Dennis J. Carroll
Interactive ML Tools • Bayesian Analytics • Creative Fiction
I build interactive tools that make complex ideas intuitive. Over the past 5 years, I've created 24+ standalone web applications spanning neural network visualizations, Bayesian analytics dashboards, real-time ML training environments, and mathematical exploration tools — all designed to run in the browser.
My current focus is at the intersection of deep learning and interpretability: understanding not just what neural networks learn, but how and why they learn it. Recent projects include an Agent Trace Viewer for SWE-agent interaction analysis and a Mechanistic Interpretability visualizer for transformer architectures.
I'm also the author of three original fictional universes — including Crack in the Veil, a post-humanity sci-fi saga, and A Chronicle of Lyos, a fantasy world where dead gods' bloodlines still remember.
By the Numbers
Skills & Technologies
Programming Languages
Machine Learning & Deep Learning
Data Science & Analytics
Web Development
Tools & Infrastructure
Experience
Independent Software Developer & Data Scientist
Self-Directed- ▸Built 24+ browser-based interactive applications — neural network visualizers, Bayesian analytics dashboards, mechanistic interpretability tools, generative audio/visual systems — all running in the browser with no install required.
- ▸Designed and implemented ML pipelines in Python using TensorFlow, PyTorch, Scikit-learn, and PyMC; deployed interactive frontends with React, Gatsby, Three.js, and TensorFlow.js.
- ▸Authored research-level interpretability tooling for transformer model analysis, including an attention-head visualizer and a structured annotation system for MLP blocks.
- ▸Wrote and self-published three original fictional universes — a post-humanity sci-fi saga, a space opera, and a secondary-world fantasy — developed in parallel with technical work.
Doorman / Building Security
Residential Building- ▸Primary point of contact and access control for a high-occupancy residential building — trusted with building security, resident communication, and emergency response.
- ▸Managed relationships with residents, management, vendors, and emergency services; developed strong judgment under pressure in a high-visibility, low-error-tolerance role.
- ▸Applied communications degree background daily: de-escalation, conflict resolution, clear documentation, and coordination with building staff.
Construction & Site Work
Various Projects- ▸Performed skilled labor across multiple construction sites — developed strong work ethic, spatial reasoning, and understanding of project sequencing under tight deadlines.
- ▸Collaborated with crews on-site, reading plans and coordinating tasks; skills in logistics and structured problem-solving carried directly into software project work.
My Journey
The Mission
With expertise in Python programming and data analysis, I enjoy tackling complex problems and turning data into actionable insights. My goal is to bridge the gap between technical complexity and practical solutions.
The Passion
When I'm not coding, you might find me writing creative fiction or exploring new technological frontiers. I believe that creativity and technical skills complement each other beautifully.
Data Science & Deep Learning
My fascination with data science stems from its power to uncover hidden patterns and transform raw information into actionable insights. I'm particularly drawn to the intersection of statistical rigor and computational innovation—where Bayesian inference meets modern machine learning architectures.
Deep learning captivates me because of its ability to learn representations directly from data. From neural network theory to practical implementations with TensorFlow and PyTorch, I enjoy exploring how these systems learn, adapt, and sometimes surprise us. Whether it's building interactive visualizations to understand training dynamics or implementing novel architectures, I find the blend of mathematics, intuition, and engineering deeply rewarding.
Currently, I'm exploring areas like reinforcement learning, graph neural networks, and the emerging field of mechanistic interpretability—understanding not just what neural networks learn, but how and why they learn it.
Currently Exploring
Mechanistic Interpretability
Understanding how transformer attention heads and MLP blocks represent concepts
See experiment →Graph Neural Networks
Extending neural architectures to non-Euclidean graph-structured data
Reinforcement Learning Theory
Connecting RL optimization to dynamical systems and chaos theory
See experiment →About This Website
This website serves as a portfolio of my work in data science and project development, as well as a platform for sharing my creative writing. Built with modern web technologies, it showcases both my technical abilities and my creative interests.