Research

Current Research Areas

My current research interests are principally in the following areas:

• Mobile And Wearable Computing (activity and gesture recognition, IoT-based sensing, wireless energy harvesting, novel wearable interfaces) More...
• Urban And Socio-Physical Analytics and Smart City Services (spatiotemporal data mining, urban event and anomaly detection) More...
• Urban Services and Applications (Mobile Crowdsourcing, Smart Venues and Testbeds) Less..

Over the last few years, mobile crowdsourcing, where a pool of at-will workers performs location-specific micro-tasks, has created disruptions in many urban services—including transportation (e.g., Uber) and last-mile package delivery (e.g., Amazon Flex). Broadly speaking, this body of research is driven by a central question: Can such mobile crowdsourcing services be made more effective by better leveraging the predicted movement path and the behavioral preferences of workers? Some of my recent work in this area includes:

· Trajectory-aware task recommendation. To improve worker productivity, we pioneered a “push” model of crowdsourcing [paper1, paper2], where the crowdsourcing platform proactively recommends tasks that maximize the task completion rate while minimizing a worker’s detour from her routine movement trajectory. More recently, we’ve been exploring techniques to extend this paradigm to crowd-sourced pickup-and-delivery tasks (e.g., last mile package delivery), where task execution requires a worker to visit both (source, destination) locations.

· Experimental crowdsourcing. To support experimental investigations into worker behavior during crowdsourcing, we have developed and deployed Ta$ker, a campus-based experimental mobile crowdsourcing platform used over 1000 student “workers”, on the SMU campus. Ta$ker has enabled us to develop and empirically validate a variety of crowd-sourcing related technologies: e.g., the use of task bundling [paper] to improve worker productivity, the use of dynamic peer offloading [paper] to significantly improve the task completion rate, and the development of location obfuscation strategies [paper] that enhance worker location privacy with only modest impact on productivity. Recently, we’ve been exploring smart notification strategies that help to increase both overall task completion rates and also tackle spatiotemporal skews in such task completion. We are actively working with public agencies in Singapore to embed these crowd-sourcing concepts into new, city-scale services being rolled out for greater government-citizen and citizen-citizen engagement.