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          <p class="lead">I will finish my Physics PhD from the University of Texas at San Antonio in May 2017.</p>
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          <p class="lead">Parameter estimation is a critically important subject with real-world applications. We often want to find information that is buried in noise. An efficient technique for doing so is called Particle Swarm Optimization. I employed this algorithm in my data analysis research involving gravitational-wave and gamma-ray burst data.</p>
          <p>I wrote a demonstration webapp, and my research code uses a C++ implementation. Both are available on my GitHub site.
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              <p>As part of community outreach I gave a talk to members of Code#RGV:Brownsville.</p>
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              <h1>Member of LIGO</h1>
              <p>I've been a member of LIGO for the past 6 years. In September 2015, the last predication of Albert Einstein's theory of General Relativity was confirmed with the successful direct detection of gravitational waves!</p>
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          <h2 class="featurette-heading">UTSA Physics PhD</h2>
          <p class="lead">I will finish my Physics PhD from the University of Texas at San Antonio in May 2017. My research is a multi-messenger population study of gamma-ray bursts and gravitional waves. It required the development of statistical models, parameter estimation, data analysis of large data sets, and the numerical implmentation of algorithms in parallel programs that were run on the Lonestar supercomputer.</p>
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          <h2 class="featurette-heading">Finding information in the noise. <span class="text-muted">Parameter Estimation</span></h2>
          <p class="lead">Parameter estimation is a critically important subject with real-world applications. We often want to find information that is buried in noise. An efficient technique for doing so is called Particle Swarm Optimization. I employed this algorithm in my data analysis research involving gravitational-wave and gamma-ray burst data.</p>
          <p>I wrote a demonstration webapp, and my research code uses a C++ implementation. Both are available on my GitHub site.
        </div>
        <div class="col-md-5">
          <a href="http://marcnormandin.github.io/pso_js"><img class="featurette-image img-responsive center-block" src="static/featurette_pso.png" alt="Particle Swarm Optimization image"></a>
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          <h2 class="featurette-heading">Intensive computations <span class="text-muted">Run them on a supercomputer!</span></h2>
          <p class="lead">Extracting information from big data requires a lot of computations. My algorithms were implemented as a parallel program that runs on the Texas Advanced Computing Center's Lonestar 5 supercomputer (under an approved grant).</p>
        </div>
        <div class="col-md-5 col-md-pull-7">
          <a href="https://www.tacc.utexas.edu/systems/lonestar"><img class="featurette-image img-responsive center-block" src="https://www.tacc.utexas.edu/documents/1084364/1085267/lonestar.jpg" alt="TACC Lonestar 5 image"></a>
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