Elijah Cole

[ scholar ] [ datasets ] [ github ] [ blog ] [ short cv ]

About

I am a computer scientist. My primary interests are machine learning and computer vision, with an emphasis on creating machine learning systems that enable scientific discoveries.

Currently, I am a Research Scientist at GenBio AI. I am working on large multimodal AI models for biology, particularly at the cell and tissue level.

Previously:

Selected Publications

Impact of Segmentation Errors in Analysis of Spatial Transcriptomics Data
Jonathan Mitchel, Teng Gao, Elijah Cole, Viktor Petukhov, Peter Kharchenko
Under Review
TLDR: A matrix factorization method for mitigating the confounding effect of segmentation errors in spatial transcriptomics data.
[paper]

WildSAT: Learning Satellite Image Representations from Wildlife Observations
Rangel Daroya, Elijah Cole, Oisin Mac Aodha, Grant Van Horn, Subhransu Maji
ICCV 2025
TLDR: Using LLMs and geospatial location encodings to learn good embeddings for satellite imagery.
[paper]

Feedforward Few-shot Species Range Estimation
Christian Lange, Max Hamilton, Elijah Cole, Alexander Shepard, Samuel Heinrich, Angela Zhu, Subhransu Maji, Grant Van Horn, Oisin Mac Aodha
ICML 2025
TLDR: Multimodal transformer for predicting the spatial distribution of unseen species from few training examples.
[paper]

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification
Markus Marks, Manuel Knott, Neehar Kondapaneni, Elijah Cole, Thijs Defraeye, Fernando Perez-Cruz, Pietro Perona
IJCV 2025
TLDR: Do common self-supervised learning evaluation techniques actually predict performance on downstream tasks?
[paper]

Combining Observational Data and Language for Species Range Estimation
Max Hamilton, Christian Lange, Elijah Cole, Alexander Shepard, Samuel Heinrich, Oisin Mac Aodha, Grant Van Horn, Subhransu Maji
NeurIPS 2024
TLDR: Using natural language to enable zero-shot estimation of the spatial distribution of a species.
[paper]

From Coarse to Fine-Grained Open-Set Recognition
Nico Lang, Vésteinn Snæbjarnarson, Elijah Cole, Oisin Mac Aodha, Christian Igel, Serge Belongie
CVPR 2024
TLDR: Revealing the factors that make open-set recognition easy or difficult, with new benchmarks.
[paper]

Active Learning-Based Species Range Estimation
Christian Lange, Elijah Cole, Grant Van Horn, Oisin Mac Aodha
NeurIPS 2023
TLDR: An active learning approach to rapidly estimate the spatial distribution of a novel species.
[paper]

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]

Contact

Feel free to reach out via [email] or [linkedin].