Nicholas LaHaye

Research Data Scientist, GeoAI; Environmental Mapping, Spatial Informatics Group

Joined

November 2022

Names

Nicholas LaHaye (Preferred)

Emails

****@jpl.nasa.gov (Confirmed)

****@chapman.edu (Confirmed)

****@sig-gis.com (Confirmed)

Personal Links

Homepage

Google Scholar

ORCID

Career & Education History

Research Data Scientist

GeoAI; Environmental Mapping, Spatial Informatics Group (sig-gis.com)

2025 – Present

Research Data Scientist

Machine Learning and Instrument Autonomy Group, Jet Propulsion Laboratory (jpl.nasa.gov)

2021 – 2025

Data Scientist

Processing Algorithms and Calibration Engineering Group, Jet Propulsion Laboratory (jpl.nasa.gov)

2018 – 2021

PhD student

Computational and Data Sciences, Chapman University (chapman.edu)

2016 – 2021

Software Engineer

Processing Algorithms and Calibration Engineering Group, Jet Propulsion Laboratory (jpl.nasa.gov)

2013 – 2018

MS student

Computer Science, University of Southern California (usc.edu)

2013 – 2016

Undergrad student

Computer Science and Mathematics, Chapman University (chapman.edu)

2009 – 2013

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Nicholas LaHaye, Thilanka Munasinghe, Hugo Kyo Lee, Xiaohua Pan, Gonzalo Gonzalez Abad, Hazem Mahmoud, Jennifer Wei
- IEEE International Geoscience and Remote Sensing Symposium
- Readers: Everyone
A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking
Nicholas LaHaye, Michael Joseph Garay, Brian D Bue, Hesham El-Askary, Erik Linstead
- MDPI Remote Sensing
- Readers: Everyone
Learning in the machine: To share or not to share?
Jordan Ott, Erik Linstead, Nicholas LaHaye, Pierre Baldi
- Elsevier Neural Networks
- Readers: Everyone
Multi-Modal Object Tracking and Image Fusion With Unsupervised Deep Learning
Nicholas LaHaye, Jordan Ott, Michael Joseph Garay, Hesham El-Askary, Erik Linstead
- IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
- Readers: Everyone
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the Fire Influence on Regional to Global Environments and Air Quality Datasets
Nicholas LaHaye, Anastasija Easley, Kyongsik Yun, Hugo Kyo Lee, Erik Linstead, Michael Joseph Garay, Olga V. Kalashnikova
- MDPI Remote Sensing
- Readers: Everyone
Self-supervised contrastive learning for wildfire detection: Utility and limitations
Jae Won Choi, Nicholas LaHaye, Yuzhou Chen, Hugo Kyo Lee, Yulia Gel
- Advances in Machine Learning and Image Analysis for GeoAI
- Readers: Everyone

Nicholas LaHaye

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data

A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking

Learning in the machine: To share or not to share?

Multi-Modal Object Tracking and Image Fusion With Unsupervised Deep Learning

Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the Fire Influence on Regional to Global Environments and Air Quality Datasets

Self-supervised contrastive learning for wildfire detection: Utility and limitations

Co-Authors