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hi all,<br>
<br>
there will be <b>no group meeting tomorrow</b> due to a couple other
interesting events on the schedule this week. <br>
<br>
instead, as a group we should attend <b>today's compton lecture given
by the director of the nih</b> at 3:30pm in 10-250 (more info <a
href="http://compton.mit.edu/">here</a>). we are also hosting a <b>talk
by dr jimeng sun on thursday</b>, more info pasted below.<br>
<div class="moz-signature"><br>
our group meetings will resume on <b>wednesday november 12</b> since
pete is traveling next week. who's presenting then?<br>
<br>
cheers,<br>
fern<br>
<br>
<br>
-------------<br>
<h3>Do it once, Do it right - Building a Scalable Predictive Modeling
Platform for Healthcare Applications</h3>
<p><b>Speaker:</b> Jimeng Sun</p>
<p><b>Speaker Affiliation:</b> Georgia Institute of Technology</p>
<p><b>Host:</b> Prof. Peter Szolovits</p>
<p><b>Host Affiliation:</b> CSAIL Clinical Decision Making Group</p>
<p><b>Date:</b> Thursday, October 30, 2014</p>
<p><b>Time:</b> 1:00 PM to 2:00 PM</p>
<p><b>Refreshments Time:</b> 2:00 PM</p>
<p><b>Location:</b> 32-D463</p>
<p>Predictive modeling plays an important role in biomedical research.
Thanks to the explosion of Electronic Heart Records (EHR), the interest
of building predictive models using EHR data have skyrocketed in recent
years. However, the methodologies for develop a predictive model are
still labor intensive and ad-hoc. Such rudimentary approaches have
hindered the quality and throughput of healthcare and biomedical
research. <br>
</p>
<p> In this talk, we promote a holistic approach that combines 1)
algorithm development and 2) system building. We believe such a
specialized big-data platform that can significantly speedup the
development of robust and accurate predictive models for biomedical
research. <br>
</p>
<p>I will present different projects covering both aspects: <br>
</p>
<p>Algorithms: I will first describe our work on computational
phenotyping from EHR using sparse tensor factorization; then I will
present patient similarity method using supervised distance metric
learning. <br>
</p>
<p>System: Finally I will introduce a parallel predictive modeling
platform using Hadoop for enabling parallel model exploration on big
data. <br>
</p>
<p><br>
Bio:<br>
Jimeng Sun is an Associate Professor of School of Computational Science
and Engineering at College of Computing in Georgia Institute of
Technology. Prior to joining Georgia Tech, he was a research staff
member at IBM TJ Watson Research Center. His research focuses on health
analytics using electronic health records and data mining, especially
in designing novel tensor analysis and similarity learning methods and
developing large-scale predictive modeling systems. <br>
</p>
<p>Dr. Sun has worked on various healthcare applications such as
computational phenotyping from electronic health records, heart failure
onset prediction and hypertension control management. He has
collaborated with many healthcare institutions including Vanderbilt
university medical center, Children's healthcare of Atlanta, Center for
Disease Control and Prevention (CDC), Geisinger Health System and
Sutter Health. <br>
</p>
<p>He has published over 70 papers, filed over 20 patents (5 granted).
He has received ICDM best research paper award in 2008, SDM best
research paper award in 2007, and KDD Dissertation runner-up award in
2008. Dr. Sun received his B.S. and M.Phil. in Computer Science from
Hong Kong University of Science and Technology in 2002 and 2003, and
PhD in Computer Science from Carnegie Mellon University in 2007.</p>
<br>
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