Nathan Fox

What I do:
Nathan Fox is an AI Scientist at MIDAS, specializing in developing and applying AI tools to help scientists analyze complex environmental datasets. His work focuses on making machine learning, computer vision, and geospatial analysis accessible for biodiversity monitoring, conservation, and ecosystem research.

Who I am:
Before joining MIDAS, Nathan was a Schmidt AI in Science Fellow at the University of Michigan, where he applied AI to ecological and conservation challenges. He holds a Ph.D. in Ocean and Earth Sciences from the University of Southampton. His background spans AI-driven biodiversity monitoring, geospatial data analysis, and citizen science applications. He collaborates across disciplines to integrate AI into scientific workflows and environmental decision-making.

Research Description:
Nathan’s research focuses on leveraging AI for biodiversity science, conservation, and human-nature interactions. He specializes in computer vision, machine learning, and geospatial analysis, utilizing foundation vision models and natural language processing to extract insights from citizen science data, social media imagery, and ecological datasets.
Key areas of his work include:
– Automated Species Identification: Developing deep learning models to classify species in images, enabling scalable biodiversity tracking.
– Human-Nature Interactions: Applying AI to assess how people engage with biodiversity, from urban greenspaces to conservation areas.
– Sensitive Data Protection in AI: Ensuring AI-driven monitoring tools safeguard sensitive species and human privacy.

By integrating these approaches, his work enhances conservation efforts, biodiversity monitoring, and ethical AI applications in environmental science.
Recent fun projects:
– AI for Biodiversity Monitoring: Using computer vision to identify species in social media images, improving global tracking of species distributions.
– Protecting Sensitive Data with AI: Implementing Visual Question Answering (VQA) models to detect and safeguard crowdsourced biodiversity images containing sensitive species or human presence.
– AI for Habitat Mapping: Creating tutorials on using segmentation models like Segment Anything (SAM) to delineate habitats from aerial imagery.

Why I’m passionate about my work:
Nathan is passionate about making AI tools more accessible to scientists. He believes AI has the potential to transform research by making workflows more scalable, inclusive, and efficient. His goal is to bridge the gap between cutting-edge AI research and practical applications that scientists and conservationists can use to protect biodiversity and ecosystems.

Fun facts:
Originally from Southampton, United Kingdom, and an avid fan of Southampton FC.
Enjoys hiking and wildlife photography.
Loves live music and plays bass guitar as a hobby.