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SUMMARY:Toward Personalized and Adaptive Health and Wellbeing Assistants  
 - Akane Sano (Rice University)
DTSTART:20210201T160000Z
DTEND:20210201T170000Z
UID:TALK151960@talks.cam.ac.uk
CONTACT:Lorena Qendro
DESCRIPTION:*Abstract:* Imagine 24/7 rich human multimodal data could iden
 tify changes in physiology and behavior\, and provide personalized early w
 arnings to help you\, patients\, or clinicians for making better decisions
  or behavioral changes to support health and wellbeing. \nI will introduce
  a series of studies\, algorithms\, and systems we have developed for meas
 uring\, predicting\, and supporting personalized health and wellbeing for 
 clinical populations as well as people at increased risk of adverse events
 \, including ongoing COVID-19 related projects.\nI will also discuss chall
 enges\, learned lessons\, and potential future directions in digital healt
 h and wellbeing research.\n\n*Bio:* Akane Sano is an Assistant Professor a
 t Rice University\, Department of Electrical Computer Engineering\, Comput
 er Science\, and Bioengineering.\nShe directs Computational Wellbeing Grou
 p. She is also a member of Rice Scalable Health Labs.\n\nHer research focu
 ses on affective\, 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 physiol
 ogical\, biological\, and behavioral data for measuring\, predicting\, imp
 roving\, and understanding human physiology and behavior and human factors
  such as health\, wellbeing\, and performance and (2) development of human
 -centered computing technologies to support health and wellbeing. \n\nShe 
 obtained her Ph.D. at MIT. Before she came to the US\, she was a researche
 r/engineer at Sony Corporation and worked on affective/wearable computing\
 , intelligent systems\, and human-computer interaction.\n\nHer recent awar
 ds include Microsoft Productivity Research Award in 2019\, \nthe Best Pape
 r Award at IEEE BHI 2019 conference\, the Best Paper Award at the NIPS 201
 6 Workshop on Machine Learning for Health\, and the 2014 AAAI Spring Sympo
 sium Best Presentation Award.
LOCATION:Virtual (see abstract for Zoom link)
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