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:

  • How can machine learning make scientific research faster and cheaper?
  • How should machine learning systems incorporate domain knowledge and feedback from scientists?
  • How can machine learning make new kinds of scientific inquiry possible?

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).

Selected Publications

See Google Scholar for a complete list.

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
ICML 2023
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
ECCV 2022
TLDR: Increasing the accuracy and data efficiency of weakly supervised object localization by using coarse-grained labels.
Paper Website

When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie
CVPR 2022
TLDR: An empirical analysis of self-supervised learning through the lens of real-world problems.
Paper Website

Multi-Label Learning from Single Positive Labels
Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic
CVPR 2021
TLDR: It's possible to train effective multi-label image classifiers using only one positive label per image.
Paper Code Video

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.
Paper

Presence-Only Geographical Priors for Fine-Grained Image Classification
Oisin Mac Aodha, Elijah Cole, Pietro Perona
ICCV 2019
TLDR: Efficient algorithms for using spatio-temporal information to improve fine-grained image classification.
Paper Code Video

Based on Plain Academic