I work at the meeting point of classical rumen nutrition and modern machine perception - building better ways to measure, and reduce, the methane that livestock release.
Trained as a ruminant nutritionist, I study enteric methane and rumen fermentation through in vitro batch and continuous-culture systems, gas chromatography, and a wet-lab toolkit spanning fiber, protein, and volatile-fatty-acid analysis. My doctoral work at Southern Illinois University examined how bromoform and other feed additives reshape methane production and fermentation in the rumen.
From the rumen to the camera - measurement is where better livestock decisions begin.
More recently I've paired that biology with computer vision, co-developing deep-learning models - Gasformer, GasTwinFormer, and the CarboNeXT family - that segment and quantify methane and CO₂ straight from optical-gas-imaging cameras, turning thermal video into emissions data. I'm a co-investigator on USDA-NIFA projects bringing AI to greener livestock and to early detection of subacute ruminal acidosis.
Today I'm a postdoctoral fellow at the CLEAR Center at UC Davis, focused on clear, science-based understanding of animal agriculture and climate. My path here ran through Egypt, Greece, France, and the United States - including earlier careers in official statistics and geospatial analysis that still shape how I work with data.
PhD Agricultural Sciences
Fulbright Scholar
CLEAR Center · UC Davis
25+ publications
CVPR · ICCV papers