TOCS Event
|
Speaker: |
 Joy Ying Zhang
|
|
Date/Time: |
Nov. 1, 1:30pm PT |
|
Location: Webcast: |
CMUSV, Rm 118 [directions] |
| Title: | Mobile Sensing for Behavior-ware Mobile Computing |
| Abstract: | Today’s smart phones come equipped with a rich range of sensors including GPS,
accelerometers, WiFi, Bluetooth, NFC, microphone etc. Combined, this contextual information
can tell us a great deal about a user’s current activity: what is the user doing now at which
location and for how long. When logged, this data can provide important information about the
user’s behavior patterns based on which caregivers can design effective and personalized plans
to improve the user’s health and wellbeings. If we can aggregate this kind of information across
hundreds of volunteers in a city, it can also tell us a great deal about that city, for example, wait
times for buses, how public and private places are used, what residents typically do, and so on.
This kind of large-scale data collection and analysis offers a way to understand human behavior
at large scale, which can have positive impact in a number of domains, including health care,
traffic planning, urban design, and social network analysis. Though collecting sensor information is trivial, making sense of these heterogeneous sensory data is challenging. In our research, we separate the data representation and the processing algorithms to develop a generic framework for mobile sensing. Though quantization and sensor fusion, heterogeneous sensor input is converted into a symbolic representation called behavior text. Based on this text-like representation, well-established statistical natural language processing algorithms developed in the areas of language modeling, information retrieval, text summarization, text classification and even machine translation can be applied to tackle the mobile sensing problems. |
| Speaker Bio: | Dr. Joy Ying Zhang is an assistant research professor in Mobility Research Center at Carnegie Mellon University Silicon Valley. He received his Ph.D. from Language Technologies Institute of Carnegie Mellon University. Most of his research centers around applying statistical learning on natural language processing problems, in particular, statistical machine translation systems. He has developed the Pandora translation system, a full-scale two-way phrase-based statistical machine translation engine for mobile devices. This technology has been commercialized in the Jibbigo Speech Translator for iPhone, the first and so-far only voice-to-voice translation system that does not require network connection. His current research interests include applying statistical learning methods on mobile applications for user behavior modeling and behavioraware mobile computing including indoor positioning, geo-trace modeling, mobile lifelog. |