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brianjmalone/README.md

Neural Networking in vivo and in silico

I'm interested in intelligent systems, representation learning, and building ML systems that bridge rigorous science and real-world applications.

💼 Open to ML/AI Engineering and Data Science opportunities. Excited about hard, impactful problems.

Here, I'm documenting what I'm learning about building scalable, production-grade systems, talking about frameworks and tooling, and archiving Lessons Learned to help people on similar paths with my "Gisthub".

What I'm working on:

  • Production-grade ML pipelines for complex, high-dimensional data
  • Deep learning applications in auditory/neural signal processing
  • Statistical inference frameworks and experimental design automation

Tech stack: Python (PyTorch, pandas, scikit-learn) • SQL • Git • Docker

Background: Systems and computational neuroscience (Ph.D. in Neural Science @NYU) • 20+ years of research experience (@UCLA, UCSF, UC Davis) • Google Scholar

Connect: LinkedIn | Email

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  1. mac-mini-m4-remote-compute-setup mac-mini-m4-remote-compute-setup Public

    Demonstrates a local distributed compute architecture using Apple Silicon as a remote Jupyter kernel, coordinated over ethernet from a primary development machine.

    Shell

  2. vibe-coded-terminal-based-markdown-viewer vibe-coded-terminal-based-markdown-viewer Public

    Terminal-based Markdown viewer with syntax highlighting and pagination built with a paragraph of standard English text and a YouTube link.

    Python

  3. deep-speech-models-predict-auditory-cortical-activity deep-speech-models-predict-auditory-cortical-activity Public

    ANNs trained on speech audio predict auditory cortex spiking at fine timescales — replication code for Ahmed et al. (2025)

    Jupyter Notebook

  4. ripit-pattern-search-simplified ripit-pattern-search-simplified Public

    A readable DSL for ripgrep — write intuitive patterns, get fast regex code search

    Python

  5. technical_writing technical_writing Public

    Technical guides on Python, AI/ML, and developer tooling

  6. ml-deployment-chassis ml-deployment-chassis Public

    Production ML deployment: FastAPI + Docker + Streamlit, live on Render + Streamlit Cloud

    Python