Abstract
Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth technical challenges to research and engineering on both the device and the server end. This talk focuses on addressing three technical topics: 1) sensor signal processing and fusion, 2) big data analytics on streaming sensor data, and 3) power-conserving hardware and software co-design for reducing power consumption on devices. This talk uses two significant industrial projects that the speaker led - Google indoor positioning and HTC XPRIZE Tricorder - to illustrate technical challenges and depict promising solutions.
About the speaker
Prof Edward Chang received his PhD in Electrical Engineering from Stanford University in 1999. He joined the University of California at Santa Barbara afterwards and became Full Professor of Electrical Engineering in 2006. He was then Director of Google Research for 6.5 years, in charge of research and development in several areas including indoor positioning, big data mining, social networking and search integration, and Web search (spam fighting). He has joined HTC Corporation since 2012, and is currently Vice President of Research and Innovation.
Work of Prof Chang’s research team on indoor positioning with project X was deployed via Google Map. Prof Chang’s contributions in parallel machine learning algorithms and big-data mining are recognized via several keynote invitations. The open-source, big-data mining codes developed by his team (PSVM, PLDA+, Parallel Spectral Clustering, and Parallel Frequent Pattern Mining) have been collectively downloaded over 10,000 times. The Google Q&A system (codename Confucius) developed by his team was launched in over 60 countries including China, Russia, Thailand, Vietnam, and Indonesia, as well as 17 Arab and 40 African nations. His team is also devoted to develop algorithms and components for Web search (spam fighting), Google+ (recommendation engine and Open ID), Chrome, and PicasaWeb. His book titled Foundations of Large-Scale Multimedia Information Management and Retrieval provides a good summary of his experience in applying big data techniques in feature extraction, learning, and indexing for organizing multimedia data to support both management and retrieval.
Prof Chang has received prestigious awards including the US National Science Foundation Career Award, the IBM Faculty Partnership Award, and the Google Innovation Award.
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