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<div id="stephen-berg" class="section level2" number="0.1">
<h2><span class="header-section-number">0.1</span> Stephen Berg</h2>
<p><img src="department-photo.jpg" width="250"/></p>
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<div id="about-me" class="section level2" number="0.2">
<h2><span class="header-section-number">0.2</span> About Me</h2>
<p>I am an assistant professor of statistics at Penn State University. I joined Penn State in 2020 after receiving my PhD in Statistics from the University of Wisconsin-Madison, where I was advised by Jun Zhu and Murray Clayton.</p>
<p>My research focuses on applications in spatial and spatial-temporal statistics, as well as on statistical computing problems, particularly related to Markov chain Monte Carlo (MCMC).</p>
<ul>
<li>Statistical computing</li>
</ul>
<p>My current work revolves around variance estimation and variance reduction for MCMC simulations. MCMC simulations are now standard computational tools in Bayesian statistics, for estimating things like posterior means and credible intervals.</p>
<pre><code>* Recently I have worked in collaboration with Hyebin Song at PSU on nonparametric variance estimation for MCMC variances, using tools similar to classical shape-constrained regression approaches like isotonic regression.</code></pre>
<p><br /></p>
<ul>
<li>Spatial-temporal statistics
<ul>
<li>I have worked on application problems in landscape ecology and in modeling chronic wasting disease (CWD) in Wisconsin deer. Generally, I am interested in constructing realistic models of natural phenomena in order to do inference and forecasting. Methodologically I am interested in differential equation and partial differential equation models, and addressing statistical and computational challenges for these models.</li>
<li>A recently added focus on the theory side is the nonparametric estimation of spatial covariance functions.</li>
</ul></li>
</ul>
<p><br /></p>
<ul>
<li>Nonparametric statistics
<ul>
<li>dsafd</li>
</ul></li>
</ul>
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