Speaker
Description
With recent advances in experimental tools and wearable devices, it has become increasingly easy to collect large volumes of time-series data. In this talk, I will demonstrate how such data can be utilized to uncover the hidden structures within complex systems. First, I will introduce GOBI (General Model-based Inference), a simple yet scalable method designed for inferring regulatory networks from time-series data. GOBI can infer both gene regulatory and ecological networks, surpassing the capabilities of traditional causality detection methods such as Granger and CCM. Next, I will present Density-PINN (Physics-Informed Neural Network), a method that infers the shape of the time-delay distribution governing interactions within a network. This inferred distribution helps identify the number of pathways responsible for signaling responses to antibiotics, addressing a long-standing question about the primary sources of cell-to-cell heterogeneity under stress. Finally, I will explore how the combination of mathematical modeling and machine learning can be used to analyze big data of sleep-wake timesereis measured with smartwatches. This allowed us to develop personalized sleep-wake schedules that help mitigate daytime sleepiness and reduce the risk of depression. I will also touch on how these findings can be translated into a practical app for broader use.