About

I am a machine learning leader, researcher, and developer with over 20 years of experience in in the industry. I currently work in enterprise consulting with a focus on large language models and ML system deployment.

Background

Throughout my career, I've bridged the gap between ML research and production systems. I have developed scalable machine learning models that serve millions of end users across applications including machine translation, speech recognition, and natural language understanding.

My research focuses on natural language understanding, reasoning, and inference using deep learning and statistical methods. I'm particularly interested in how neural networks learn language representations, model evaluation methodologies, and the fundamental challenges of building systems that process human language effectively.

I studied philosophy, computational linguistics, and completed my PhD in Language Technology.

Current Work

I lead initiatives in large language models and ML system architecture, helping organizations build and deploy machine learning solutions that deliver measurable business impact. My work spans from model development and optimization to strategic consulting on ML adoption and infrastructure.

This Blog

I write about three areas that intersect in my work: machine learning, technology, and philosophy. Some posts dive deep into technical details; others explore broader connections.

Machine Learning: Model architectures, training methodologies, inference optimization, evaluation frameworks, and what neural networks actually learn. I'm particularly interested in language models and the mechanics of how they process and generate text.

Technology: Side projects and experiments that catch my attention. The tools I build for fun, weekend hacks that solve interesting problems, and personal projects that make me think differently about what's possible with code.

Philosophy: Philosophy of mind, language, science, and logic. Exploring questions about meaning, knowledge representation, understanding, and what it means for machines to "know" something.

I also share notes from various technical projects. For example, this blog runs on a lightweight static generator I built because I prefer simple, controllable tools for ML workflows. If any of that resonates, welcome.