Selected work

Representative Papers

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

Oct 01, 2026

This study addresses critical bottlenecks in artificial intelligence models for chemical engineering, including prediction inaccuracies, data scarcity, and limited interpretability, by exploring the deep integration of AI with first-principles modeling. Methodologically, this work proposes a paradigm shift from purely black-box approaches to physics-informed hybrid frameworks, synergizing atomistic simulations, process systems engineering, and physics-informed neural networks to ensure strict adherence to thermodynamic consistency and physical conservation laws. Consequently, the project significantly enhances predictive robustness and reliability while enabling efficient human–machine collaboration. By safeguarding engineering safety, this research amplifies the scientific value of core chemical engineering principles.

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Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

Sep 29, 2026

Existing root cause analysis (RCA) research is constrained by Top@k metrics, making it difficult to distinguish between failures originating in the retrieval and reranking stages. This work proposes a retrieval-reranking decoupled framework that constructs a two-stage pipeline comprising a multi-signal fusion retriever and a large language model-based reranker. The proposed approach achieves high-precision root cause localization without requiring causal graphs or annotated data. Experimental results demonstrate that this method comprehensively outperforms the strongest baselines across six benchmarks, improving Top@1 accuracy by up to 18 percentage points.

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Recent publications

Latest Papers

Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

Oct 01, 2026

This study addresses critical bottlenecks in artificial intelligence models for chemical engineering, including prediction inaccuracies, data scarcity, and limited interpretability, by exploring the deep integration of AI with first-principles modeling. Methodologically, this work proposes a paradigm shift from purely black-box approaches to physics-informed hybrid frameworks, synergizing atomistic simulations, process systems engineering, and physics-informed neural networks to ensure strict adherence to thermodynamic consistency and physical conservation laws. Consequently, the project significantly enhances predictive robustness and reliability while enabling efficient human–machine collaboration. By safeguarding engineering safety, this research amplifies the scientific value of core chemical engineering principles.

0 citationsRead paper

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

Sep 29, 2026

Existing root cause analysis (RCA) research is constrained by Top@k metrics, making it difficult to distinguish between failures originating in the retrieval and reranking stages. This work proposes a retrieval-reranking decoupled framework that constructs a two-stage pipeline comprising a multi-signal fusion retriever and a large language model-based reranker. The proposed approach achieves high-precision root cause localization without requiring causal graphs or annotated data. Experimental results demonstrate that this method comprehensively outperforms the strongest baselines across six benchmarks, improving Top@1 accuracy by up to 18 percentage points.

0 citationsRead paper