Computer Science Colloquium - Cuong Pham, Microsoft
Thu, March 15th, 2018
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Fast and Accurate Indoor Localization based on Spatially Hierarchical Classification
Location fingerprinting is a common approach to indoor localization. For good accuracy, the training set of sample fingerprints should be sufficiently large to be well-representative of the environment in terms of both spatial coverage and temporal coverage. As such, the computation required during the positioning phase can be expensive because we have to evaluate each new fingerprint against the training data repeatedly over time. It is desirable, therefore, to optimize computational efficiency, not just localization accuracy. Existing techniques are
far from this goal due to their polarization toward one criterion but not both. We propose a substantially better technique based on the novel approach of modeling indoor localization as a classification learning problem where classes form a spatial hierarchy. Its performance is substantiated in our evaluation study.
Cuong (Charlie) Pham is a software engineer in Office Product Group at Microsoft, where he is working on Office 365 suite and services for educational institutions, enterprises, and home users. He earned a BS degree in Computer Science from Bauman Moscow State Technical University and a PhD from the University of Massachusetts Boston. His research interests span the areas of
computer networks and distributed systems. His research work resulted in 14 publications, a Best Paper Award at IEEE MASS in 2014, and Research Excellence Awards from the Department of Computer Science at UMass Boston in 2011 and 2009. He was a research intern at Distributed Storage Network Group at EMC and
Cloud Computing and Storage Group at Huawei R&D. Dr. Pham has served as TPC co-chair for the International Workshop on Wireless Mesh and Ad Hoc Networks (WiMAN) in 2018 and 2017, and TPC member for journals and international conferences. He is a volunteer teacher for Hour of Code. He also spent sometimes chasing storms at National Severe Storm Lab in Oklahoma back in 2007.