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spelling and workshop schedule
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‎events/psb2025/workshop.html‎

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@@ -87,53 +87,47 @@ <h3> Workshop Program: </h3>
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<tr>
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<td>Predrag Radivojac</td>
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<td class="affiliation">Northeastern University</td>
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<td class="time-cell">1:40-2:00</td>
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<td class="time-cell">1:40-2:10</td>
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<td>Assessment and failure-modes of generative models</td>
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</tr>
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<td>Emily Alsentzer</td>
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<td class="affiliation">Stanford University</td>
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<td class="time-cell">2:00-2:20</td>
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<td class="time-cell">2:10-2:30</td>
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<td>Foundational Models for Few-Shot Learning</td>
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</tr>
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<tr>
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<td>Geena Wu</td>
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<td class="affiliation">UChicago</td>
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<td class="time-cell">2:20-2:45</td>
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<td class="time-cell">2:30-2:55</td>
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<td>Foundational models in Genetics: 8 Most Interesting Papers from 2024</td>
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</tr>
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<tr class="break-row">
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<td colspan="2" style="text-align: center;">Break</td>
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<td class="time-cell">2:45-2:55</td>
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<td class="time-cell">2:55-3:05</td>
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<td></td>
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</tr>
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<td>Michael Burkhart</td>
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<td class="affiliation">UChicago</td>
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<td class="time-cell">2:55-3:20</td>
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<td class="time-cell">3:05-3:30</td>
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<td>Foundational models in Biomedicine: 8 Most Interesting Papers from 2024</td>
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</tr>
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<tr>
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<td>Ben Brown</td>
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<td class="affiliation">Lawrence Berkeley National Laboratory / Arva Intelligence</td>
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<td class="time-cell">3:20-3:40</td>
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<td>Foundation models in environmental and biomedical science -- and paths toward overcoming trust issues in high-risk domains</td>
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</tr>
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<tr>
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<td>Jonathan Chen</td>
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<td class="affiliation">Stanford University</td>
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<td class="time-cell">3:40-4:00</td>
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<td class="time-cell">3:30-3:50</td>
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<td>Foundation Models in Medical Reasoning - Fountain of Creativity or Pandora's Box?</td>
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</tr>
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<tr class="break-row">
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<td colspan="2" style="text-align: center;">Break</td>
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<td class="time-cell">4:00-4:10</td>
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<td class="time-cell">3:50-4:00</td>
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<td></td>
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</tr>
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<td>Panel / Open Discussion</td>
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<td></td>
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<td class="time-cell">4:10-4:30</td>
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<td class="time-cell">4:00-4:30</td>
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<td>What needs to happen for transformative as opposed to iterative progression?</td>
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</tr>
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</tbody>

‎index.html‎

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@@ -61,9 +61,15 @@ <h2>Select Publications</h2>
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<p><a href="https://scholar.google.com/citations?hl=en&user=2SvHNeQAAAAJ&view_op=list_works"> See a full list on Google Scholar </a></p>
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</header>
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<dl>
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<dt><a href="https://www.medrxiv.org/content/10.1101/2024.12.30.24319785v1">Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review</a></dt>
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<dd>
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<p>Rowan Hussein, Anna Zink, Bashar Ramadan, Frederick M Howard, Maia Hightower, Sachin Shah, Brett K Beaulieu-Jones. <i>Preprint</i> (2024)
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<p>Our systematic analysis of healthcare AI governance frameworks revealed significant gaps in addressing diverse organizational needs, leading to the development of HAIRA - a novel, resource-aware maturity model spanning seven critical domains. This adaptive framework provides actionable governance pathways across five organizational levels, from small practices to major medical centers, enabling healthcare institutions to systematically assess and advance their AI governance capabilities based on available resources.
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</p>
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</dd>
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<dt><a href="https://www.medrxiv.org/content/10.1101/2024.09.27.24314517v1">Synthetic Data Distillation Enables the Extraction of Clinical Information at Scale</a></dt>
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<dd>
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<p>Elizabeth Geena Woo*, Michael C Burkhart*, Emily Alsentzer, Brett Beaulieu-Jones<i> Preprint</i> (2024)
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<p>Elizabeth Geena Woo*, Michael C Burkhart*, Emily Alsentzer, Brett Beaulieu-Jones. <i> Preprint</i> (2024)
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<br> *co-first authors</p>
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<p>
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Our team demonstrated that synthetic data distillation can fine-tune smaller, open-source large-language models (LLMs) to achieve performance similar to larger models in extracting clinical information. This smaller model outperforms its base version and sometimes even the larger model. This approach will enable more scalable and cost-efficient clinical information extraction, improving tasks like patient phenotyping. </p>
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<section class="wrapper style1 align-center" id="people">
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<div class="inner">
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<h2>People</h2>
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<p>We're always looking to add talented & curious students, post-docs, progammers and data scientists.</p>
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<p>We're always looking to add talented & curious students, post-docs, programmers and data scientists.</p>
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<div class="items style1 medium onscroll-fade-in">
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<section>
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<span class="icon style2 major"><img src="./headshots/bbj-circle-140.png"></img></span>

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