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<h1>Rapid Detection of Emerging Pattern</h1>
<p>Auton research is driven by the strong desire to create innovative solutions
which make real life impact. Research at the Auton Lab is not motivated solely
by its "coolness" from an academic point of view, it is also important that the
research is useful to end users. As a matter of fact, the majority of our
research is driven by challenging real world problems presented by end users in
the application domain, be it bio-surveillance, asteroid tracking, link
analysis, or drug discovery. This is an <strong>incomplete</strong> list of our
main lines of research in recent years, which hopefully will give you a taste of
the research activities in the Auton Lab.</p>
<h1>Massive Data Mining</h1>
<h2>Rapid Detection of Emerging Patterns</h2>
<h1>Social Network Analysis/Link Analysis/Group Detection</h1>
<p>The ability to rapidly detect and identify unanticipated emerging patterns
and trends in data can be highly beneficial in many practical situations...
<a href="daisy:16614">Read More</a></p>
<h1>Life Science Data Mining</h1>
<h2>Rapid Detection of Emerging Pattern</h2>
<p><tt><tt>Data mining algorithms at Auton Lab have successfully detected new
emerging patterns in various domains: Health services, Agriculture,
Manufacturing and Oil companies. Our algorithms are 10-1000 times faster than
other traditional techniques. The results demonstrate significantly higher
detection power with much smaller false positive rates. We have applied these
algorithms in semi/fully-automated modes under supervied/unsupervised
environments and for retrospective/prospective surveillance. A few algorithms
for Rapid detection of emerging patterns are: WSARE, Ultra Fast SSS, and
TipMon.</tt></tt></p>
<h2>Massive Data Mining</h2>
<p><tt>The Auton Lab has over 10 years of experience with data mining on massive
data streams. We have expertise with both established techniques and in the
development of new algorithms to provide robust and efficient solutions for
massive data sets. Our work has previously addressed problems in range of
fields, including: bio-survelience, large-scale astronomy, the intelligence
community, robotics, life sciences, and a variety of industrial applications.
This work include both a large number of successful software deployments and a
range available general purpose software.</tt></p>
<p>Development of new algorithms to provide robust and efficient solutions for
massive data sets, which addressed problems in a range of fields including
bio-survelience, large-scale astronomy, intelligence community applications,
robotics, and a variety of industrial applications...
<a href="daisy:16615">Read More</a></p>
<p><tt>Our work in massive scale data mining allows users to tractably process
large data sets, addressing such problem as:</tt></p>
<h2>Social Network Analysis/Link Analysis/Group Detection</h2>
<ul>
<li><tt>Discovering (previously unknown) structure or patterns in the data -
What can we say about the underlying structure of the data? Our work on this
problem focuses on learning underlying probabilistic models. In particular, we
have significant experience in efficiently learning large Bayesian networks,
which provide a powerful and readable description of the underlying model.</tt>
</li>
</ul>
<p>Discover interesting relationships and patterns among people or other
entities... <a href="daisy:16616">Read More</a></p>
<ul>
<li><tt>Finding anomalous or interesting data points buried within the data -
Given a large set of data points, can we identify any as anomalous? Our work on
this problem has been used to find new, interesting objects in such data sets as
the Sloan Digital Sky Survey.</tt></li>
</ul>
<h2>Life Science Data Mining</h2>
<ul>
<li><tt>Accurately classifying new data points - Can we accurately classify a
new observation given a historical set of data points? Our work on this problem
has touched a variety of applications and includes developing new more efficient
methods for such techniques as nearest neighbor classification and logistic
regression.</tt></li>
</ul>
<p>From core areas like drug discovery and drug classification, to big-picture
problems in epidemiology and pathogen detection...<a href="daisy:16617">Read
More</a></p>
<ul>
<li><tt>Intelligently choosing the best action to perform - Given a noisy view
of the current world state, how do we best choose the next action to perform?
Our work on this problem includes both traditional questions in robotics and the
question of active learning. Active learning asks how we should next sample the
data point so as to get the most useful information, allowing us to minimize the
number of potentially expensive experiments.</tt></li>
</ul>
<p><tt>Our primary specialty is in developing novel ways to exploit structure
within both the data and the problem itself to make our approaches significantly
faster. In particular, we have developed a range of efficient data structures
and search algorithms that effectively target the algorithms, focusing the
computation on the important aspects of the problem. Thus our work enables
experts in other fields to accurately and tractably mine massive data streams in
their area of interest.</tt></p>
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