Research
My research lies at the intersection of data science, animal welfare science, and AI bias. As a PhD candidate at the UBC Animal Welfare Program, and a collaborator at the UBC TrustML Research Cluster, I contribute to building reliable, secure, explainable, and ethical AI systems. My previous research focused on advancing automated methods for behavior monitoring, redefining dominance hierarchy calculations, creating novel approaches for lameness detection, and understanding behavioral baselines for individual animals from sensor data. Currently, my work explores the representation bias of livestock farming in generative AI.
Publications
The erasure of intensive livestock farming in text-to-image generative AI
ACM Conference on Fairness, Accountability, and Transparency (FAccT) (2025)
Redefining Lameness Assessment: Constructing Lameness Hierarchy using
Crowd-Sourced Data
Computers and Electronics in Agriculture (2025)
Redefining dominance calculation: Increased competition flattens the dominance
hierarchy in dairy cows
Journal of Dairy Science, 107(9): 7286-7298 (2024)
Crowd sourcing remote comparative lameness assessments for dairy cattle
Journal of Dairy Science, 106(8): 5715-5722 (2023)
AI for One Welfare: the role of animal welfare scientists in developing valid and
ethical AI-based welfare assessment tools
Frontiers in Veterinary Science (2025)
Automated, longitudinal measures of drinking behavior provide insights into the
social hierarchy in dairy cows
JDS Communications, 5(5): 411-415 (2024)
Effects of group size on agonistic interactions in dairy cows: a descriptive
study
Animal, 18(3): 101083 (2024)
Research Interests
Current Focus
- AI bias and representation in animal welfare contexts
- Automated farm animal behaviour andwelfaremonitoring
- Reproducible research methodologies
Technical Expertise
- The application of AI agents and generative AI models (e.g., large language models and text-to-image models)
- Machine learning and computer vision for animal behavior analysis
- Crowdsourcing and human-AI collaboration
- Reproducible data science workflow and software development (R, Python)