Reimagining Time Series Foundation Models: Metadata and State-Space Model Perspectives
Type
Publication
NeurIPS Workshop on Time Series in the Age of Large Models
Ozan Baris Mulayim
ML Engineer
Ozan Baris was a PhD student at Carnegie Mellon University in Advanced Infrastructure Systems. He worked on supply-demand flexibility in electricity consumption using Thermostatically Controlled Loads with the help of ML algorithms.
Mario Bergés
Professor of Civil and Environmental Engineering
My research interests vary, but generally gravitate towards the development of technologies to make our built enviornment and the communities in them more autonomous and efficient. Lately I am interested in developing responsible autonomous solutions for infrastructure systems.