Gauss-Markov modeling and online crowdsensing for spatio-temporal processes

This talk will discuss (1) modelling and (2) monitoring of large spatio-temporal processes covering a city or country, with an application to urban traffic. (1) Gauss-Markov models are well suited for such processes. Indeed, they allow for efficient and exact inference and can model continuous variables. I will explain how to learn a discrete time Gauss-Markov model based on batch historical data using the elastic net and the graphical lasso.(2) Such processes are traditionally monitored by dedicated sensors set up by civil authorities, but sensors deployed by individuals are increasingly used due to their cost-efficiency. This is called crowdsensing. However, the reliability of these sensors is typically unknown and must be estimated. Furthermore, bandwidth, processing or cost constrains may limit the number of sensors queried at each time-step. We model this problem as the selection of sensors with unknown variance in a large linear dynamical system. We propose an online solution based on variational inference and Thompson sampling.

Bio

Francois Schnitzler is a post doctoral researcher at the Technion, working under the supervision of Professor Shie Mannor. He works on time-series modelling and event detection from heterogenous data and crowdsourcing. He obtained his PhD in September 2012 from the University of Liege, where he studied probabilistic graphical models for large probability distributions, and in particular ensemble of Markov trees.