Andreas Krause, ETH Zurich, Switzerland

Machine Learning in Community Sensing and Human Computation
There is a tremendous potential in harnessing sensing resources spread throughout the population and infrastructure in order to achieve benefits for society at large. In this series of talks I will introduce two projects in Community Sensing. The Community Seismic Network aims to rapidly detect and monitor earthquakes using accelerometers in cell phones and other consumer devices. The OpenSense project employs inexpensive sensors mounted on trams, buses and bikes to provide high-resolution air quality maps, in order to study health implications of exposure to air pollution. Both of these projects share the goal of estimating complex phenomena (air quality, earthquakes) based on large numbers of heterogeneous sensors operated by the community. Motivated by these and other applications, e.g., in Human Computation, citizen science and crowdsourcing, I will focus on particular technical challenges such as: How can one piece together a global picture from large numbers of noisy observations of a priori unknown quality? How can one maximize the value of the information gained under constraints such as communication, privacy, power and budget? In particular, I will discuss how modern techniques from machine learning, discrete optimization and probabilistic reasoning offer solutions to these challenges.

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