About Me

I am a Ph.D. Candidate in Civil Engineering at the University of Illinois Urbana-Champaign, advised by Professor Erol Tutumluer, with an expected graduation in 2026.

My work sits at the intersection of transportation engineering, computer vision, and applied machine learning, with a focus on turning research into practical tools for field inspection and condition evaluation.

My research focuses on utilizing computer vision and deep learning to automate railway ballast inspection. This includes multimodal sensing, signal processing, image and point cloud segmentation, data synthesis, edge deployment, vision foundation models, and software systems that support large-scale ballast condition evaluation in real-world railway environments.

Selected Publications

BallastAttN: Occlusion-Robust 3D Railway Ballast Characterization using Data Synthesis and Deep Learning

Ding, K., Fan, Q., & Tutumluer, E.

Transportation Research Record 2026

Image-Based Moisture Content Prediction in Railway Ballast Using Deep Learning

Ding, K., & Tutumluer, E.

Geo-Congress 2026 2026

Condition Evaluation of Mud Spots in Railroad Ballast Using Deep Learning

Ding, K., Qamhia, I. I., Hart, J. M., & Tutumluer, E.

Journal of Infrastructure Systems 2025

Deep Learning Image Segmentation to Assess Field Ballast Angularity Index

Ding, K., Qamhia, I. I., Hart, J. M., & Tutumluer, E.

Geotechnical Frontiers 2025 2025

I-BALLAST: Computer Vision Solutions for Ballast Degradation Analysis Using Deep-Learning and Data Fusion Methods

Ding, K., Luo, J., Huang, H., Hart, J. M., Qamhia, I. I., Tutumluer, E., ... & Sussmann, T. R.

Geodata and AI 2024

Augmented Dataset for Vision-Based Analysis of Railroad Ballast via Multi-Dimensional Data Synthesis

Ding, K., Luo, J., Huang, H., Hart, J. M., Qamhia, I. I., & Tutumluer, E.

Algorithms 2024

For a fuller list, see the Publications page or my Google Scholar.

Education