Jae Kyoung Kim

Bionote:

Jae Kyoung Kim is an associate professor in Dept. of Mathematical Sciences, KAIST and Chief Investigator of Biomedical Mathematics Group, IBS. He got his Ph. D. in Mathematics at the University of Michigan and was a postdoctoral fellow in the Mathematical Biosciences Institute at the Ohio State University. He has combined nonlinear dynamics, the theory of stochastic processes, and scientific computing to solve critical biological and medical problems, including sleep disorders. In particular, his math models have been used for the development of a new drug and digital medicine for sleep disorders.  He is a recipient of Human Frontier Science Program Young Investigator Award, Young Researcher Award from Korea SIAM, Sangsan Young Mathematician Award from Korea Math Society and 30 Young Scientists of Korea Award. He is the editor of Mathematical Biosciences, NPG Systems Biology, Current Opinions in Systems Biology, J of Biological Rhythms, and PLOS One.

Math + AI + Wearables for Diagnosing and Treating Sleep and Circadian Rhythms Disorders

Abstract

During this presentation, I will showcase the collaborative efforts between our mathematics group and medical researchers in diagnosing and treating disrupted circadian rhythms and sleep-related issues. To begin, I'll delve into the fundamental molecular mechanisms that underpin robust circadian rhythms in the face of spatio-temporal disturbances in the cell by using a combination of mathematical modeling and experiments. This analysis will shed light on how unstable circadian rhythms and sleep-wake cycles can be disrupted by cytoplasmic traffic jams associated with conditions like Alzheimer's disease, obesity, and aging. These insights offer a fresh perspective on the treatment of sleep disorders. Moving forward, I'll introduce our collaboration with the Samsung Medical Center. In this joint effort, we employed mathematical modeling and machine learning techniques to dissect the intricate sleep patterns of shift workers and individuals with mood disorders, as measured by wearable devices. This innovative approach allows us to identify personalized sleep-wake patterns that effectively minimize daytime sleepiness and mood disorders. This allowed us to develop a mobile application that can provide individuals with tailored sleep schedules, optimizing their overall sleep experience.