Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering. She directs the 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. She received her Ph.D. at the Massachusetts Institute of Technology. Her recent awards include the NSF Career Award, the Best Paper Award at IEEE BHI 2019 conference, and the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health.
Multimodal sensor machine learning for mental health
Digital phenotyping and machine learning technologies have shown a potential to measure objective behavioral and physiological markers, provide risk assessment for people who might have a high risk of poor health and wellbeing, and help 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. I will also discuss challenges, learned lessons, and potential future directions in health and wellbeing research.