Dallas FosterOregon State University | OSU · Department of Mathematics
Dallas Foster
BSc
About
5
Publications
200
Reads
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47
Citations
Introduction
I currently am interested in Data Assimilation, Uncertainty Quantification in Machine Learning and their applications to dynamical systems and Earth science.
Additional affiliations
May 2020 - July 2020
Position
- Researcher
Description
- Developed a framework for informing subseasonal mixed layer depth estimates using satellite sea surface variable products. In this framework, I designed and trained probabilistic machine learning models to produce calibrated probabilistic mixed layer depth estimates. We are currently exploring the scientific application and interpretation of these mixed layer depth estimates.
July 2018 - September 2018
Position
- Research Assistant
Description
- Performed Bayesian statistical analysis on sea surface temperature anomalies using a linear inverse model framework. Developed a methodology that produces better calibrated forecasts of these anomalies and more accurate estimation of linear inverse model parameters.
September 2016 - present
Education
August 2016 - June 2021
August 2012 - May 2016
August 2012 - May 2016
Publications
Publications (5)
We propose improvements to the Dynamic Likelihood Filter (DLF), a Bayesian data assimilation filtering approach, specifically tailored to wave problems. The DLF approach was developed to address the common challenge in the application of data assimilation to hyperbolic problems in the geosciences and in engineering, where observation systems are sp...
Experimental evidence lends support to the conjecture that cell-to-cell communication plays a role in the gradient sensing of chemical species by certain chains of cells. Models have been formulated to explore this idea. For cells with no identifiable sensing structure, Mugler et al. [Proc. Natl. Acad. Sci. (U.S.A.) 113, E689 (2016)] have defined a...
Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies - typically focusing more on tropical...
The chemotactic dynamics of cells and organisms that have no specialized gradient sensing organelles is not well understood.
In fact, chemotaxis of this sort of organism is especially challenging to explain when the external chemical gradient is so small as to make variations of concentrations minute over the length of each of the organisms. Exper...
Sea ice features a dense inner pack ice zone surrounded by a marginal ice zone (MIZ) in which the sea ice properties are modified by interaction with the ice-free open ocean. The width of the MIZ is a fundamental length scale for polar physical and biological dynamics. Several different criteria for establishing MIZ boundaries have emerged in the l...
