Campus Event Calendar
Event Entry
What and Who
Optimizing Sensing from Water to the Web
Andreas Krause
Carnegie Mellon University, USA
Talk
Andreas Krause is a Ph.D. Candidate at the Computer Science Department
of Carnegie Mellon University. He is a recipient of a Microsoft
Research Graduate Fellowship, and his research on sensor placement and
information acquisition received awards at several conferences (KDD
'07, IPSN '06, ICML '05 and UAI '05). He obtained his Diplom in
Computer Science and Mathematics from the Technische Universität
München, where his research received the NRW Undergraduate Science
Award.
AG 1, AG 3, AG 5, RG2, AG 2, AG 4, RG1, SWS
MPI Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Wednesday, 30 April 2008
16:00
45 Minutes
E1 4
024
Saarbrücken
Abstract
Where should we place sensors to quickly detect contaminations in
drinking water distribution networks? Which blogs should we read to
learn about the biggest stories on the web? These problems share a
fundamental challenge: How can we obtain the most useful information
about the state of the world, at minimum cost?
Such sensing, or active learning, problems are typically NP-hard, and
were commonly addressed using heuristics without theoretical
guarantees about the solution quality. In this talk, I will present
algorithms which efficiently find provably near-optimal solutions to
large, complex sensing problems. Our algorithms exploit submodularity,
an intuitive notion of diminishing returns, common to many sensing
problems; the more sensors we have already deployed, the less we learn
by placing another sensor. To quantify the uncertainty in our
predictions, we use probabilistic models, such as Gaussian Processes.
In addition to identifying the most informative sensing locations, our
algorithms can handle more challenging settings, where sensors need to
be able to reliably communicate over lossy links, where mobile robots
are used for collecting data or where solutions need to be robust
against adversaries and sensor failures.
I will also present results applying our algorithms to several
real-world sensing tasks, including environmental monitoring using
robotic sensors, activity recognition using a built sensing chair,
deciding which blogs to read on the web, and a sensor placement
competition.
drinking water distribution networks? Which blogs should we read to
learn about the biggest stories on the web? These problems share a
fundamental challenge: How can we obtain the most useful information
about the state of the world, at minimum cost?
Such sensing, or active learning, problems are typically NP-hard, and
were commonly addressed using heuristics without theoretical
guarantees about the solution quality. In this talk, I will present
algorithms which efficiently find provably near-optimal solutions to
large, complex sensing problems. Our algorithms exploit submodularity,
an intuitive notion of diminishing returns, common to many sensing
problems; the more sensors we have already deployed, the less we learn
by placing another sensor. To quantify the uncertainty in our
predictions, we use probabilistic models, such as Gaussian Processes.
In addition to identifying the most informative sensing locations, our
algorithms can handle more challenging settings, where sensors need to
be able to reliably communicate over lossy links, where mobile robots
are used for collecting data or where solutions need to be robust
against adversaries and sensor failures.
I will also present results applying our algorithms to several
real-world sensing tasks, including environmental monitoring using
robotic sensors, activity recognition using a built sensing chair,
deciding which blogs to read on the web, and a sensor placement
competition.
Contact
Thorsten Thormählen
+49 681 9325-651
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Thorsten Thormählen, 04/29/2008 17:04 -- Created document.