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DESCRIPTION:Foundation Models for Time-Series and Spatio-Temporal Data\n\nB
 y Flora Salim\n\nProfessor University of New South Wales\n\nDate: Friday\,
  February 13\, 2026\n	Time: 8:00 a.m. to 9:30 a.m.\n	Location: Online\n\nAtt
 end online\n\nView poster\n\n\nAbstract\n\nThis talk explores the foundati
 ons of AI for time-series and multimodal sensor data\, emphasizing the pre
 ssing challenges and frontier solutions for real-world spatio-temporal lea
 rning. Time-series data from sensors in domains such as transport\, energy
 \, and urban systems are often riddled with missing values\, heterogeneity
 \, irregular sampling\, high noise\, and label scarcity. These issues are 
 compounded by modality differences across sensors\, domain shifts\, and dy
 namic environments. We present a comprehensive overview of recent advances
 \, grounded in a series of foundational works. We also introduce a massive
  traffic forecasting\, building IoT time-series\, and human mobility datas
 ets and benchmarks. and pretrained models for generalizable spatio-tempora
 l inference across diverse urban contexts. We ground this discussion in br
 oader trends outlined in a recent comprehensive survey on foundation model
 s for spatio-temporal data science\, which articulates how pretraining\, c
 ross-domain transfer\, and unified architectures are reshaping the field.
 \n\nIn summary\, this talk offers a unified vision of foundational AI for 
 time-series and multimodal sensors\, combining robust temporal modeling\, 
 cross-modal alignment\, and scalable representation learning to unlock new
  capabilities in dynamic\, real-world environments.\n
DTSTART:20260213T130000Z
DTEND:20260213T143000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Flora Salim
URL:/desautels/channels/event/mcche-precision-converge
 nce-webinar-series-flora-salim-371165
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