Ph.D. Candidate at Carnegie Mellon University
Human-AI Decision Making for Engineering Systems
I’m a Ph.D. candidate in Civil and Environmental Engineering at Carnegie Mellon University.
I develop human-AI decision systems for complex engineering operations. My work connects procedures, incident records, design knowledge, and real-time conditions so that people can make decisions with evidence they can inspect and trace.
View my CV for more details.
Infrastructure operators and designers must reconcile procedures, system conditions, historical incidents, and incomplete information. I study how AI can connect these sources while keeping its recommendations understandable and accountable.
Designing AI systems that help people interpret complex conditions and make traceable decisions in safety-critical work
Helping teams turn early design concepts into operationally useful systems while preserving decisions, assumptions, and open questions
Structuring procedures, equipment, incidents, and design choices as knowledge graphs that AI can retrieve and explain
Evaluating how AI support affects consistency, workload, uncertainty, trust, and human responsibility
2026
At Idaho National Laboratory, developed a multimodal pipeline combining document layout detection and LLM-based extraction to transform nuclear operating procedures into structured knowledge graphs for the DOE Light Water Reactor Sustainability Program.
2025–Present
Developing a GraphRAG-based assistant for PFAS treatment systems that helps engineers refine process flow diagrams into P&IDs while tracking decisions, assumptions, and open questions.
2024–2026
Developed BuildLink, a GraphRAG assistant that connects procedure and incident knowledge graphs with operator interfaces to support traceable decisions during remote microreactor operations.
2024
Developed a dataset of 101,381 images to improve the detection of hanging objects on construction sites, addressing safety challenges in lifting operations.
2024
Developed a predictive model using fine-tuned GPT and saliency visualization to analyze 15,000 construction accident records. Achieved 82% accuracy in classifying six accident types, demonstrating AI’s potential to enhance safety management.
2023
Developed an audio-visual multimodal model to accurately recognize and monitor concurrent activities of multiple equipment in tunnel construction projects. The model enhances operational efficiency by integrating spatial and temporal contexts, achieving an F-score of 96.3% in real-world data testing.
2023
Developed a sensor-based model for recognizing worker activities without the need for individual re-training. Achieved 78.64% accuracy using a variational-denoising autoencoder, outperforming existing benchmarks.
2023
Developed a data-driven approach to detect slip, trip, and fall hazards by analyzing workers’ loss of body balance using wearable sensors and GPS-based location mapping.
| Jul 2026 | Presented my work at PSAM 18 |
| Jun 2026 | Joined Idaho National Laboratory as a Summer Research Intern |
| Feb 2026 | Selected as a Claire and John Bertucci Fellow in Engineering |
| Aug 2025 | Selected as a CUAHSI Hydroinformatics Innovation Fellow |
| Jul 2025 | Participated in DesignSafe SPARC Program |
| Apr 2025 | Selected as a Chishiki-AI Fellow |
| Mar 2025 | Presented my poster at CMU Energy Week |
| Feb 2025 | Passed my Ph.D. qualifying exam |
| Aug 2024 | Joined HMHI at CMU |
I’m always happy to connect about research, collaboration, or related ideas.