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     <loc>https://RConsortium.github.io/RMedicine_website/Register.html</loc>
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workshops.html

@@ -164,7 +164,7 @@ <h3 class="anchored" data-anchor-id="travis-gerke">Travis Gerke</h3>
 </section>
 <section id="biography" class="level3">
 <h3 class="anchored" data-anchor-id="biography">Biography</h3>
-<p>Travis Gerke is an epidemiologist and open-source software developer. After receiving his Ph.D.&nbsp;in epidemiology from the University of Southern California, he worked as a data scientist at Apple and Posit. His work has focused on causal inference methodology and software development, including many R packages for causal inference. Collectively, open-source tools he has authored have millions of downloads. Malcolm also teaches workshops and courses in R, software development, and causal inference.</p>
+<p>Travis Gerke, ScD, is Director of Data Science at the PCCTC, a contract research organization that facilitates clinical trials and real-world evidence studies in oncology. He is also co-founder and chief analytics officer of cStructure, a technology company built to empower teams with a collaborative causal design and inference platform. He was trained in biostatistics and epidemiology at Harvard School of Public Health, and led academic labs in quantitative public health prior to roles in biotech/pharma.</p>
 </section>
 <section id="contributors" class="level3">
 <h3 class="anchored" data-anchor-id="contributors">Contributors</h3>
@@ -177,11 +177,11 @@ <h3 class="anchored" data-anchor-id="contributors">Contributors</h3>
 <h2 class="anchored" data-anchor-id="causal-inference-in-r">Causal Inference in R</h2>
 <section id="abstract" class="level3">
 <h3 class="anchored" data-anchor-id="abstract">Abstract</h3>
-<p>This workshop will use the NHANES Epidemiologic Follow-up Study (NHEFS) data. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.</p>
+<p>In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. By the workshop’s conclusion, you will have completed a “whole game” causal analysis.</p>
 </section>
 <section id="drs.-gerke-and-mcgowan-on-the-web" class="level3">
 <h3 class="anchored" data-anchor-id="drs.-gerke-and-mcgowan-on-the-web">Drs. Gerke and McGowan on the Web</h3>
-<p>Dr.&nbsp;Gerke can be found on <a href="https://github.com/tgerke">Github</a> and on Twitter <a href="https://twitter.com/travisgerke">@mtravisgerke</a>. Dr.&nbsp;Gerke leds cancer-focused research teams in the application and development of tools for applied machine learning, causal inference, and biostatistics. They direct data science efforts in cloud-based informatics and advanced analytics with a focus on oncology clinical trials.</p>
+<p>Dr.&nbsp;Gerke can be found on <a href="https://github.com/tgerke">Github</a> and on LinkedIn <a href="https://www.linkedin.com/in/travisgerke/">@travisgerke</a>.</p>
 <p>Dr.&nbsp;McGowan can be found blogging at <a href="https://livefreeordichotomize.com/">livefreeordichotomize.com</a>, on Twitter <a href="https://twitter.com/LucyStats">@LucyStats</a>, and podcasting on the American Journal of Epidemiology partner podcast, <a href="https://casualinfer.libsyn.com/">Casual Inference</a>.</p>
 </section>
 </section>