[talks] Seminar-Dr. Shijia Pan, Thursday, September 27

[Emily Lawrence]

Seminar Speaker

Dr. Shijia Pan from Carnegie Mellon University

Thursday, September 27, 2018 - 12:30pm

Computer Science - Room 402

Host: Margaret Martonosi

 

"Indoor Human Information Acquisition from Physical Vibrations"

 

Abstract:

The number of everyday smart devices (such as smart TV, Samsung SmartThings,
Nest, Google

Home) is projected to grow to the billions in the coming decade. The
Cyber-Physical Systems or Internet

of Things systems that consist of these devices are used to obtain human
information for various smart

building applications. Different sensing approaches have been explored,
including vision-, sound-, RF-,

mobile-, and load-based methods, to obtain various indoor human information.
>From the system

perspective, general problems faced by these existing technologies are their
sensing requirements (e.g.,

line-of-sight, high deployment density, carrying a device) and intrusiveness
(e.g., privacy concerns).

 

My research focuses on non-intrusive indoor human information acquisition
through ambient

structural vibration, which is referred to as 'structures as sensors'.
People's interaction with structures in

the ambient environment (e.g., floor, table, door) induces those structures
to vibrate. By capturing and

analyzing the vibration response of structures, we can indirectly infer
information about the people and

their actions that cause it. However, challenges remain. Due to the
complexity of the physical world (in

this case, both structures and people), sensing data distributions can
change significantly under different

sensing conditions. Therefore, from the data perspective, accurate
information learning through a pure

data-driven approach requires a large amount of labeled data, which is
costly and difficult if not

impossible to obtain in real-world sensing applications. My research
addresses these challenges by

combining physical knowledge and data-driven approaches. Specifically, my
system can robustly learn

human information from limited labeled data distributions by iteratively
expanding the labeled dataset.

With insights into the relationship between changes of sensing data
distributions and measurable physical

attributes, the iterative algorithm guides the expansion order by measured
physical attributes to ensure a

high learning accuracy in each iteration.

 

Bio:

Dr. Shijia Pan is a postdoctoral researcher at Carnegie Mellon University.
She received her Bachelor's

degree in Computer Science and Technology from University of Science and
Technology of China and

her Ph.D. degree in Electrical and Computer Engineering at Carnegie Mellon
University. Her research

interests include cyber-physical systems, Internet-of-Things (IoT), and
ubiquitous computing. She worked

in multiple disciplines and focused on indoor human information acquisition
through ambient sensing.

She has published in both top-tier Computer Science ACM/IEEE conferences
(IPSN, UbiComp) and

high-impact Civil Engineering journals (Journal of Sound and Vibration,
Frontiers Built Environment).

She is the recipient of numerous awards and fellowships, including Rising
Stars in EECS, Nick G.

Vlahakis Graduate Fellowship, Google Anita Borg Scholarship, Best Poster
Awards (SenSys, IPSN), Best

Demo Award (Ubicomp), Best Presentation Award (SenSys Doctoral Colloquium),
and Audience Choice

Award (BuildSys) from ACM/IEEE conferences.

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