Colloquium A

日時(Date) 2022年10月18日(火)2限(11:00--12:30)
Tue. Oct. 18th, 2022, 2nd period (11:00--12:30)
場所(Location) face-to-face (L3)
司会(Chair) 安本
講演者(Presenter) Akane Sano (Rice University)
題目(Title) Multimodal Machine Learning and Human Centered Computing for Health and Wellbeing
概要(Abstract) 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 make 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.
講演言語(Language) English
講演者紹介(Introduction of Lecturer) Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering. She directs the Computational Wellbeing Group and is a member of Rice Digital Health Initiative. Her research includes data science, machine learning, and human-centered intelligent systems for health and wellbeing and spans in the field of affective computing, ubiquitous and wearable computing, and biobehavioral sensing and analysis/modeling. She has been working on developing tools, algorithms, and systems to measure, forecast, understand and improve health and wellbeing using multimodal data from mobile and wearable devices in daily life settings, and clinical assessment. She received her Ph.D. at the Massachusetts Institute of Technology and her MEng and BEng at Keio University, Japan. Her recent awards include the NSF Career Award, the Best of IEEE Transactions on Affective Computing 2021, the Best Paper Award at IEEE BHI 2019 conference, and the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health.