Research Projects
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Active Data Selection for Extreme Weather Events
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B. Champenois and T. P. Sapsis.
What are the most informative data points for predicting extreme events?
Nonlinear Dynamics, 2025.
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S. Guth, B. Champenois, and T. P. Sapsis.
Application of Gaussian Process Multi-Fidelity Optimal Sampling to Ship Structural Modeling.
34th Symposium on Naval Hydrodynamics, June 2022.
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Data-Driven Modeling of Ocean Acidification
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B. Champenois, C. Bastidas, B. LaBash, and T. P. Sapsis.
Data-Driven Modeling of 4D Ocean and Coastal Acidification in the Massachusetts and Cape Cod Bays From Surface Measurements.
AGU JGR: Biogeosciences, June 2025.
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B. Champenois and T. P. Sapsis.
Machine Learning Framework for the Real-Time Reconstruction of Regional 4D Ocean Temperature Fields from Historical Reanalysis Data and Real-Time Satellite and Buoy Surface Measurements.
Physica D: Nonlinear Phenomena, January 2024.
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Modeling Ocean Flow from Lagrangian Drifter Observations
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B. Champenois and T. P. Sapsis.
Predicting Ocean Flow from Observed Lagrangian Trajectories via Bayesian Optimization in the Latent Space of an Autoencoder-Based Reduced-Order Model.
preprint +
2025 IEEE Oceans Conference Paper.
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C. Xia, B. Champenois, F. Campuzano, and R. Mendes.
Drifter Challenge: A Low-Cost, Hands-On Platform for Teaching Ocean Instrumentation and Sensing.
Oceanography: Ocean Education, September 2025.
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A. Papalia, C. Dawson, B. Champenois, et al.
A Roadmap for Climate-Relevant Robotics Research.
arXiv, July 2025.
