Current Projects

XPRIZE Rainforest

  • Since 2022 I have been developing computer vision tools for Limelight: Rainforest, the first place winners of the XPRIZE rainforest competition. XPRIZE Rainforest was a 10 million dollar competition to develop automated technology capable of rapidly surveying biodiversity in tropical forests, concluded in November of 2024.

  • An automated blacklight trap sampling insects in the canopy of the Brazilian Amazon
      An automated blacklight trap sampling insects in the canopy of the Brazilian Amazon
    The overarching goal of XPRIZE Rainforest is to develop technology that will allow rapid, automated surveys of biodiversity in highly complex tropical systems. Our solution is a canopy raft that takes data from a large number of separate streams, including image, audio, and DNA data, with a focus on insects. We also seek to avoid the colonialism tacitly present in many efforts to survey biodiversity by directly involving local indigenous communities in data collection and by incorporating indigenous knowledge of rainforest systems into our measures of rainforest communities.
    In June of 2023 Limelight: Rainforest competed in the XPRIZE Rainforest semifinals competition in Singapore. In a 24 hour period we were able to photograph more than 17,000 insects from 47 different families. With the addition of acoustic monitoring, DNA barcoding, and eDNA, we were able to identify 21 named species, 46 unique genera, 62 unique families, and 21 orders of plants and animals from the sampling area. Limelight: Rainforest was one of 6 finalist teams selected from the 13 competitors. We are now preparing for the finals competition in July of 2024 in Amazonas, Brazil. I am currently working on developing software for the automated training of insect identification models based on community science repositories and digitized insect collections, as well as building data pipelines for incorporating taxonomic, functional, and cultural information into cohesive stories about rainforest ecosystems.

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