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James Howard (Johns Hopkins University)


Decoding Time's Mysteries for Better Predictions


In our data-driven world, making sense of complex information is paramount. Data comes in various shapes and forms, from healthcare to finance, but perhaps none as intricate as time-series data. How can we unravel the underlying stories that this sort of data tells us? In this talk, we will journey through the cross-disciplinary avenues of harmonic analysis, survival models, and machine learning to answer this question. By taking a closer look at signature methods and rough paths, we will explore how mathematics not only dissects the intricacies of time-series data but also enhances our understanding of stochastic processes. These advancements have unprecedented applications in predicting vital healthcare outcomes and beyond. Drawing upon the latest research, including my own, I will illustrate how this mathematical framework provides a robust and efficient way to revolutionize predictive models. The aim is to bring together insights from various fields to show how an enriched mathematical understanding can lead to practical, real-world applications that can potentially save lives.