I am a Research Engineer based in Japan, working at the intersection of statistical modeling, machine learning, and software development. I hold a PhD in Mathematical Informatics from the Nara Institute of Science and Technology (NAIST).

Previously, I was an Assistant Professor of Statistics at the University of the Philippines, Diliman.

• Research Engineering: Developing and optimizing advanced vision models, 3D modeling pipelines, and deep learning architectures.
• Bayesian Inference: Exploring Variational Inference, Bayesian Deep Learning, and robust statistical algorithms.
• Open Source: Bringing robust statistical computing to the JavaScript/Node.js ecosystem.

I am the author and maintainer of @dominicdayta/nodestat, a lightweight statistical computing library built for Node.js. It bridges the gap for developers who need R-like statistical capabilities natively in JavaScript.

Key Features:

• 🎲 Random Number Generation: High-quality pseudorandom sampling across various probability distributions.
• 📉 Linear Modeling (lm): Native ordinary least squares regression analysis inside Node.
• 🧪 Hypothesis Testing: Robust implementation of classical statistical tests complete with multiple testing corrections (family-wise error rate and FDR control).

npm i @dominicdayta/nodestat

• Languages: Python, R, JavaScript / TypeScript, C++
• Frameworks: PyTorch, Node.js, Express
• Specializations: Computer Vision, 3D Graphics/Modeling, Variational Inference, Statistical Consulting

• 📝 Writing: Beyond technical papers, I write fiction and satire—some of my short stories have found homes in literary journals.
• 🎸 Music & Tech: When I'm not coding, I'm probably tuning my guitar to Drop D, building Small Form Factor (SFF) PCs, or overanalyzing a chess puzzle on a physical board.

• Website: dominicdayta.com
• Email: contact@dominicdayta.com