Sara Beery

Sara Beery is the Homer A. Burnell Career Development Professor and assistant professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. Beery seeks to break down knowledge barriers between fields and works closely with Microsoft AI for Earth, Google Research, and Wildlife Insights to turn her research into usable tools for the ecological community. Beery received a PhD in computing and mathematical sciences from Caltech and a BS in electrical engineering and mathematics from Seattle University.

Publications

• Kay, J., Stathatos, S., Deng, S., Young, E., Perona, P., Beery, S., & Van Horn, G. (2023). Unsupervised domain adaptation in the real world: a case study in sonar video. In NeurIPS Computational Sustainability: Promises and Pitfalls from Theory.
• Chen, J., Hu, M., Coker, D. J., & Berumen, M. L. (2023). MammalNet: a large-scale video benchmark for mammal recognition and behavior understanding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13052-13061.
• Kay, J. et al. (2022). The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting. In Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham.

Media

• February 6, 2025: MIT News, Streamlining data collection for improved salmon population management
• December 20, 2024: MIT News, Ecologists find computer vision models’ blind spots in retrieving wildlife images
• December 13, 2024: MIT News, MIT affiliates named 2024 Schmidt Sciences AI2050 Fellows