Sherri Rose

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Sherri Rose

Sherri Rose

@sherrirose

Professor @Stanford | Computational Health Economics & Outcomes | Fair Machine Learning | Causality

Stanford 🌅 Katılım Haziran 2008
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Sherri Rose
Sherri Rose@sherrirose·
Honored to receive an ✨NIH Director's Pioneer Award✨ to develop a framework for the social impact of algorithms in health care! This work is at the intersection of machine learning, decision science, economics, policy & health equity. I'm excited to get started! #NIHHighRisk
NIH Common Fund@NIH_CommonFund

The @NIHDirector's Pioneer Award funds researchers from all career stages pursuing groundbreaking new ideas to tackle significant challenges in behavioral, #SocialScience, & #BiomedicalResearch. Meet this year’s awardees: bit.ly/hrhrpioneer2022. #NIHHighRisk

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Sherri Rose
Sherri Rose@sherrirose·
What does statistics bring to machine learning & AI? New piece w/@mark_vdlaan on why machine learning cannot ignore the lessons of maximum likelihood estimation arxiv.org/abs/2110.12112 Many ML algorithms aren't suited for statistical inference by having deviated from sieve MLEs
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Stanford University
Stanford University@Stanford·
How to find cause and effect in real-world randomness? A Tiny Lecture, #NobelPrize edition!
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Stanford HAI
Stanford HAI@StanfordHAI·
Congratulations to HAI faculty member @SherriRose for winning the Mortimer Spiegelman Award – the highest recognition for outstanding contributions to public health statistics. Read more about her work: shar.es/aWHa22
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StanfordHealthPolicy
StanfordHealthPolicy@StanfordHP·
Welcome to the Farm, Sherri Rose, an expert on statistical machine learning, AI and economics. "I love working on the computational health economics tools I develop because they may have a direct impact on individual lives in the health-care system." stanford.io/3865gLl
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Sherri Rose
Sherri Rose@sherrirose·
Our new @NBERpubs working paper is out! nber.org/papers/w26736 Fixing undercompensation for several groups in risk adjustment improved fairness for *many other groups and overall fit* We did this by bringing together: 1️⃣fair regression 2️⃣ML for variable selection 3️⃣reinsurance
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HMS HCP
HMS HCP@HMSHCP·
This summer, @sherrirose will receive the @PennCausal Mid-Career Award for achievements in the development and application of innovative causal inference methods. Dr. Rose will deliver an invited award lecture during the Causal Inference Summer Institute: cceb.med.upenn.edu/cci/2020-summe…
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JAMA
JAMA@JAMA_current·
At a minimum, a #machinelearning model should be reproduced, and ideally replicated, before it is deployed in a clinical setting ja.ma/35m6Aqj
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Sherri Rose
Sherri Rose@sherrirose·
Our paper on fair regression is now forthcoming in @Biometrics_ibs! Biometrics link onlinelibrary.wiley.com/doi/10.1111/bi… ArXiv arxiv.org/abs/1901.10566 Code github.com/zinka88/Fair-R… Discussed next steps needed to bring this work to practice at the recent Int’l Risk Adjustment Network meeting
Sherri Rose@sherrirose

Our new paper "Fair Regression for Health Care Spending" is out: arxiv.org/abs/1901.10566 We build fairness into the objective function for continuous outcomes & see large improvements in group undercompensation Coauthored w/PhD student Anna Zink Code: github.com/zinka88/Fair-R…

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Harlan Krumholz
Harlan Krumholz@hmkyale·
Nice talk by @sherrirose: Towards Standards in Machine Learning. And she is emphasizing the need for teams that cross disciplines, leveraging experience from different areas. Understand applied problem. Respect the analysis. Think about the application.
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