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dwhite25/README.md

Computational Physicist | Scientific Machine Learning | Signal Processing & Statistical Inference

PhD candidate in Computational and Data Sciences at Chapman University, graduating December 2026. My research focuses on extracting information from noisy physical measurements using statistical estimation, Fourier/signal-processing methods, simulation, and machine learning. Previous research includes gravitational-wave astrophysics and generative modeling for LIGO-related numerical simulations.

Current research: sub-resolution sensing and parameter identifiability, statistical estimation, Fisher information, signal processing. Previous research: gravitational-wave astrophysics, scientific data pipelines, generative neural networks. Interested in: scientific ML, space/planetary science, remote sensing, astrophysics, signal processing.

CV/Resume:

LinkedIn: https://www.linkedin.com/in/derek-white-1443a763

Publications: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.110.L061502

Email: derwhite@chapman.edu derekdwhite25@gmail.com

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  1. MSG-CcGAN MSG-CcGAN Public

    Scientific ML research implementation of a multi-scale continuously conditioned GAN for binary-neutron-star gravitational-wave modeling.

    Jupyter Notebook

  2. Central_Moments_for_Scatter_Characterization Central_Moments_for_Scatter_Characterization Public

    Code for the paper "A Moment-Based Framework for Sub-Resolution Radar Scatterer Characterization"

    Jupyter Notebook

  3. Reconstructing-Superoscillations Reconstructing-Superoscillations Public

    Code and experimental data for reconstructing noise-buried superoscillations and demonstrating sub-bandwidth range resolution

    Jupyter Notebook 1

  4. h5_conversions h5_conversions Public

    Contains programs for converting gravitational wave simulations to the .h5 format for use in LALsuite/PyCBC for research related to LIGO. Also contains programs for reporting on existing simulations.

    Jupyter Notebook