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maddiepr/README.md

Madeline (Maddie) Preston

Hi, I'm Maddie Preston - a computational scientist and applied mathematican who loves turning mathematical models into real, working code. I specialize in high-performance simulations, numerical solvers, and backend tools for scientific and engineering problems.

Right now, I'm focused on:

  • Simulating PDEs with Monte Carlo methods
  • Writing efficient solvers in Python, C++, and Fortran
  • Building reproducible workflows with Docker and SQL
  • Exploring backend APIs and data systems for simulation output

My work spans scientific computing, machine learning, and infrastructure. I'm always looking for ways to bridge theory and practice clean, scalable code.

Featured Projects

Check out my GitHub Portfolio for detailed writeups and code. Highlights include:

  • Monte Carlo PDE Solver (Python + Fortran + C++): Domain-mapped framework for non-conservative PDEs
  • CNN + Transfer Learning (PyTorch): Custom vision model with ResNet fine-tuning
  • C++ Heat Equation Solver: Finite difference simulation with CLI interface
  • Simulation Database & Querying (SQL + Python): Store and analyze simulation results
  • Java Grid Simulation: Object-oriented simulation of particle movement
  • Dockerized Simulation API: Containerized Python backend with REST interface

Contact

📫 LinkedIn • Email • Resume

Thank you for visiting!

Popular repositories Loading

  1. 1d-pde-simulation-sandbox 1d-pde-simulation-sandbox Public

    Research sandbox for testing MC solutions 1D divergence-dependent PDEs

    Python 1

  2. pytorch-mnist-resnet pytorch-mnist-resnet Public

    PyTorch baselines: MNIST CNN + ResNet transfer learning (reproducible).

    Python 1

  3. maddie-preston-portfolio maddie-preston-portfolio Public

    Portfolio of simulation, scientific computing, machine learning, and backend projects using Python, C++, SQL, Java, and Docker.

    1

  4. domain-mapping-monte-carlo-public domain-mapping-monte-carlo-public Public

    Redacted structure of a Monte Carlo simulation framework for PDEs in conformally mapped domains. Core methods removed for IP protection.

    C++ 1

  5. pde-surrogate-modeling pde-surrogate-modeling Public

    Uses finite differences to solve nonlinear PDEs and trains a neural network to emulate the solution behavior.

    Python 1

  6. maddiepr maddiepr Public

    Simulation & Scientific Computing | PDEs, HPC, Monte Carlo Methods