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About Ari ↓

Hello!

I'm currently a 6th year PhD candidate studying chemical ecology in the EECB program at the University of Nevada Reno. I received my BS in entomology from Cornell university in 2019, where I worked in labs broadly studying non-consumptive effects of predators, induced plant defenses, and how chemical signaling mediates pollinator and social insect behavior.

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Research

Oncopeltus fasciatus robbing nectar from Asclepias fascicularis

    Oncopeltus fasciatus robbing nectar from Asclepias fascicularis

My research broadly focuses on how specialized chemistry mediates interactions between insects and plants, from the level of the individual, to communities, to global patterns of chemical and biological diversity. I aim to combine field and laboratory approaches with state of the art analytical and statistical tools to elucidate subtle processes.

My current research interests include

  • Latitudinal gradients in species interactions and functional diversity
  • The role of plant physical and chemical traits in mediating herbivore and pollinator interactions
  • Intra and interspecific variation in nectar chemistry and the adaptive role of nectar toxins
  • Global patterns of semiochemical richness
  • The application of machine learning and computer vision for rapid surveys of biodiversity.

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Projects

A drone lowers an automated blacklight trap into the canopy in the Amazon rainforest

    A drone lowers an automated blacklight trap into the canopy in the Amazon rainforest

In 2022 I became involved in the XPRIZE Rainforest competition to develop methods of rapidly surveying tropical biodiversity. I am the computer vision and data pipeline lead for Limelight: Rainforest. Our goal is to use multi-modal data collection and state-of-the-art analytic tools to identify taxa, with a special focus on insect communities, to provide critical insights for rainforest conservation.

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Software

State of the art computer vision models allow automated annotation of field images and video

    State of the art computer vision models allow automated annotation of field images and video

I frequently build tools to assist in processing the complicated data associated with chemistry, insect behavior, and global biodiversity repositories. Open source tools I've built include:

  • Computer vision models for localizing and identifying insects in field videos
  • Annotators for rapidly associating image data with taxonomic and ecological information

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