I’m Blake Moya, a statistician and open-source developer.
I’m fascinated by the elegant ways that mathematics can illuminate complex systems, primarily in hidden Markov ranodm fields of partitions and statistical analysis in function neuroimaging. My work sits at the intersection of statistical modeling, high-performance computing, and open-source software development.
My Journey
I’ve been writing code since high school, where I discovered that computer programming balanced the analytical pursuit of puzzle solving with the creative process of ideation and realization. At The University of Texas at Dallas, I earned a B.S. in Cognitive Science — a blend of my interests in neuroscience and computation. I worked concurrently on an M.S. in Applied Cognition and Neuroscience, researching the effects of chronic drug use on neural connectivity, the similarities and differences between human and machine facial recognition, and the process by which machines can encode human voices.
During this time, while interning at the Center for BrainHealth, I learned R while to produce visualzations for a study I coauthored linking cannabis use disorder to changes in the dynamic functional connectivity of the brain. What I had picked up as a practical tool for plotting results (because Python wasn’t on the BrainHealth computers) quickly became my favorite language for statistical thinking.
My growing interest in statistical theory led me to pursue a Ph.D. in Statistics at UT Austin, where I dove deep into Bayesian modeling, nonparametric inference, and developing new methods for understanding spatially structured data — especially in the context of neuroimaging.
What I Do
I’m passionate about turning complex statistical ideas into usable, performant software. I’ve released open-source tools like CopRe, an R package for martingale-based posterior inference, and contributed custom extensions to popular forecasting libraries like prophet, allowing new probabilistic models for time-series forecasting.
My work spans:
- Bayesian modeling and inference
- Local functional clustering and hidden Markov random fields
- Parallel computing and performance optimization
- Statistical software engineering and package development
- Visualization and interactive dashboards for communication
I love designing tools that balance theoretical rigor with practical usability, empowering practitioners to do more insightful work.
Academia and Industry
Beyond academia, I’ve worked with industry teams at Anaconda and Customer Marketing Group, translating advanced statistical techniques into production tools for forecasting, analytics, and business decision support. I enjoy the challenge of building solutions that both stand up to mathematical scrutiny and deliver real-world value.
Beyond Code
When I’m not coding or researching, you’ll often find me in my garden, trying out new recipes with my wife, or exploring creative projects that bridge art and technology.
Let’s Connect!
I’m always open to connecting with people interested in statistical modeling, open-source development, or innovative applications of data science. Feel free to check out my work on GitHub, connect on LinkedIn, or drop me an email.
Thanks for visiting my corner of the internet!
