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<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>When:
Tuesday,
April 8, 1:00pm<b><span style='font-weight:bold'><o:p></o:p></span></b></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Where:
TTI-C Conference Room<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p style='margin:0in;margin-bottom:.0001pt'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Who:
Andreas Krause, <st1:place w:st="on"><st1:PlaceName w:st="on">Carnegie</st1:PlaceName>
<st1:PlaceName w:st="on">Mellon</st1:PlaceName> <st1:PlaceType w:st="on">University</st1:PlaceType></st1:place><o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Topic:
Optimizing
Sensing from Water to the Web<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>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?<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>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.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>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.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>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.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>Bio:<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'>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.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'>Contact:
Nathan Srebro,
TTI-C nati@tti-c.org
834-7493<font color=navy><span style='color:navy'><o:p></o:p></span></font></span></font></p>
<p style='margin:0in;margin-bottom:.0001pt'><font size=3 face=Arial><span
style='font-size:12.0pt;font-family:Arial'> </span></font><o:p></o:p></p>
<p class=MsoNormal><font size=3 face=Arial><span style='font-size:12.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
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