Currently: I am a Machine Learning Research Scientist at Altos Labs in San Diego. At Altos, my research program aims to develop ML methods that accelerate the process of scientific discovery in molecular biology, with an emphasis on spatial data and representation learning.
Research Interests: I am a machine learning researcher specializing in deep learning and computer vision. Broadly, my goal is to understand how we should combine AI and human experts to empower science. My work addresses questions like:
Previously: I completed my Ph.D. in Computing + Mathematical Sciences at Caltech in 2023, where I was advised by Pietro Perona and supported by an NSF Graduate Research Fellowship and an Explorer Grant from the Resnick Sustainability Institute. I graduated from Duke University in 2017 with a B.S.E. in Electrical and Computer Engineering and Mathematics. I've also spent time at Google Research, Microsoft Research, the Air Force Research Lab, the Duke University Marine Lab, and the Woods Hole Oceanographic Institution. I'm a proud product of public schools in Utah (Grantsville, Sandy, Salt Lake City), Nebraska (Omaha), and Texas (San Antonio).
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha
TLDR: Implicit neural representations for jointly estimating the spatial range of thousands of species from sparse data, with new benchmarks.
Paper Website Demo Code+Data Blog
On Label Granularity and Object Localization
Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge Belongie, Andrew Howard, Oisin Mac Aodha
TLDR: Increasing the accuracy and data efficiency of weakly supervised object localization by using coarse-grained labels.
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, Oisin Mac Aodha
CVPR 2021 (Oral)
TLDR: New benchmarks for self-supervised learning grounded in realistic fine-grained ecology tasks.
Paper Code+Data Video
Species Distribution Modeling for Machine Learning Practitioners: A Review
Sara Beery*, Elijah Cole*, Joseph Parker, Pietro Perona, Kevin Winner
ACM COMPASS 2021
TLDR: A review paper to help ML researchers learn about one of the most important problems in ecology and conservation.