Shuaiyu Zhao — Research Profile

Shuaiyu Zhao

I am a Ph.D. student in Chemical Engineering at Texas A&M University (TAMU), advised by Prof. Qingsheng Wang and Prof. Faisal Khan.

My research focuses on artificial intelligence for industrial process safety and automation, with an emphasis on fault detection and diagnosis, interpretable machine learning, and digital twins. I also explore the potential of large language models and generative AI for process systems engineering.

I am particularly interested in bridging AI research and real-world industrial applications, and welcome opportunities for academic and industry collaboration.

News

Oct 2026 Looking forward to attending the 29th MKO Process Safety Symposium in Houston. See you there!
Apr 2026 Our causal-guided graph transformer with dual-layer interpretability analysis was published in JLPPI.
Jan 2026 Our PRISMA-based review examines data-driven diagnosis, prognosis, and predictive maintenance across industrial process systems, now available in RESS. JIF 13.7 · 2025
Oct 2025 Our fine-tuned LLM framework for extracting structured insights from Texas Natech incident reports was published in PSEP.
Jun 2025 Participated in an MKO quarterly Steering Committee meeting hosted by Samsung Austin Semiconductor, discussing progress on industry-sponsored process safety research and visiting its semiconductor fabrication facilities.
Dec 2024 Our collaborative work with Minfeng Yu on PPO-based resilient control for safer exothermic CSTR operation was accepted for publication in PSEP.
Sep 2024 Our adaptive multiscale CNN and enhanced highway LSTM framework was published in RESS. HIGHLY CITED PAPER