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Automatically discovering ordinary differential equations from data with sparse regression

Published in Communications Physics, 2024

Developed ARGOS methodology combining denoising techniques, sparse regression, and bootstrap confidence intervals to automatically discover dynamical systems from noisy data.

Recommended citation: Egan, K., Li, W. & Carvalho, R. (2024). "Automatically discovering ordinary differential equations from data with sparse regression." Communications Physics. 7, 20.
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Discovering Governing Equations from Data using Sparse Regression

Published: July 01, 2021

Presented work on sparse regression methods for system identification at the Society for Industrial and Applied Mathematics conferences.

Discovering Governing Equations from Data using Sparse Regression

Published: August 01, 2021

Presented work on sparse regression methods for discovering ordinary differential equations from time-series data at the American Statistical Association conferences.

Leveraging Physics-Informed Neural Networks for 4D-Variational Data Assimilation

Published: January 01, 2025

Presented research on physics-informed neural networks for 4D-Var data assimilation in marine ecosystem modeling.

Building a Physics-Informed Neural Network for NPZ Modeling

Published: March 01, 2025

Workshop presentation on developing physics-informed neural networks for NPZ ecosystem dynamics.

Elementary Summer School Math Teacher

Instructor, Susan H. Hagen Center, 2014

Taught mathematics to elementary students in summer enrichment program.

Elementary School Teaching Assistant

Teaching Assistant, Wittenberg University, 2015

Assisted with elementary school mathematics instruction.

Math Workshop Tutor

Tutoring, Wittenberg University, 2016

Provided individual and group tutoring support for undergraduate mathematics courses.

Language and Culture Assistant

Teaching, IES Angel Correa, Madrid, Spain, 2016

Assisted with English language instruction and cultural exchange programs.