Photo of Sano

WEBSITE(S)| Personal Website | Computational Wellbeing Group | Research Areas: Data Science

Education: 

2003 B.Eng. Keio University
2005 M.Eng. Keio University
2015 Ph.D. Massachusetts Institute of Technology


Research Summary: 

Dr. Sano's research focuses on mobile health and affective computing. She has been working on measuring and understanding stress, sleep, mood and performance from ambulatory human long-term data and designing intervention systems to help people be aware of their behaviors and improve their health conditions.


Bio: 

Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering and Computer Science.


Her research focuses on human sensing, data analysis and application development for health and wellbeing. She directs the Computational Wellbeing Group and is a member of Scalable Health Labs.


She has been working on developing technologies to measure, forecast, understand and improve health and wellbeing. She has worked on measuring and predicting stress, mental health, sleep and performance and designing systems to help people to reduce their stress and improve their mental health, sleep and performance for student and employee populations including SNAPSHOT study project, Eureka project (symptom prediction and digital phenotyping in schizopherenia using phone data) and IARPA mPerf project (Using mobile sensors to support productivity and employee well-being).


She obtained her PhD at MIT Media Lab, and her MEng and BEng at Keio University, Japan. Before she joined Rice University, she was a Research Scientist in Affective Computing Group at MIT Media Lab, and a visiting scientist/lecturer at People-Aware Computing Lab, Cornell University. Before she came to the US, she was a researcher/engineer at Sony Coproration on wearable computing, intelligent systems, and human computer interaction.


Selected Awards and Honors: 

2017 NIH mHTI Scholarship
2016 NIPS machine learning for health Best Paper Award
2014 AAAI Spring Symposium Best Presentation Award
2013 MIT Global Fellowship


Selected Research: 

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