Machine learning engineer · researcher · builder

I build machine learning products end to end.

I turn raw, complex data into reliable models and production systems, and I'm increasingly applying that foundation to modern AI.

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About

Theory and practice have always shared a workspace.

I'm a machine learning engineer, researcher, and lifelong builder. I worked full-time throughout my bachelor's, master's, and Ph.D. programs, so education and engineering were never separate tracks. Outside of work, I'm a father of two, an enthusiastic gamer, and a homelabber who enjoys building the infrastructure behind the things my family and I use.

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Selected work

Evidence across research, systems, and teaching.

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iCHARM interactive globe showing global surface temperatures and atmospheric streamlines

Active contribution · 2012–present

Climate Research to iCHARM

A long-running climate-data story spanning computational statistics, distributed processing, browser visualization, and a living institutional platform.

Title slide for the BDA-602 Machine Learning Engineering course taught by Dr. Julien Pierret

Published teaching material · 2019–present

Making ML Engineering Practical

An end-to-end machine learning engineering curriculum connecting source control, testing, data systems, modeling, APIs, and containers.

Now

Applied AI, local systems, and tools that make repeated work safer.

I'm exploring LLM workflows and agents through the lens of production ML: evidence, evaluation, observability, and useful boundaries.

At home, I keep evolving a NixOS and Kubernetes-based homelab that supports household services, game streaming, emulation, and hands-on infrastructure work.

Recent writing