Faculty Candidate Seminar

Computational Wireless Sensing at Scale

Rajalakshmi NandakumarPh.D. CandidateUniversity of Washington

Computational wireless sensing is an exciting field of research where we use wireless
signals from everyday computing devices to enable sensing. The key challenge is to enable new
sensing capabilities that can be deployed at scale and have an impact in the real world. In this
talk, I will show how to enable computational wireless sensing at scale by leveraging ubiquitous
hardware such as smartphones. Specifically, I will present core technology that can wirelessly
sense motion and physiological signals such as breathing using just a smartphone, in a
contactless manner. To achieve this, we transform smartphones into active sonar systems. I will
show how we can use this technology to detect potentially life-threatening conditions such as
opioid overdoses as well as sleep apnea. Finally, I will briefly talk about my work that leverages
new hardware trends in micro-controllers to enable wireless sensing applications ranging from
object tracking to sensing using live insects such as bees.
Rajalakshmi Nandakumar is a Ph.D. candidate at the Paul G. Allen School of Computer
Science and Engineering at the University of Washington. Her research focuses on developing
wireless sensing technologies that enable novel applications in various domains including
mobile health, user interfaces and IoT networks. She developed the first contactless
smartphone-based sleep apnea diagnosis system that was licensed by ResMed Inc. and now
used by millions of users for sleep staging. She was recognized with the UW Medicine Judy Su
Clinical Research award, Paul Baran Young Scholar award by the Marconi Society and also
named as the rising star in EECS by MIT. She has first author papers in top medical journals
including Science translational medicine as well as computing venues (CHI, SIGCOMM, SenSys,
MobiCom, MobiSys). Her research was awarded multiple accolades and nominations including
MobiSys 2015 best paper nominee, CHI 2016 Honorable mention award and SenSys 2018 best
paper award.

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