updated resume

index.html

@@ -92,12 +92,10 @@ <h2>
                         <div class="col-12" id="Novartis Institutes for Biomedical Research (NIBR)-data-col">
                             <ul>
 <li>Investigator in the Scientific Data Analysis (SDA) team reporting to <a href="https://www.linkedin.com/in/holger-hoefling-8418905">Holger Hoefling</a>.</li>
-<li>Led an initiative to characterize the performance of message passing neural networks.</li>
-<li>Developed a hierarchical Bayesian 4-parameter dose response model to aid project team in compound selection.</li>
-<li>Co-hosted a machine learning session with <a href="https://www.linkedin.com/in/sivakumar-gowrisankar-b71a016/">Sivakumar Gowrisankar</a> at the internal Data in Drug Discovery Day (D4).</li>
-<li>Co-organized an internal course with <a href="https://www.linkedin.com/in/yuan-wang-王元-0b19a822/">Yuan Wang</a> and <a href="https://www.linkedin.com/in/laszlo-urban-4444974/">Laszlo Urban</a> on machine learning for NIBR colleagues in the Pre-Clinical Safety (PCS) department.</li>
-<li>Led a deep learning workshop at our NIBR Shanghai office.</li>
-<li>Currently developing a deep Bayesian optimization pipeline to accelerate protein engineering cycles.</li>
+<li>Co-organized and developed teaching material for machine learning &amp; deep learning workshops and seminars internally at NIBR with colleagues <a href="https://www.linkedin.com/in/sivakumar-gowrisankar-b71a016/">Sivakumar Gowrisankar</a>, <a href="https://www.linkedin.com/in/yuan-wang-王元-0b19a822/">Yuan Wang</a>, <a href="https://www.linkedin.com/in/laszlo-urban-4444974/">Laszlo Urban</a>, and <a href="">Sean Xiao</a>.</li>
+<li>Developed a massively parallelized engine for training, evaluating, and serving machine learning models on internal assay data, with an emphasis on serving prediction uncertainties with NIBR colleagues <a href="https://www.linkedin.com/in/nikolaus-stiefl-39583b25/">Nikolaus Stiefl</a> and <a href="https://www.linkedin.com/in/gregorigerebtzoff/">Gregori Gerebtzoff</a>.</li>
+<li>Developed Bayeisan graph deep learning models for chemical and protein property prediction with <a href="https://www.linkedin.com/in/william-j-godinez/">William J. Godinez</a></li>
+<li>Co-mentored <a href="https://www.linkedin.com/in/arkadij-kummer-78b249b9/">Arkadij Kummer</a> with <a href="https://www.linkedin.com/in/richard-lewis-8916891/">Richard Lewis</a> in support of the development of machine learning and deep learning workflows for protein engineering.</li>
 </ul>
                         </div>
                     </div>
@@ -120,8 +118,8 @@ <h2>
 <li>Investigator in the Scientific Data Analysis (SDA) team reporting to <a href="https://www.linkedin.com/in/borowsky/">Mark Borowsky</a>.</li>
 <li>Performed internal consulting projects and expanded the SDA Statistical Learning initiative with colleagues.</li>
 <li>Developed parameterized Bayesian agent-based models of internal project portfolio for scenario planning purposes, using <a href="http://docs.pymc.io">PyMC3</a> and <a href="https://github.com/projectmesa/mesa">Mesa</a>.</li>
-<li>Authored custom deep learning package to train graph convolutional neural networks to predict structural determinants of RNA cutting, with one paper currently in writing.</li>
-<li>Assisted in mentoring two interns, <a href="https://www.linkedin.com/in/stacy-meichle/">Stacy Meichle</a> (Computer Aided Drug Discovery) and <a href="https://www.linkedin.com/in/flekschas/">Fritz Lekschas</a> (SDA).</li>
+<li>Assisted in the analysis of high throughput DROSHA cleavage data, with one paper currently in writing.</li>
+<li>Assisted in mentoring two interns, <a href="https://www.linkedin.com/in/stacy-meichle/">Stacy Meichle</a> (Computer Aided Drug Discovery with <a href="https://www.linkedin.com/in/clayton-springer-5a48072/">Clayton Springer</a>) and <a href="https://www.linkedin.com/in/flekschas/">Fritz Lekschas</a> (SDA with <a href="https://www.linkedin.com/in/brant-peterson-b390a51/">Brant Peterson</a>).</li>
 </ul>
                         </div>
                     </div>

resume.yaml

@@ -21,7 +21,9 @@ experience:
       description: |
         - Investigator in the Scientific Data Analysis (SDA) team reporting to [Holger Hoefling](https://www.linkedin.com/in/holger-hoefling-8418905).
         - Co-organized and developed teaching material for machine learning & deep learning workshops and seminars internally at NIBR with colleagues [Sivakumar Gowrisankar](https://www.linkedin.com/in/sivakumar-gowrisankar-b71a016/), [Yuan Wang](https://www.linkedin.com/in/yuan-wang-王元-0b19a822/), [Laszlo Urban](https://www.linkedin.com/in/laszlo-urban-4444974/), and [Sean Xiao]().
-        - Developed a massively parallelized engine for training, evaluating, and serving machine learning models on internal assay data, with an emphasis on serving prediction uncertainties.
+        - Developed a massively parallelized engine for training, evaluating, and serving machine learning models on internal assay data, with an emphasis on serving prediction uncertainties with NIBR colleagues [Nikolaus Stiefl](https://www.linkedin.com/in/nikolaus-stiefl-39583b25/) and [Gregori Gerebtzoff](https://www.linkedin.com/in/gregorigerebtzoff/).
+        - Developed Bayeisan graph deep learning models for chemical and protein property prediction with [William J. Godinez](https://www.linkedin.com/in/william-j-godinez/)
+        - Co-mentored [Arkadij Kummer](https://www.linkedin.com/in/arkadij-kummer-78b249b9/) with [Richard Lewis](https://www.linkedin.com/in/richard-lewis-8916891/) in support of the development of machine learning and deep learning workflows for protein engineering.
 
     - company: Novartis Institutes for Biomedical Research (NIBR)
       title: Investigator I
@@ -32,7 +34,7 @@ experience:
         - Performed internal consulting projects and expanded the SDA Statistical Learning initiative with colleagues.
         - Developed parameterized Bayesian agent-based models of internal project portfolio for scenario planning purposes, using [PyMC3](http://docs.pymc.io) and [Mesa](https://github.com/projectmesa/mesa).
         - Assisted in the analysis of high throughput DROSHA cleavage data, with one paper currently in writing.
-        - Assisted in mentoring two interns, [Stacy Meichle](https://www.linkedin.com/in/stacy-meichle/) (Computer Aided Drug Discovery) and [Fritz Lekschas](https://www.linkedin.com/in/flekschas/) (SDA).
+        - Assisted in mentoring two interns, [Stacy Meichle](https://www.linkedin.com/in/stacy-meichle/) (Computer Aided Drug Discovery with [Clayton Springer](https://www.linkedin.com/in/clayton-springer-5a48072/)) and [Fritz Lekschas](https://www.linkedin.com/in/flekschas/) (SDA with [Brant Peterson](https://www.linkedin.com/in/brant-peterson-b390a51/)).
 
     - company: Insight Data Science
       title: Health Data Science Fellow