David Yang

@davidkmyang

Protein research in the Marks lab ; Harvard CS+Bio ‘20. prev

Cambridge, MA
Vrijeme pridruživanja: rujan 2018.

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  1. 13. pro 2019.

    “Found data is not enough.” Generating the right, appropriate data is key to building better models, can’t agree more

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  2. 11. pro 2019.

    Really excited to see all the cool posters, come check it out! Drop by my poster too to say hi :)

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  3. 10. pro 2019.

    Folks at NeurIPS, looking for something to do after the poster session tomorrow? Come check out my friend 's recital tomorrow evening!

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  4. proslijedio/la je Tweet
    10. pro 2019.
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  5. 2. pro 2019.
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  6. 29. stu 2019.
    Prikaži ovu nit
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  7. 28. stu 2019.

    congrats !

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  8. 28. stu 2019.

    "Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design" Very cool work in an important area. Loving fig4b

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  9. 22. stu 2019.

    Just finalized itinerary for ! Excited to meet everyone at and . I will be presenting some preliminary work on using generative models for codon optimization

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  10. 21. stu 2019.

    Super cool application of Cas13 - this time for imaging!

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  11. 21. stu 2019.

    and will likely keep growing. Wonder what enabling techs can add value for these rapidly emerging modalities

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  12. proslijedio/la je Tweet
    18. stu 2019.

    Just published - our opinion piece in on how can revolutionize and drive the next - and what we need to do to make it happen. Output from our @cshlbnbury meeting

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    4. stu 2019.
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  14. 22. lis 2019.

    huge potential for modern bioscience in consumer tech/Agtech/even beauty tech, beyond therapeutics

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  15. 21. lis 2019.
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  16. proslijedio/la je Tweet
    10. lis 2019.

    Excited to share our latest work, where we use Cas13 as a programmable antiviral against influenza and other mammalian viruses

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  17. 3. lis 2019.

    Another exciting addition to the generative model + protein design set of works

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  18. 28. kol 2019.
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  19. proslijedio/la je Tweet
    19. lip 2019.

    Excited to share our work on TAPE (Tasks Assessing Protein Embeddings), where we assess the state of self-supervision for proteins! 📝 💻 w/ John Canny 1/8

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    19. lip 2019.

    This is a beautiful paper. A new language pretraining method that achieves compelling improvements over BERT. Large jumps on a number of benchmarks. Uses a clever permutation invariant autoregressive formulation plus Transformer-XL for handling long sequences

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