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Riyang Liu Profile
Riyang Liu

@liu_riyang

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Following
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Currently Postdoctoral Associate @YaleSPH @CCHYale, working on spatiotemporal exposure modeling of #AirPollution. He/him/his. #rstats.

New Haven, CT
Joined March 2022
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@AguGeohealth
AGU_GeoHealth
7 months
@AguGeohealth and @ametsoc Board on Environment and Health are excited to hold the 2025 Joint AGU/AMS Climate and Health Showcase. This virtual event will feature keynote talks, panel discussions, and engaging sessions on GeoHealth topics. Register at: https://t.co/2QgZkmaRsh
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@AguGeohealth
AGU_GeoHealth
1 year
#GeoHealth is excited to announce the inauguration of an annual retreat held alongside the #AGU conference, dedicated to forging human-centered solutions and actionable strategies to mitigate the impacts of #ClimateChange on #HumanHealth. register now!
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docs.google.com
We are excited to invite you to the inauguration of an annual retreat held alongside the AGU Annual Meeting, dedicated to forging human-centered solutions and actionable strategies to mitigate the...
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@janniyuval
janniyuval
1 year
New @nature paper: https://t.co/xL2BnRkTum NeuralGCM results (all are a "first"): 1) A differentiable hybrid atmospheric model 2) Competitive with ECMWF ensemble 3) Competitive with a GCRM in a year-long simulation 4) 40-year AMIP-like runs. Smaller bias than AMIP runs. 1/
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@ChenKai_yale
Kai Chen
2 years
Our new paper @EnvSciTech provides a generalizable case for estimating 40-years spatiotemporal-resolved PM2.5 estimates for life course or early life exposure to air pollution in the U.K. @liu_riyang @AGasparrini75 @YaleDeptEHS @YaleSPH @CCHYale πŸ‘‰ https://t.co/fGqb0tXXEZ
pubs.acs.org
Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-...
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@kareem_carr
πŸ”₯ Dr Kareem Carr πŸ”₯
3 years
GAMs are ML now??? OK I give up. Statistics is fake news. It’s all Machine Learning.
@AIatMeta
AI at Meta
3 years
Generalized Additive Models (GAMs) are fully interpretable ML models, unlike DNNs, but it's hard to make them efficient. We're sharing research on scaling GAMs to real world tasks w/o sacrificing accuracy or interpretability. https://t.co/9a9zaP4kdN https://t.co/1IxxVceeu2 [1/4]
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@YaleSPH
Yale School of Public Health
4 years
You can now watch Prof. Linda Birnbaum's presentation on environmental health's past, present, and future from the @YaleSPH Winslow Medal ceremony here:
@vasilisyale
Vasilis Vasiliou
4 years
Prof Linda Birnbaum, the recipient of the highest YSPH honor (Winslow Metal) gave a marvelous talk on the Past, Present and Future of Env Health. Thank you Linda for all your efforts to raise awareness about the Environment! ⁦@YaleSPH⁩ ⁦@YaleDeptEHS⁩ ⁦@NIEHS⁩
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