<div dir="ltr">Can we talk about this one publicly? I think we could get good venues for this one.<div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Fri, Jul 15, 2016 at 11:44 AM, Artur Dubrawski <span dir="ltr"><<a href="mailto:awd@cs.cmu.edu" target="_blank">awd@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
Team,<br>
<br>
I was surprised but very pleased to hear one of our projects
showcased as the sole template of<br>
a success story in a plenary talk by Dr. Joel Rynes, Assistant
Director for R&D at the U.S. Department<br>
of Homeland Security, at the DNDO ARI conference earlier this week.
<br>
<br>
This was completely unexpected, because the government officials at
this level of responsibility <br>
typically refrain from mentioning any vendor or partner names
explicitly in their public appearances. <br>
In the past, we had prepared numerous batches of presentation and
press-releasable materials for <br>
the use by our government sponsors, from which we had to remove any
and all references to CMU, <br>
the Robotics Institute, or the Auton Lab. <br>
<br>
Dr. Rynes basically said that everyone in the R&D community
working in the space of radiation safety<br>
should look at how our CMU team has developed the foundations of
ERNIE system and how the results of <br>
our research are being successfully transitioned to the U.S. Customs
practice, and how significant difference<br>
it will make when the system is fully deployed throughout the
organization.<br>
<br>
Hats go down above all to Saswati who continues to deliver
exceptional implementations of our <br>
algorithms and who makes our partners from Lawrence Livermore
National Laboratory and various<br>
groups of our sponsors and end-users very pleased with them.<br>
<br>
Cheers!<br>
ArturĀ <br>
<br>
PS ERNIE stands for "
Enhanced Radiological Nuclear Inspection and Evaluation". <br>
This is the project in which we look at the data collected using
Radiation Portal Monitors <br>
at the U.S. ports of entry, and the primary objective is to combine
physics of radiation models <br>
and machine learning to allow dismissing a large number of alerts
that are generated <br>
by non-threatening sources of radiation, while never missing a real
threat.
</div>
</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><font size="1"><a href="http://www.cs.cmu.edu/~awm/" target="_blank">Andrew Moore</a>, Dean, <a href="http://www.cs.cmu.edu/" target="_blank">School of Computer Science</a>, Carnegie Mellon. <a href="https://twitter.com/awmcmu" target="_blank">Twitter feed</a></font><div><br></div></div></div>
</div>