Seongeun Park

Redefining safety and decision-making at the intersection of AI and human intelligence

Ph.D. in Civil and Environmental Engineering, Carnegie Mellon University (2024 - Present)
M.S. in Architectural Engineering, Seoul National University (2021 - 2023)
B.S. in Architectural Engineering, Kyung Hee University (2016 - 2020)

What I’m Exploring

I'm fascinated by the intersection of AI and human decision-making, particularly in high-risk environments. My research aims to enhance safety and operational efficiency by integrating AI-driven insights with human expertise.

Human-AI Decision Making

Understanding how humans and AI collaborate in critical decision-making under uncertainty

AI for Safety Management

Developing intelligent AI systems that enhance workplace safety in high-risk industries

Knowledge Graphs & LLMs

Building structured knowledge systems to support decision-making and hazard recognition

Human-Centered AI

Ensuring AI systems align with human cognitive processes and decision workflows

Automation & Trust

Exploring how automation impacts human trust and performance in high-stakes scenarios

AI for Infrastructure Operations

Applying AI-driven solutions to improve efficiency and safety in infrastructure management

Real-Time Safety Decision Support for Nuclear Power Plant Operators (2025)

Developing a real-time decision support system using Knowledge Graphs and RAG to help operators anticipate risks and verify safety guidelines for 4th generation nuclear microreactors.

HangCon: Benchmark Dataset for Hanging Object Detection (2025)

Developed a dataset of 101,381 images to improve the detection of hanging objects on construction sites, addressing safety challenges in lifting operations.

Contextual Multimodal Recognition for Tunnel Construction (2024)

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.

AI-Driven Accident Prediction for Construction Safety (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.

Human-Independent Activity Recognition for Construction Workers (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.

Collective Sensing for Slip, Trip, and Fall Hazard Identification (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.

Get in touch

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