<html xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:st1="urn:schemas-microsoft-com:office:smarttags" xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta http-equiv=Content-Type content="text/html; charset=iso-8859-1">
<meta name=Generator content="Microsoft Word 11 (filtered medium)">
<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags"
name="PlaceType"/>
<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags"
name="PlaceName"/>
<o:SmartTagType namespaceuri="urn:schemas-microsoft-com:office:smarttags"
name="place"/>
<!--[if !mso]>
<style>
st1\:*{behavior:url(#default#ieooui) }
</style>
<![endif]-->
<style>
<!--
/* Font Definitions */
@font-face
{font-family:"MS Mincho";
panose-1:2 2 6 9 4 2 5 8 3 4;}
@font-face
{font-family:"\@MS Mincho";
panose-1:2 2 6 9 4 2 5 8 3 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0in;
margin-bottom:.0001pt;
font-size:12.0pt;
font-family:"Times New Roman";}
a:link, span.MsoHyperlink
{color:blue;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{color:purple;
text-decoration:underline;}
span.EmailStyle17
{mso-style-type:personal-compose;
font-family:Arial;
color:windowtext;
font-weight:normal;
font-style:normal;
text-decoration:none none;}
@page Section1
{size:8.5in 11.0in;
margin:.8in .8in .8in .8in;}
div.Section1
{page:Section1;}
-->
</style>
</head>
<body lang=EN-US link=blue vlink=purple>
<div class=Section1>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'>When:
Tuesday, April 8, 1:00pm<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'> <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'>Where:
TTI-C Conference Room<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'> Topic:
Optimizing Sensing from Water to the Web<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'> <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'>Bio:<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
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=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'> <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'>Contact:
Nathan Srebro, TTI-C
nati@tti-c.org
834-7493<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:11.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
</div>
</body>
</html>