Akane Sano





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 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 (symtom 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 Corporation.


News/Upcoming conferences/events

[July, 2018] Presentation at IEEE EMBC 2018 Minisymposia "Sensor-based behavioral informatics: advances in understanding of human behavior"in Hawaii.

[June, 2018] Giving a talk at Gordon Research Seminar: Advanced Health Informatics, Emerging Perspectives in Health Informatics from Wearable Sensing to Big Data in Hong Kong

[June, 2018] Presentation at NIH 2018 mHealth Technology Showcase

[April, 2018] Our paper about SNAPSHOT study and machine learning models to detect stress and mental health conditions and identify underlying related physiological and modifiable behavioral markers will be published at Journal of Medical Internet Research

[February, 2018] Our paper about N=1 experiment platform was published in Sensors: the Special Issue "QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform"

[January, 2018] Teaching Ubiquitous Computing class this semester at Cornell!

[November, 2017] A paper about a system that enables users to conduct N=1 study (self experimentation) "QuantifyMe: An Automated Single-Case Experimental Design Platform" was presented at MobiHealth 2017.

[October, 2017] Papers about micro-stress intervention delivery timing, stress analysis using toungue images and filling missing data with auto-encoder were presented at ACII 2017.

[August, 2017] I am looking for motivated students, post-docs and collaborators to work with.

Please email me with your CV, interest and reference information if you are interested in any of the following.
(1) Wearable/mobile/ubiquitous computing
(2) Human Computer Interaction/Interface
(3) designing technologies for measuring and improving human behaviors, emotion, performance, health and wellbeing
(4) analyzing human-related data and understanding human behaviors, emotion, sleep and physiology etc.

Graduate admission deadline is December 31, 2017 for Rice ECE and January 1, 2018 for CS.
Please see more details at admission websites, ECE and CS

[July, 2017] Started working for People-Aware Computing Lab, Cornell University. Will be working on mPerf project (Using mobile sensors to support productivity and employee well-being) and other mental health/wellbeing projects!

[June, 2017] Our abstract about weekly sleep regularity at Sleep2017 was press-released. See more about it here "Sleep regularity is important for the happiness and well-being of college students"

[April, 2017] Selected as a mHealth Scholar for the NIH Training Institutes for mHealth Methodologies.

[March, 2017] We will organize the 1st Workshop on Tools and Algorithms for Mental Health and Wellbeing, Pain, and Distress at ACII 2017 in October 2017.
Papers are due June 15, 2017.



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