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Explore the mathematical foundations of complex streamed data analysis in this 50-minute conference talk that challenges conventional approaches to multimodal data processing. Discover why traditional models fail when they ignore the critical order of events in data streams and learn how Rough Path Theory provides a revolutionary framework for capturing essential timing and relationship information. Through practical examples, including scenarios like commuter transportation patterns, understand how this mathematical approach preserves event sequences without requiring dense sampling, significantly reduces data dimensionality and training set requirements, and enables more accurate data stream descriptions. Gain insights into real-world applications with massive impact across data science, machine learning, and mathematical modeling, while learning how this framework addresses the fundamental limitations of current models that treat multimodal streamed data the same as unimodal data.
