Abstract

Abstract: Imagine 24/7 rich human multimodal data could identify changes in physiology and behavior, and provide personalized early warnings to help you, patients, or clinicians for making better decisions or behavioral changes to support health and wellbeing. I will introduce a series of studies, algorithms, and systems we have developed for measuring, predicting, and supporting personalized health and wellbeing for clinical populations as well as people at increased risk of adverse events, including ongoing COVID -19 related projects. I will also discuss challenges, learned lessons, and potential future directions in digital health and wellbeing research.

Bio: Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering. She directs Computational Wellbeing Group. She is also a member of Rice Scalable Health Labs.

Her research focuses on affective, ubiquitous, and wearable computing, and biobehavioral sensing and analysis/modeling. Her research targets (1) the analysis and modeling of human ambulatory multimodal time-series data including physiological, biological, and behavioral data for measuring, predicting, improving, and understanding human physiology and behavior and human factors such as health, wellbeing, and performance and (2) development of human-centered computing technologies to support health and wellbeing.

She obtained her Ph.D. at MIT . Before she came to the US, she was a researcher/engineer at Sony Corporation and worked on affective/wearable computing, intelligent systems, and human-computer interaction.

Her recent awards include Microsoft Productivity Research Award in 2019, the Best Paper Award at IEEE BHI 2019 conference, the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health, and the 2014 AAAI Spring Symposium Best Presentation Award.