Well met! I'm Lauren Shriver, a computational science researcher focused on oscillatory systems, neuroscience, and using the scientific method & first-principles reasoning to construct hypotheses that can be tested in silico. I am passionate about translating mathematical structure and physical axioms into executable simulations that are not only reproducible, but also interpretable. I invite you to explore my projects below to see how I approach scientific modeling and computational problem-solving.
Main Projects
Kuramoto Benchmark Suite
A modular Python framework for simulating Kuramoto oscillator systems, designed to study synchronization, phase transitions, and emergent collective behavior. Includes spectral and order-parameter analysis tools for quantifying coherence and exploring network topology.
Kuramoto BenchmarkPIML Projects
A collection of physics-informed machine learning experiments for Scriber Labs, combining my personal expertise with AI-assisted development. Focused on building physically interpretable models of dynamical systems while enforcing known physical constraints during training.
Harmonic OscillatorResearch Notebook for Scriber Labs
A structured research environment for documenting scientific reasoning, model development, and experimental results across Scriber Labs projects. Designed to provide a clear, organized, and reproducible record of methodologies and findings.
Research NotebookMy Zettelkasten
A long-term knowledge management system built on first-principles reasoning. It supports a range of scientific domains and is designed to facilitate the organization and retrieval of interdisciplinary foundations. Rather than a finished document, this project serves as an evolving educational resource that exercises understanding and promotes the synthesis of ideas into research-ready frameworks.
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