Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering and Computer Science.
She directs Computational Wellbeing Group. 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 (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 Corporation and worked on wearable computing, intelligent systems and human computer interaction.
Please check admission websites at Rice ECE and CS
depending on your background and mention my name and your interest in your research statement!
[March, 2019] 2019 Society of Affective Science annual conference
for Method lunch session "Mobile and ubiquitous emotion sensing"
[Dec, 2018] MD2K webinar on Dec 6 "Human Sensing and Data analysis/modeling for Health, Wellbeing and Performance"
[Nov, 2018] NSF Press Release NSF announces awards to shape the human-technology partnership for the well-being of workers and their productivity
Rice Press Release Enhancing cognitive abilities for healthier work
Nature News Article about our research Happy with a 20% chance of sadness
[Oct, 2018] Rice Data Science Conference
[Sept, 2018] New paper: Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks
was published in IEEE Journal of Biomedical Health Informatics (IEEE JBHI).
Teaching "Human sensing, analysis and applications" this semester.
Very excited to receive a NSF grant "Future of Work at the Human-Technology Frontier: Advancing Cognitive and Physycal Capabilities".
We develop "an Embodied Intelligent Cognitive Assistant to Enhance Cognitive Performance of Shift Workers"
and people with social jetlag as well as their wellbeing.
This is a 3-year collaborative project with UMass Amherst, Cornell University, Harvard Medical School, Baylor College of Medicine and Microsoft Research.
Co-organizing Workshops: Modeling Cognitive Processes from Multimodal Data
at ICMI 2018
in Denver and Mental Health: Sensing & Intervention
at Ubicomp 2018 in Singapore
[July, 2018] Presentation at IEEE EMBC 2018 Minisymposia "Sensor-based behavioral informatics: advances in understanding of human behavior"in Hawaii.
[June, 2018] Presentation 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.
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