[Colloquium] **TIME CHANGE** Andreas Krause, Carnegie Mellon University- TTI-C Talk

[Julia MacGlashan]

**Please note: to accommodate those who would like to attend also Eirc
Vigoda's talk, Andreas Krause's talk will be postponed by 15 minutes.
The talk will start at 1:15PM rather than 1PM.**



When:             Tuesday, April 8, 1:00pm

Where:            TTI-C Conference Room

Who:              Andreas Krause, Carnegie Mellon University

Topic:            Optimizing Sensing from Water to the Web


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.

Bio:

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.

Contact:          Nathan Srebro, TTI-C         nati at tti-c.org       834-7493