Ideas become clearer when they are challenged in public.
Selected keynotes, invited sessions, and engineering talks on data quality, AI reliability, large-scale data platforms, and operational trust.
Data Quality in the Age of AI
IEEE NEXOTECH keynote. A practical argument that AI reliability begins with the data architecture, controls, and evidence around the model.
Invited academic and industry sessions
Talks for engineering and research audiences on trustworthy AI, distributed data systems, and the changing role of data quality.
DAMA Georgia
A practitioner-focused session connecting data-quality architecture, operational controls, and AI-era governance.
Measurement readiness for science and engineering
An internal engineering summit talk described here only at a public-safe level: reducing the distance between raw enterprise data and trustworthy model inputs.
Petabyte-scale data engineering and cost discipline
An engineering summit session on operating large analytical workloads with stronger efficiency, ownership, and observability.
More than a project recap.
The most useful technical talks do not simply show architecture. They name the tradeoff the team faced, explain why the obvious answer was insufficient, and give the audience a way to recognize the same problem in their own systems.
That is the standard I use when preparing a keynote, conference session, or engineering review.