Ryan Young, Ph.D.

Scientist,

Engineer

(Site under development 🚧)

01

About
me

Ryan Young — Senior ML Engineer

I build neural networks for GPS and EM signal protection — detecting what signals are and where they come from. My work spans the stack from model development (Python, Julia) to deployment on edge devices via C and hardware acceleration (GPU, NPU).

My foundation is computational neuroscience: I earned my Ph.D. at Brandeis University studying how the brain encodes memory and navigation. That training in machine learning, statistics, and signal processing now powers my engineering work.

Current Work

Neural networks for EM signal classification and geolocation
Edge deployment (Python to C, hardware acceleration)
Open-source neuroscience tools
Open-source memory tools

Background

Ph.D. Computational Neuroscience — Brandeis University
Large-scale neural data analysis
Signal processing, probabilistic programming, sequence models

Open to collaboration — get in touch.

02

My
Skills

Soft Skills
	Communication
	Teamwork
	Public Speaking
	Technical Writing
	Teaching / Mentorship

Data Science Machine Learning
	Unstructured Data
	Bayesian Statistics
	Exploratory Data Analysis
	Markov Decision Processes
	Time-series Analysis
	Probabilistic Programming
	Linear & Tensor Models

AI
	Deep Neural Networks
	LLM Fine-tuning & RAG
	Reservoir Networks
	Autoencoders
	Convolutional NNs
	Reinforcement Learning

Programming
	Python, Julia, C++/x86 ASM, R, Shell
	CI/CD, Git
	Cloud, Secure Shell (SSH), Apache Tools
	PyTorch, TensorFlow, JAX
	Vim, Emacs
	DSpy, Langchain, LlamaIndex

Software, Cloud, API
	Adobe Illustrator
	Adobe Photoshop
	Adobe After Effects
	Eagle PCB Design
	AWS, Google Cloud, Docker
	RESTful APIs

03

My
Experience

2024 - Present

Senior ML Engineer

Mayflower Communications

Building neural networks for GPS and EM signal protection — detecting what signals are and where they come from.
ML Development: Model development in Python and Julia for signal classification and geolocation.
Edge Deployment: Deploying models to edge devices via C with hardware acceleration (GPU, NPU).
2023 - 2024

ML Engineer

Stealth Startup (Healthcare/Mental Health)

Part-time consulting role building AI solutions for elderly care.
LLM Systems: Architected multi-agent LLM network using DSPy for healthcare optimization.
Voice AI: Deployed Streamlit app with real-time Whisper transcription to AWS EC2.
Data Engineering: Built pipelines for LLM fine-tuning; NLP/NER for knowledge graph construction.
2016 - 2023

Ph.D. Researcher

Brandeis University

Research Focus:
 Communication Subspaces in the Hippocampal-Prefrontal Circuit.
 Non-local brain representations for memory-guided decision-making.
Specialized in advanced time-series analysis, spectral methods, Bayesian decoding, manifold learning, and causal detection techniques.
Leadership: Mentored personnel in hypothesis development and testing using programming and mathematical models.
Data Engineering: Managed 400TB server infrastructure, optimized data storage and retrieval using AWS S3, Apache Arrow, Parquet, and SQL.
Programming: Developed Julia-based data architecture and analysis tools. Contributed to open-source projects in Julia and Python.
2014 - 2016

Research Assistant

Brandeis University

Developed C++-based software for real-time phase disruption in neural activities.
Engaged in equipment setup for neural data collection and designed 3D printed components for experimental research.
2012 - 2014

Research Assistant

University of Houston, Rice University

Sheth Lab: Conducted literature synthesis to support novel theories in visual processing.
Kemere Lab: Investigated immune reactions to carbon nanotubes in biological tissues.