Wooden Robot Lab
AI for Ecology
The Wooden Robot Lab develops machine learning and computer vision tools to analyze complex ecological systems, with particular focus on forest ecosystems. By combining advanced AI architectures with ecological expertise, the lab creates adaptive tools that can learn from environmental data to support conservation, biodiversity assessment, and ecosystem management.
Current Focus
Decoding Nature's Chemistry
Our lab employs deep learning frameworks to decode the chemistry of nature. By predicting efficacy from molecular structure, we accelerate the discovery of bioactive compounds without the need for exhaustive physical testing. Our primary focus is identifying novel insect attractants and repellents to support sustainable ecosystem management.
Generative Landscape Analysis
We utilize geospatial foundation models to scale environmental understanding beyond human capacity. Through target-specific fine-tuning, we adapt these models for precise semantic segmentation and species distribution modeling. This approach turns vast datasets of satellite imagery into a granular, actionable understanding of forest health and system complexity.
Multimodal Edge Inference
We engineer custom neural networks to detect and classify species from image and audio streams. To bridge the gap between model development and field application, we utilize Ocelli, a motion and audio-activated platform that deploys models for offline monitoring. Our latest classifier, Elytra 1.0, achieves 91.27% accuracy across 3,127 North American insect species while processing images in 1.4 milliseconds at just 30 MB—enabling real-time identification on mobile devices without internet connectivity.
Elytra 1.0 Classifier
Drop an insect image here
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