Ashley joined the lab as a Ph.D. student in 2024 and is also a Data Scientist at Canadian National Railway (CN). She studies vision-language models for railway asset management. At the 7th Toronto Machine Learning Summit (TMLS), she presented automated rail-car inspection with computer vision.
Talks
- Automated Inspection for Rail Cars Using Computer Vision and Machine Learning, 7th Toronto Machine Learning Summit (TMLS)
Current projects
- Vision-language models for railway asset management (ISPRS 2026 review)
Publications (3)
2026
- Ghorbanalivakili, M., Varghese, A., and Sohn, G. (2026). Reasoning-guided Ego-path Segmentation for Autonomous Trains using Vision-Language Models. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, pp. 89-96. doi:10.5194/isprs-archives-XLIX-B3-2026-89-2026
- Varghese, A., Ghorbanalivakili, M., and Sohn, G. (2026). The Emerging Role of Vision-Language Models in the Automation of Railway Asset Management: A Review and Future Perspective. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B2-2026. doi:10.5194/isprs-archives-XLIX-B2-2026-745-2026
Preprints and Under Review
- Sohn, G., Ghorbanalivakili, M., and Varghese, A. Triplet-based Railway Ego-path Instance Tracing Using Learned Attraction Field Representation. SSRN 6827662.