APRIL 25, 2025
Akane Sano, PhD
Associate Professor
Computational Wellbeing Group
Rice University Department of Electrical and Computer Engineering
About the Presentation: Sensors and computing systems facilitate non-disruptive monitoring of human daily life behaviors and responses and enable real-time interventions. Combining diverse and multimodal measurements, such as clinical and remote sensing data, has demonstrated potential in predicting and managing mental health. However, challenges related to data collection, modeling, feedback, and deployment still remain.
In this talk, I will address these challenges and showcase progress and future directions for measuring, predicting, and supporting mental health and wellbeing. I will introduce studies to characterize physiology and behaviors across diverse populations, including college students, shift workers, and patients with opioid use disorders. Additionally, I will highlight the development of robust and fair inference models using unlabeled and multimodal data, the potential of leveraging social graph networks, and the advancement of adaptive, diverse sensing, and interpretable feedback systems.
About the Presenter: Akane Sano is an Associate 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 focuses 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 assessments. She received her Ph.D. at the Massachusetts Institute of Technology and her M.Eng. and B.Eng. 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.