Shadab Khan Profile
Shadab Khan

@skhanshadab87

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Team Lead - Healthcare AI at M42.

Abu Dhabi, UAE
Joined September 2009
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@skhanshadab87
Shadab Khan
1 year
Excited to share Med42, by @M42Health - our open-access clinical LLM. It scores well on several benchmarks (USMLE - 72%, 0-shot), and available on @huggingface. Beyond benchmarks, we're on course to evaluate it on safety + real-world use cases. Please reach out for collaboration!
@CleChristophe
Clément Christophe
1 year
Today @M42Health, we are releasing Med42: the best open-source clinical LLM to date, closing the gap with GPT-4 and MedPaLM-2 ! 🔥🩺 It is available on @huggingface 🤗:
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@skhanshadab87
Shadab Khan
7 days
RT @BrandesNadav: New preprint claims that most existing DNA language models perform just as well with random weights, suggesting that pret…
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@skhanshadab87
Shadab Khan
1 month
RT @BiologyAIDaily: Genomic Foundationless Models: Pretraining Does Not Promise Performance 1. This study challenges the paradigm of pretr…
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@skhanshadab87
Shadab Khan
1 month
RT @kirill_vish: 🧬 Genomic Foundation Models (GFMs) rely on costly pretraining just like models in NLP & vision. However, is unsupervised…
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@skhanshadab87
Shadab Khan
5 months
RT @OpenlifesciAI: 🚨 Medical AI Research alert! 🚨 Can we evaluate LLM's medical capabilities across multiple modalities and specialties?…
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@skhanshadab87
Shadab Khan
7 months
@paimadhu UAE! Great flight connectivity all over the world, straightforward visa process, availability of large convention centers!
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@skhanshadab87
Shadab Khan
10 months
Sila is amazing to work with!
@silakurugol
Sila Kurugol
10 months
Postdoc position available @Harvard on medical image analysis using deep learning! Join us to develop cutting-edge tech that will revolutionize patient care. #DeepLearning #MedicalImaging #Postdoc #Harvard P.S. Help us spread the word!
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@skhanshadab87
Shadab Khan
1 year
We also are hosting at all levels for MLE and applied scientists! Please DM me of your interested!
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@skhanshadab87
Shadab Khan
2 years
@Michael_D_Moor @_akhaliq Interesting work! I particularly appreciated the effort on deduplicating for eval and finding leakage! Quick que: I noted the number (4721) and categories of the books (fig 8) but the source was missing. Would you mind mentioning the source? Will MTB released at some point?
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@skhanshadab87
Shadab Khan
3 years
RT @MaartenvSmeden: NEW PAPER Our review of guidance documents for AI based prediction models in healthcare is out. Guidance *not just* fo…
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@skhanshadab87
Shadab Khan
3 years
@BrianPogue19 @DartmouthMP Good to see the familiar faces and a familiar room! Happy holidays Brian!
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@skhanshadab87
Shadab Khan
3 years
RT @DrHughHarvey: Clinical validation for CE marking, a thread 🧵[1/5]... Many startups get confused about the requirements for clinical va…
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@skhanshadab87
Shadab Khan
3 years
Best thing I read today!
@bneyshabur
Behnam Neyshabur
3 years
Totally agree! Anyone screening applications and any applicant thinking their CV is not representative of their skills/potentials, I think you might want to read the story of my own PhD application in this thread: 1/
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@skhanshadab87
Shadab Khan
3 years
RT @devendratweetin: Late to the party but happy to share our #NeurIPS2021 work:led by @matej_zecevic where we cons…
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@skhanshadab87
Shadab Khan
3 years
Huazhu is n outstanding researcher and amazing human being - highly recommended!
@Hz_MedAI
Huazhu FU
3 years
[We're hiring!!] We have several Research Scientist (AI for Healthcare) Positions available at IHPC, A*STAR, Singapore. If you are interested in working with us, please send your CV to me. Also welcome to share it with your colleagues! Thx. JD link:
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@skhanshadab87
Shadab Khan
3 years
RT @lpachter: It's time to stop making t-SNE & UMAP plots. In a new preprint w/ Tara Chari we show that while they display some correlation…
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@skhanshadab87
Shadab Khan
3 years
@lpachter @neurobongo Intriguing results! Some readers of this paper might benefit from a reference to this 2016 @distillpub article (in addition to [3,6] ) which discussed misleading nature of tsne embeddings and provides an accessible (+interactive) introduction.
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@skhanshadab87
Shadab Khan
3 years
@karpathy At loss plateau, the model could have overfit. When you drop the LR by 10X, would you take the model at the end of the plateau, or would you select the model with best val performance (from anywhere on the training curve). "ReduceLROnPlateau" in PT does the former. Thoughts?
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@skhanshadab87
Shadab Khan
4 years
This from someone who was previously a lecturer at Harvard (and whose workshop lectures that I attended were extremely good). Advise to students - when in doubt, find and talk to good mentors.
@rahuldave
Rahul Dave
4 years
We trained two cohorts of incredible students in India. Our best students there were totally on par with the best I trained at Harvard. And they were NOT all from eminent Indian technical Universities like the IITs.
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@skhanshadab87
Shadab Khan
4 years
RT @kdpsinghlab: Thank you for folks who have shared or commented on our paper. I know the paper is being used by some to dunk on Epic. Rat…
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