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Merge pull request #10 from zabore/main
adding erin pic and bio
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Register.qmd

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### Registration Pricing
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The following table lists the registration fees for three categories of attendees: students, academics and members of non-profit organizations, and professionals employed in industry who do not have non-profit status. All prices are in U.S. dollars. Early Bird pricing ends **April 10, 2025**.
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The following table lists the registration fees for three categories of attendees: students, academics and members of non-profit organizations, and professionals employed in industry who do not have non-profit status. All prices are in U.S. dollars. Early Bird pricing ends **April 10, 2025**.
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| Category | Early Bird | Regular |
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docs/index.html

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<section id="ziad-obermeyer" class="level3">
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<h3 class="anchored" data-anchor-id="ziad-obermeyer">Ziad Obermeyer</h3>
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<p><strong>Reinventing medicine with AI</strong></p>
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<p><img src="images/Z-Obermeyer-mucem-scaled.jpg" class="img-fluid"></p>
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<p><img src="images/Z-Obermeyer-mucem-scaled-square.jpg" class="img-fluid"></p>
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<section id="abstract" class="level4">
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<h4 class="anchored" data-anchor-id="abstract">Abstract</h4>
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<p>Many medical breakthroughs start with an empirical observation: a curious, unexplained pattern seen in real patients. Underlying mechanisms, unknown at first, are later mapped out in careful experiments. This “bedside to bench” pathway for discovery is less and less common —both because low-hanging fruit has been picked, and because doctors today have little time for observation. I’ll give a few examples of how artificial intelligence can help reboot this pathway: AI is a powerful engine for generating novel empirical observations in real-world data, many of them invisible to the human eye. Translating facts ‘discovered’ by AI into (i) improvements in clinical care and (ii) scientific discoveries are at the core of a new science of medicine, powered by data and computation.</p>
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</div><div class="column" style="width:45%;">
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<section id="erin-ledell" class="level3">
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<h3 class="anchored" data-anchor-id="erin-ledell">Erin LeDell</h3>
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<p><strong>Title TBD</strong></p>
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<p><img src="images/erin.jpg" class="img-fluid"></p>
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<details>
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<summary>
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Biography
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</summary>
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Dr.&nbsp;Erin LeDell is the Chief Scientist at Distributional, Inc.&nbsp;where she’s helping build an automated testing platform for statistical testing of AI applications. She is also the founder of DataScientific, Inc., an AI Advisory and Consulting firm specializing in the development and implementation of cutting-edge AI solutions. Previously, she was the Chief Machine Learning Scientist at H2O.ai, and founder of Women in Machine Learning and Data Science (WiMLDS) and co-founder of R-Ladies Global. Erin received her Ph.D.&nbsp;in Biostatistics from University of California, Berkeley and has a B.S. and M.A.&nbsp;in Mathematics.
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</details>
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docs/search.json

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"href": "index.html#keynote-addresses",
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"title": "R/Medicine 2025",
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"section": "KEYNOTE ADDRESSES",
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"text": "KEYNOTE ADDRESSES\n\n\n\nZiad Obermeyer\nReinventing medicine with AI\n\n\nAbstract\nMany medical breakthroughs start with an empirical observation: a curious, unexplained pattern seen in real patients. Underlying mechanisms, unknown at first, are later mapped out in careful experiments. This “bedside to bench” pathway for discovery is less and less common —both because low-hanging fruit has been picked, and because doctors today have little time for observation. I’ll give a few examples of how artificial intelligence can help reboot this pathway: AI is a powerful engine for generating novel empirical observations in real-world data, many of them invisible to the human eye. Translating facts ‘discovered’ by AI into (i) improvements in clinical care and (ii) scientific discoveries are at the core of a new science of medicine, powered by data and computation.\n\n\nBiography\n\nZiad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. He teaches at School of Public Health and was a founding member of the Berkeley–UCSF joint program in Computational Precision Health. His research uses machine learning to help doctors make better decisions, and help researchers make new discoveries by ‘seeing’ the world the way algorithms do. His work on algorithmic racial bias has been highly influential in shaping how health care organizations and policy makers hold AI accountable, from work with state Attorneys-General to testimony before the Senate Finance Committee. He is a cofounder of Nightingale Open Science and Dandelion Health, a Chan–Zuckerberg Biohub Investigator, and a Research Associate at the National Bureau of Economic Research. He was named one of the 100 most influential people in AI by TIME Magazine. Previously, he was Assistant Professor at Harvard Medical School, and he continues to practice emergency medicine in underserved communities.\n\n\n\n\n\n\n\nErin LeDell"
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"text": "KEYNOTE ADDRESSES\n\n\n\nZiad Obermeyer\nReinventing medicine with AI\n\n\nAbstract\nMany medical breakthroughs start with an empirical observation: a curious, unexplained pattern seen in real patients. Underlying mechanisms, unknown at first, are later mapped out in careful experiments. This “bedside to bench” pathway for discovery is less and less common —both because low-hanging fruit has been picked, and because doctors today have little time for observation. I’ll give a few examples of how artificial intelligence can help reboot this pathway: AI is a powerful engine for generating novel empirical observations in real-world data, many of them invisible to the human eye. Translating facts ‘discovered’ by AI into (i) improvements in clinical care and (ii) scientific discoveries are at the core of a new science of medicine, powered by data and computation.\n\n\nBiography\n\nZiad Obermeyer is Associate Professor and Blue Cross of California Distinguished Professor at UC Berkeley. He teaches at School of Public Health and was a founding member of the Berkeley–UCSF joint program in Computational Precision Health. His research uses machine learning to help doctors make better decisions, and help researchers make new discoveries by ‘seeing’ the world the way algorithms do. His work on algorithmic racial bias has been highly influential in shaping how health care organizations and policy makers hold AI accountable, from work with state Attorneys-General to testimony before the Senate Finance Committee. He is a cofounder of Nightingale Open Science and Dandelion Health, a Chan–Zuckerberg Biohub Investigator, and a Research Associate at the National Bureau of Economic Research. He was named one of the 100 most influential people in AI by TIME Magazine. Previously, he was Assistant Professor at Harvard Medical School, and he continues to practice emergency medicine in underserved communities.\n\n\n\n\n\n\n\nErin LeDell\nTitle TBD\n\n\n\nBiography\n\nDr. Erin LeDell is the Chief Scientist at Distributional, Inc. where she’s helping build an automated testing platform for statistical testing of AI applications. She is also the founder of DataScientific, Inc., an AI Advisory and Consulting firm specializing in the development and implementation of cutting-edge AI solutions. Previously, she was the Chief Machine Learning Scientist at H2O.ai, and founder of Women in Machine Learning and Data Science (WiMLDS) and co-founder of R-Ladies Global. Erin received her Ph.D. in Biostatistics from University of California, Berkeley and has a B.S. and M.A. in Mathematics."
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images/erin.jpg

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index.qmd

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**Reinventing medicine with AI**
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![](images/Z-Obermeyer-mucem-scaled.jpg)
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![](images/Z-Obermeyer-mucem-scaled-square.jpg)
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#### Abstract
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### Erin LeDell
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**Title TBD**
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![](images/erin.jpg)
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<details>
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<summary>Biography</summary>
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Dr. Erin LeDell is the Chief Scientist at Distributional, Inc. where she’s helping build an automated testing platform for statistical testing of AI applications. She is also the founder of DataScientific, Inc., an AI Advisory and Consulting firm specializing in the development and implementation of cutting-edge AI solutions. Previously, she was the Chief Machine Learning Scientist at H2O.ai, and founder of Women in Machine Learning and Data Science (WiMLDS) and co-founder of R-Ladies Global. Erin received her Ph.D. in Biostatistics from University of California, Berkeley and has a B.S. and M.A. in Mathematics.
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</details>
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