Cedric Clyburn
Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software developer with a background in Kubernetes, DevOps, and container tools. Focused on open-source software, he both contributes (e.g., Podman, vLLM) and enjoys speaking, with prior experience at Devoxx, WeAreDevelopers, The Linux Foundation, and more. Cedric also spends (too much) time creating video and written content helping developers learn new topics in emerging technologies, with over 2M+ views online. He’s based in New York City and is an organizer of the local Kubernetes Community Day.
Session
Residential energy models like NREL’s ResStock generate the kind of data most humans run from: thousands of buildings, dozens of columns, and at least 8,760 rows per column. Great for research, but difficult for anyone who just wants to ask, “What happens to electricity demand in Texas if homes used solar water heating?” or “How do HVAC upgrades change my annual cooling costs in North Carolina?”
Join us for this session as a University of Texas energy researcher and a Red Hat engineer team up to see what large language models can realistically do with this kind of messy, domain-heavy data using Python. We’ll show how we sample, reshape, and describe large datasets so LLMs can help generate and refine pandas/DuckDB queries, explain upgrade scenarios in plain English, and guide non-experts through “what if” electrification questions. This and more, all while being honest about where the models break down and why humans still need to do the science.