Justin Kay

I am a PhD student at MIT, advised by Sara Beery and supported by fellowships from MIT EECS and NSF. My research focuses on making computer vision and machine learning systems more deployable and informative for science and decision-making, particularly for environmental and climate applications.

Before coming to MIT, I co-founded Ai.Fish, a computer vision company focused on applications in sustainable fisheries management and ocean conservation. I served as CTO from 2019–2023 and currently act as a technical advisor. From 2021–2023 I was also a researcher in the Computational Vision Lab at Caltech, advised by Pietro Perona. I hold a BS in EECS from UC Berkeley.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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Recent News
2024-03-19: We released ALDI! See the accompanying preprint, codebase, and dataset.
2023-12-16: I presented a Spotlight Talk at the Computational Sustainability Workshop at NeurIPS. Recording available here.
2023-12-01: Our poster on ALDI won Best Poster Honorable Mention at NECV!
2023-10-13: I presented "AI for Fisheries Monitoring: Challenges and Opportunities" at the Korean Ministry of Fisheries Artificial Intelligence and Electronic Monitoring Expert Seminar in Seoul.
2023-10-05: I organized the Exploring AI for Fisheries in Depth workshop at SAFET in Bali. A recording of my talk, "Fair Evaluation of AI systems," is available here.
2023-08-01: Ai.Fish was awarded a NOAA SBIR Phase II grant for our project "Cloud-based Automated Electronic Monitoring for Fisheries of the Future"! I will serve as Co-Investigator. Read more here.

Selected Publications
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Align and Distill: Unifying and Improving Domain Adaptive Object Detection


Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, and Grant Van Horn
Preprint, 2024
☆ Best poster honorable mention, NECV 2023
website / arxiv / code /

A unified framework for domain adaptive object detection that addresses systemic benchmarking pitfalls and achieves state-of-the-art performance across diverse benchmarks.
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Unsupervised Domain Adaptation in the Real World: A Case Study in Sonar Video


Justin Kay, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, and Grant Van Horn
Computational Sustainability Workshop at NeurIPS, 2023
☆ Spotlight talk
website / presentation /

Highlighting the promises and pitfalls of unsupervised domain adaptation through a real-world case study in fisheries monitoring.
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The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting


Justin Kay, Peter Kulits, Suzanne Stathatos, Siqi Deng, Erik Young, Sara Beery, Grant Van Horn, and Pietro Perona
European Conference on Computer Vision (ECCV), 2022
website / arxiv / poster / presentation /

A large-scale dataset for detecting, tracking, and counting fish in sonar videos, an important conservation application and a rich data source for advancing low signal-to-noise computer vision applications and tackling domain generalization in tracking and counting.
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Fine-Grained Counting with Crowd-Sourced Supervision


Justin Kay, Catherine M. Foley, and Tom Hart
Computer Vision for Animal Behavior Tracking and Modeling Workshop at CVPR, 2022
website / arxiv / poster /

A dataset and method for fine-grained (i.e. multiclass) seal counting using noisy volunteer dot annotations collected from the Zooniverse citizen science platform.
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The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries


Justin Kay and Matt Merrifield
8th Workshop on Fine-Grained Visual Categorization at CVPR, 2021
website / arxiv / poster /

A large and diverse image dataset sourced from fisheries electronic monitoring. Evaluation of existing detection and classification algorithms and quantification of key challenges.

Design and source code from here.