Akane Sano

WEBSITE(S)| Personal Website | Computational Wellbeing Group

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 Scalable Health Labs.

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. Her research targets (1) the analysis and modeling of human ambulatory multimodal time series data including physiological, biological and behavioral data and surveys 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 for health, wellbeing, and performance. 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 sensors, devices in daily life settings, and clinical assessment especially for measuring, predicting, and intervening/improving clinical outcomes, stress, mental health, sleep, and performance. 

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 Corporation focusing on wearable computing, intelligent systems, and human/computer interaction.

Research Areas

Dr. Sano's research focuses on machine learning, digital health, mobile health, affective computing, ubiquitous computing, behavioral science, adaptive systems, artificial intelligence, human/computer interaction, intelligent systems, biomedical health informatics, digital phenotyping, just-in-time interventions, personalized medicine


2015 Ph.D. Massachusetts Institute of Technology

2005 M.Eng. Keio University

2003 B.Eng. Keio University

Honors & Awards

2022 Rice Young Faculty Research Award

2021 NIH Research Career Development Award (K25)

2021 NSF Career Award

2021 The Best of IEEE Transactions on Affective Computing 2021

2020 Sony Faculty Innovation Award

2020 Microsoft Pandemic Preparedness Award Winner

2019 Microsoft Productivity Research Collaboration Winner

2019 Rice University Institute of Biosciences and Bioengineering: Hamill Innovation Awards

2019 IEEE-EMBS International Conference on Biomedical and Health Informatics (IEEE BHI’19) Best Paper Award

2017 NIH mHTI Scholarship

2016 NIPS machine learning for health Best Paper Award

2014 AAAI Spring Symposium Best Presentation Award

2013 MIT Global Fellowship


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