Chris Holmes
@cholmesuk
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Prof U of Oxford, Director for Health @turinginst. Likes statistics, causal inference, and machine learning addressing real-world problems in health
Joined April 2011
*3* research associate/fellow jobs at Uni of Manchester, work with @NielsPeek @glen_martin1 me and colleagues at Uni of Oxford and NovoNordisk: Advancing understanding of Multimorbidity using Statistical/Machine Learning
jobs.manchester.ac.uk
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funded research opportunities in modern approaches to missing data
turing.ac.uk
We are inviting researchers to participate in a virtual workshop on the topic of structured missingness, funded by the Turing-Roche strategic partnership.
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New £2m funding call for advanced analytics into #COVID19
@HDR_UK and the Turing launch a new joint funding call as part of the National Core Studies into COVID-19
turing.ac.uk
The new £2 million joint funding call to enhance un
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exciting new programme in AI for multi-morbidity
turing.ac.uk
The NIHR (National Institute for Health Research)
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The Campion lecturer at #RSS2021Conf is Bin Yu - who'll talk about 'Veridical Data Science' - the practice of responsible data analysis and decision-making - next Tuesday at 5pm (in-person and livestreamed) https://t.co/R9F01Vz40J
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check out this blog post (from a couple of my students) on SHAP values and their statistical interpretation. How local are local AI explanation methods?
towardsdatascience.com
Without understanding the mathematical expressions of local explanation methods, their attributions can be counterintuitive. This blog post...
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📣NEWS: The Alan Turing Institute launches a strategic partnership with @Roche to generate insights into #disease, patient, and outcome heterogeneity using advanced #analytics Read more: https://t.co/eX22BzRQPw
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Bayesian prediction with guaranteed coverage (calibration) obtainable as a byproduct from MCMC. We use conformal inference and "add-one-in" importance sampling
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Come work with us on causal inference -- an exciting postdoctoral position in Oxford as part of a wider collaborative network
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happening today 4pm (UK time).....should be great!
I'm really looking forward to this event -- with a focus on data science enabled policy decisions in the pandemic response
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I'm really looking forward to this event -- with a focus on data science enabled policy decisions in the pandemic response
SEMINAR: Albert Ko, Professor of Epidemiology & Medicine and Department Chair (@YaleSPH @YaleMed) will discuss the challenges in developing data-driven #policy during the #COVID19 pandemic. Friday 21 May, 16:00-17:00 Register now for this free event: https://t.co/ga68k8uVTa
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This looks like an excellent new seminar series with a fantastic first speaker! "Cambridge BSU Lecture in Biomedical Data Science - Prof Xiao-Li Meng" https://t.co/TrNnbb7boq
@EventbriteUK
eventbrite.co.uk
The MRC Biostatistics Unit are delighted to introduce our new flagship lecture series on biomedical data science
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I am recruiting an Associate Director for our @HDR_UK-@turinginst PhD Programme in Health Data Science. If you have innovative, potentially experimental ideas for doctoral research training, see further information here:
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It was great to be part of this -- applying causal inference to understand the effectiveness of the NHS COVID-19 App
Now peer-reviewed in Nature, “The Epidemiological impact of the NHS COVID-19 App”. Here’s a quick rundown of results & what they mean. We estimated that the app prevented several hundred thousand cases from arising. The app works. https://t.co/aR5UpbI5dm 1/n
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Today 4-5pm -- world leading epidemiologist Marc Lipsitch (Harvard T.H. Chan School of Public Health) to deliver JBC/Turing/Royal Statistical Society distinguished lecture -please register and share
turing.ac.uk
In this seminar, Professor Lipsitch will discuss the challenges of understanding COVID-19 transmission, clinical course, and prevention.
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Great to see the first public output from the Turing-RSS-JBC stats modelling and ML Lab!
The Turing-RSS Statistical Modelling and ML Lab has made openly available the code underpinning the nowcasting model for the no. of positive daily Covid-19 swabs. The model takes into account the data processing and reporting lag, and aids decision making.
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We at the @bdi_oxford @OxfordMedSci are thrilled to be partnering with @broadinstitute #schmidtcenter together with @turinginst @HDR_UK & many others to collaborate to understand the programs of life through research at the interface of biology and machine learning #excitingtimes
We are thrilled to announce the launch of the Eric and Wendy #SchmidtCenter. Merging biology and machine learning to understand the programs of life, the Center will enable a new field of interdisciplinary research aimed at improving human health. https://t.co/CuPENZPDzQ
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this is going to be exciting!!
We are very much looking forward to this tremendous collaboration. Welcome, #SchmidtCenter.
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Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?
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