Lauren Hoffman

Lauren Hoffman

Machine learning for Earth system science.

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portfolio

publications

Surface morphology and drug loading characterization of 3D-printed methacrylate-based polymer facilitated by supercritical carbon dioxide.

Published in The Journal of Supercritical Fluids, 2020

Characterization of 3D-printed PMMA impregnated with drugs using supercritical carbon dioxide processing.

Recommended citation: Ngo, T.T., L. Hoffman, G. Hoople, W. Trevena, U. Shakya, and G. Barr. (2020). "Surface morphology and drug loading characterization of 3D-printed methacrylate-based polymer facilitated by supercritical carbon dioxide." The Journal of Supercritical Fluids, 160, 104786.
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Ocean salinity response to atmospheric river precipitation events in the California Current System.

Published in Journal of Physical Oceanography, 2022

Observations and ocean model output are used to characterize the upper-ocean salinity response to atmospheric river events in the California Current System.

Recommended citation: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, and A. Varadarajan. (2022). "Ocean salinity response to atmospheric river precipitation events in the California Current System." Journal of Physical Oceanography, 52, 1867–1885.
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Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill.

Published in Artificial Intelligence for the Earth Systems, 2023

A neural network is trained to forecast daily Arctic sea-ice motion from remote sensing data, with explainable AI used to attribute predictive skill.

Recommended citation: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, P. Heimbach, C. Bitz, and K. Matsuyoshi. (2023). "Machine learning for daily forecasts of Arctic sea-ice motion: an attribution assessment of model predictive skill." Artificial Intelligence for the Earth Systems, 2, 230004.
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Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion.

Published in Artificial Intelligence for the Earth Systems, 2025

An assessment of how reliably common XAI methods explain neural network regression predictions of Arctic sea-ice motion.

Recommended citation: Hoffman, L., M. Mazloff, S.T. Gille, D. Giglio, and P. Heimbach. (2025). "Evaluating the trustworthiness of explainable artificial intelligence (XAI) methods applied to regression predictions of Arctic sea-ice motion." Artificial Intelligence for the Earth Systems, 4, e240027.
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Probabilistic forecasts of September Arctic sea ice extent at the interannual timescale with data-driven statistical models.

Published in Journal of Geophysical Research: Machine Learning and Computation, 2025

Data-driven statistical models are used to produce probabilistic forecasts of September Arctic sea ice extent at interannual lead times.

Recommended citation: Hoffman, L., F. Massonnet, and A. Sticker. (2025). "Probabilistic forecasts of September Arctic sea ice extent at the interannual timescale with data-driven statistical models." Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000669.
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Adaptive sampling of the upper ocean by autonomous floats during atmospheric river precipitation.

Published in Geophysical Research Letters, 2025

Autonomous floats are adaptively targeted to sample the upper ocean during atmospheric river precipitation events.

Recommended citation: Giglio, D., J. Sala, J. Gilson, L. Hoffman, and B. Kawzenuk. (2025). "Adaptive sampling of the upper ocean by autonomous floats during atmospheric river precipitation." Geophysical Research Letters, 52, e2025GL117069.
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Incorporating subsurface oceanic variables improves seasonal Antarctic sea ice prediction with neural networks.

Published in Journal of Geophysical Research: Atmospheres, 2026

Neural network forecasts of seasonal Antarctic sea ice improve when subsurface ocean variables are included as predictors.

Recommended citation: Dong, X., F. Massonnet, Y. Nie, B. Richaud, Y. Gao, L. Hoffman, Y. Wang, and Q. Yang. (2026). "Incorporating subsurface oceanic variables improves seasonal Antarctic sea ice prediction with neural networks." Journal of Geophysical Research: Atmospheres, 131, e2025JD045470.
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talks

teaching

Teaching Assistant

Graduate and undergraduate courses, University of California San Diego, 2018