_hylandSL - not here

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_hylandSL - not here

_hylandSL - not here

@_hylandSL

machine learning for healthcare - principal researcher at microsoft research in cambridge UK 🌻 she/her @hylandsl.bsky.social

Cambridge, England Katılım Temmuz 2014
379 Takip Edilen2.3K Takipçiler
_hylandSL - not here
_hylandSL - not here@_hylandSL·
if your immediate thought was to ask "what exactly do you mean by biomedical discovery?" you are double-invited to apply, because we care about details
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
We needed a new metric to support grounded report evaluation, but it was already evident when we were doing MAIRA-1 (arxiv.org/abs/2311.13668) that even for text-only evaluation, existing metrics miss things we care about. RadFact targets correctness and completeness directly!
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
Perks of Cambridge (UK): - Original Cambridge - Near but not actually in London - Almost entirely flat terrain ideal for cycling - Option to peer at beautiful university buildings through their railings - Driest part of England (still rains >100 days/year)
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
We are hiring a senior researcher in ML for healthcare at MSR Cambridge (UK)! The position is in my team, so if you get it you will work with me (is this a pro or a con? do not answer). Focus is multimodal (~vision-language) models for radiology! Link: jobs.careers.microsoft.com/global/en/job/…
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
(that said, not every internship produces a paper. That's okay - not everything needs to be published, and sometimes/often we do things bigger than 3 month project!)
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
Last note: this paper is an outcome of @hardy_qr's internship last summer. If you're interested in a research internship with us on machine learning in health/medicine, keep an eye on the MSR careers page (microsoft.com/en-us/research…) or wait for me to tweet more about it.
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_hylandSL - not here
_hylandSL - not here@_hylandSL·
Our #TACL paper on Compositional Zero-Shot Domain Transfer is now out! direct.mit.edu/tacl/article/d… We show that training on general-domain task data (say, NLI) and in-domain unstructured data (say, radiology reports) enables in-domain task capability (radiology NLI)!
_hylandSL - not here tweet media
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