addresses a critical gap in current AI systems, which often conflate technical capability with operational authority, resulting in inadequate governance of authorized autonomy. The paper proposes a structured governance framework that systematically distinguishes between an AI system’s Autonomous Capability Level (ACL) and its Authorized Autonomy Level (AAL). By integrating risk exposure, action reversibility, and accountability, the framework introduces a dynamic authorization mechanism that decouples capability from permission. Validated in enterprise-grade data engineering agents, the approach enables high-capability systems to be safely constrained to lower authorization levels aligned with organizational risk tolerance. Through layered autonomy modeling and risk-aware decision protocols, the framework ensures that AI autonomy remains both effective and responsibly governed.

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Topological Data Analysis for High-Dimensional Dynamic Process Monitoring

Jun 18, 2026

This study addresses the challenge of monitoring dynamic processes in high-dimensional time series by proposing a novel approach that integrates topological data analysis (TDA) with neural ordinary differential equations (Neural ODEs). The method represents multivariate time series as manifolds, employs topological descriptors to capture their structural properties, and leverages Neural ODEs to model the continuous evolution of the system’s topology, enabling trajectory-based real-time event detection. To the best of our knowledge, this work is the first to combine TDA with Neural ODEs for process monitoring, overcoming limitations of conventional reconstruction- or Koopman-based methods in capturing topological changes in high-dimensional dynamics. Experimental results on industrial datasets demonstrate that the proposed method significantly outperforms baseline approaches—including PCA, autoencoders, and Koopman autoencoders—across multiple event detection tasks.

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Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data

Mar 02, 2026

This work addresses the high data acquisition cost often encountered in data-driven modeling of dynamical systems by proposing an efficient fine-tuning approach based on Subset Extended Kalman Filtering (SEKF). The method enables successful transfer of a pretrained neural network model to a new dynamical system using only 1% of the original training data. By integrating Bayesian estimation with dynamical system modeling, SEKF substantially reduces both data requirements and computational overhead while enhancing model generalization. Experimental validation on a damped spring-mass system and a continuous stirred-tank reactor demonstrates that fine-tuning only a small subset of parameters suffices to accurately capture the target dynamics, thereby confirming the efficacy and practicality of the proposed approach.

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Recent publications

Latest Papers

Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization

Aug 03, 2026

This work addresses the high computational cost and constraint-handling challenges in gray-box optimization arising from the coupling between black-box and white-box variables. To this end, it proposes a bilevel decomposition approach that exploits variable separability: the outer loop employs Bayesian optimization to optimize only the black-box variables, while the inner loop solves the white-box subproblem to global optimality using deterministic global optimization. Notably, this method is the first in gray-box Bayesian optimization to rigorously enforce white-box constraints without resorting to penalty functions, chance constraints, or moment approximations, thereby significantly enhancing both efficiency and robustness. Empirical results across 13 benchmark problems demonstrate that the proposed approach achieves lower regret with fewer iterations and less runtime, while exhibiting strong robustness to variations in initialization size, exploration parameters, and choice of inner-loop solver.

0 citationsRead paper

A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields

Jul 28, 2026

This study addresses the challenge of economically viable real-time gas lift optimization in unconventional reservoirs, where reliance on costly downhole pressure gauges or multi-rate testing hinders widespread implementation. To overcome this limitation, the authors propose a data-driven automated workflow that leverages only historical production time-series data to train machine learning models for predicting gas lift performance curves. Coupled with Bayesian optimization, the framework identifies optimal gas injection rates while respecting operational constraints, eliminating the need for additional downhole instrumentation or complex well tests. Validated in the Bakken region across 30 pilot wells, the approach achieved an average production uplift exceeding 5% and has since been successfully scaled to over 200 gas-lift and plunger-assisted gas-lift wells, demonstrating both effectiveness and scalability in constrained asset environments.

0 citationsRead paper

Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels

Jul 25, 2026

This work addresses a critical gap in current AI systems, which often conflate technical capability with operational authority, resulting in inadequate governance of authorized autonomy. The paper proposes a structured governance framework that systematically distinguishes between an AI system’s Autonomous Capability Level (ACL) and its Authorized Autonomy Level (AAL). By integrating risk exposure, action reversibility, and accountability, the framework introduces a dynamic authorization mechanism that decouples capability from permission. Validated in enterprise-grade data engineering agents, the approach enables high-capability systems to be safely constrained to lower authorization levels aligned with organizational risk tolerance. Through layered autonomy modeling and risk-aware decision protocols, the framework ensures that AI autonomy remains both effective and responsibly governed.

0 citationsRead paper

Topological Data Analysis for High-Dimensional Dynamic Process Monitoring

Jun 18, 2026

This study addresses the challenge of monitoring dynamic processes in high-dimensional time series by proposing a novel approach that integrates topological data analysis (TDA) with neural ordinary differential equations (Neural ODEs). The method represents multivariate time series as manifolds, employs topological descriptors to capture their structural properties, and leverages Neural ODEs to model the continuous evolution of the system’s topology, enabling trajectory-based real-time event detection. To the best of our knowledge, this work is the first to combine TDA with Neural ODEs for process monitoring, overcoming limitations of conventional reconstruction- or Koopman-based methods in capturing topological changes in high-dimensional dynamics. Experimental results on industrial datasets demonstrate that the proposed method significantly outperforms baseline approaches—including PCA, autoencoders, and Koopman autoencoders—across multiple event detection tasks.

0 citationsRead paper

Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data

Mar 02, 2026

This work addresses the high data acquisition cost often encountered in data-driven modeling of dynamical systems by proposing an efficient fine-tuning approach based on Subset Extended Kalman Filtering (SEKF). The method enables successful transfer of a pretrained neural network model to a new dynamical system using only 1% of the original training data. By integrating Bayesian estimation with dynamical system modeling, SEKF substantially reduces both data requirements and computational overhead while enhancing model generalization. Experimental validation on a damped spring-mass system and a continuous stirred-tank reactor demonstrates that fine-tuning only a small subset of parameters suffices to accurately capture the target dynamics, thereby confirming the efficacy and practicality of the proposed approach.

0 citationsRead paper