Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering.
She directs Computational Wellbeing Group. Her research focuses on human sensing, data analysis and modeling, and intelligent system development for health, wellbeing, and performance.
She is a also member of Rice Scalable Health Labs.
Her research 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 mobile and wearable sensors and devices in daily life settings, especially for measuring, predicting, and intervening/improving stress, mental health, sleep and performance.
Her research projects include NSF future of work project: embodied cognitive assistant for shift workers, NIH funded 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.
2021 Jan: University of Cambridge
2020 Dec: NSF Future of Work PI meeting
2020 Nov: NIH Clinical Informatics and Digital Health panel
2020 Sept: Ubicomp 2020
2020 Aug: Summer School on Sensor-Based Behavioral Machine Learning
2020 Jun: NIH Clinical Informatics and Digital Health panel
2020 Apr: National Institute of Mental Health: Virtual Workshop: Transforming the Practice of Mental Health Care
2020 Feb: CRA Career workshop, DC
2020 Jan: Keynote talk: IEEE CCWC 2020
at University of Nevada, Las Vegas
2019 Dec: Invited talk: Mie University
2019 Dec: Invited talk: American Epilepsy Society Meeting workshop "Wearable Devices: Beyond Seizure Detection"
2019 Nov: Invited talk: Visionary Seminar - PERSONALIZED MEDICINE VISIONARY SEMINAR
in Leuven Mindgate/KU Leuven
2019 Sept: Workshop & Paper: ACII 2019 in UK, Cambridge (ML4AD) & Ubicomp 2019 (mental health workshop) in UK, London
2019 Aug: Invited talk: Technology Collaboration Center: Data Analytics Workshop, Rice University
2019 June: Keynote talk: 19th Brazilian Symposium on Applied Computing Health in Rio, Brazil
2019 May: Invited panel: Deep Learning in Healthcare Summit in Boston
2019 May: Paper: IEEE BHI/BSN in Chicago
2019 Apr: NSF Future of Work PI meeting
2019 Mar: Invited method session: Society of Affective Science in Boston
2019 Feb: Life Sensing Consortium meeting at UT Austin
2020 Aug: Summer School on Sensor-Based Behavioral Machine Learning "Physiological and behavioral data analysis and modeling for health and wellbeing"
2019 June: The 19th Brazilian Symposium on Applied Computing Health
2019 March: Rice University ECE Corporate Affiliates Day "Embodied intelligent assistant to enhance wellbeing and cognitive performance"
2018 Dec: Mobile Data 2 Knowledge (MD2K) webinar "Predicting mental health and mesaruing sleep using machine learning and wearable sensors/mobile phones"
2018 Oct: Rice University Data Science Conference "Human Sensing & Data Analysis: Modeling for Health, Well-being & Performance"
[Nov, 2020] Two papers
at MobiHealth2020 Conference.
"Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network"
"Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes"
[Sept, 2020] Received NIH funding to measure physical and mental health risks and develop a personalized advice system for dementia spousal caregivers to accomplish everyday tasks and boost their mental health while safely distancing in collaboration with Dr. Chris Fagundes's team at Rice Psychology
[Sept, 2020] Our rhythm feature based schizophrenia symptom personalized prediction paper was published in Scientific Reports [paper
[June, 2020] Ubicomp mental health and wellbeing virtual workshop on Sept 12
[June, 2020] Four papers
at EMBC 2020.
"Passive Sensor Data Based Future Mood, Health and Stress Prediction: User Adaption Using Deep Learning"
"Frequency-Dependent Light Stimulation Effects on Performance During Vigilance Tasks on a Laptop"
"Early versus Late Modality Fusion of Deep Features from Wearable Sensors for Personalized Prediction of Feature Wellbing"
"Forecasting Stress, Mood, and Health from Daytime Physiology in Office Workers and Students"
Please see here
[May, 2020] New paper in ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).
Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress.
[April, 2020] CHI paper "Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing"
[Feb, 2020] Excited to receive Microsoft productivity collaboration research award
to develop Unobtrusive Personalized Work Engagement Assistant.
[Jan, 2020] Joining the organization committee for International Conference on Multimodal Interaction (ICMI) 2021, Montreal.
[December, 2019] ACM Interactions Blog Article: REFLECTIONS ON MENTAL HEALTH ASSESSMENT AND ETHICS FOR MACHINE LEARNING APPLICATIONS
(with Drs. Anja Thieme, Danielle Belgrave, and Gavin Doherty)
[Novemver, 2019] Received Rice University Institute of Biosciences and Bioengineering: Hamill Innovation Awards: CraveSupport: Measuring and Intervening Craving Moments in Substance Use Disorders (SUD)
using Bio-behavioral Sensor
(in collaboration with Drs. Ashutosh Sabharwal (Rice), Nidal Moukaddam, Ramiro Salas (Baylor College of Medicine)).
Received AMED (Japan agency for medical research and development) funding for Collaboration with Mie University "Sleep and wellbeing recommendation system for shift workers"
[October, 2019] Abstract/presentation "Measuring Psychological Variables using Mobile Sensing Technologies: Modeling Big Data and Implications for Research and Designing Intelligent Support
for Well-Being and Productivity at Work"
at APA technology mind & society with Dr. Deniz Onez.
[September, 2019] Abstract/presentation at The Frontier of AI-Assisted Care Scientific Symposium, 2019 September: V. W. Tseng, A. Sano, T. Choudhury, Developing Clinically Interpretable Machine Learning Models to Predict Fine-Grained Symptom Trajectory of Schizophrenia and Identify Patients At Risk"
[August, 2019] Joining the Technical Program Committee for MobiCom 2020
[July, 2019] New Paper: Toward End-to-end Prediction of Future Wellbeing using Deep Sensor Representation Learning
in ACII 2019 ML4AD workshop
[July, 2019] New Paper: Daytime Data and LSTM Can Forecast Tomorrow's Stress, Health, and Happiness
in IEEE EMBC 2019
[May, 2019] Best Paper Award at IEEE Biomedical Health Informatics (BHI) 2019 in Chicago. Improving students daily life stress forecasting using lstm neural networks
[May, 2019] Joining a panel session "The Impacts of Machine Learning in Mental Health Care" at Deep Learning in Healthcare Summit
in Boston in May, 2019.
[May, 2019] We obtained Creative Ventures Fund: InterDisciplinary Excellence Awards (IDEA) with Profs. King and Denny at Rice Psychology department
for our new project "Fostering Positive Emotions and Psycho-Physio Resilience in Job Seekers and Beyond".
[May, 2019] Co-organizing a workshop Mental Health: Sensing & Intervention
at Ubicomp 2019 in London, UK. Paper submission deadline: July 1th, 2019
[April, 2019] Co-organizing a workshop Machine Learning for the Diagnosis and Treatment of Affective Disorders (ML4AD)
at ACII 2019 in Cambridge, UK. Paper submission deadline: June 21, 2019
[April, 2019] Our team obtained creative venture Faculty Initiative fund to support our new project "CityHealth: Measuring Mental Wellbeing of Houston to Empower City-scale Emotional Resilience and Preparedness for Adverse Weather Events"
[March, 2019] Two papers will be presented at IEEE BHI 2019 conferece in Chicago in May.
[March, 2019] Our computational wellbeing website
[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
[Nov, 2018] 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.
[August, 2018] 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.
[August, 2018] 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.
Please check admission websites at Rice ECE
depending on your background and mention my name and your interest in your research statement!
Please email me with your interest and CV.
Rice has a postdoc fellowship program for highly competitive applicants which offer substantial independence.
The Rice Academy of Fellows
provides competitive salary for 2 years for a cohort of postdoctoral scholars in departments across campus. Applications are due early January.
6100 Main Street, Houston, TX, 77098, USA
akane.sano at rice.edu
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