Ziawasch Abedjan

@ziamurai

Computer Scientist, Data Scientist, Researcher, Berliner

Berlin, Germany
Vrijeme pridruživanja: ožujak 2019.

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  1. 31. sij

    Encourage your students to submit their proposals to this year's VLDB PhD workshop. We have an excellent PC and as keynote speaker. Organized by and me

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    GI-Arbeitspapier in Zusammenarbeit mit soll bei der Ausgestaltung von Studien-, Aus- und Weiterbildungsangeboten helfen, die richtigen Themen im zu adressieren. von P. Liggesmeyer und M. Goedicke

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  3. proslijedio/la je Tweet
    7. sij

    It’s not too late to submit your application to join our group in Aalborg as assistant () or associate professor (). Application deadline: February 1st!

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  4. 1. sij

    It was cute when it started with I got my PhD and got married but became obnoxious when accomplished faculty talk about third house bought

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  5. 1. sij

    Why would you list your achievements and privileges of the last 10 years for the public? Who benefits from that? I guess academia is taking a great toll on all of us..

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  6. 31. pro 2019.
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  7. proslijedio/la je Tweet
    29. pro 2019.

    Same in tech. You can find lots of self-taught coders at big tech cos from majority backgrounds. But most of the black folk you will see are over-credentialed. "Harvard this, Howard that, MIT the other," types. Which is why "You don't *need* a CS degree!" is... complicated.

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    23. pro 2019.
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    18. pro 2019.

    Being a heavy tweeter, I mostly agree. However, a colleague confessed he left Twitter b/c it increased his anxiety & stress, seeing other papers accepted when his weren't, etc. Maybe it helps if we try to promote others, not just ourselves. Especially true if you're more senior!

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  10. 18. pro 2019.

    Curious how the scores would be for sigmod

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    i do my best to help out ... but i think graduate programs (especially masters-level programs) requiring so many rec letters massively contributes to inequity...most undergrads simply aren't able to get 3-5 substantive letters. why not require just 1-2? lessens burden on everyone

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    I disagree. It's difficult to understand and fix bias through algorithms for These are highly non-linear blackbox models. they amplify biases. many works show that trying to fix superficially is like putting lipstick on a pig. We need fresh thinking

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    6. pro 2019.

    People are biased. Data is biased, in part because people are biased. Algorithms trained on biased data are biased. But learning algorithms themselves are not biased. Bias in data can be fixed. Bias in people is harder to fix.

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    6. pro 2019.
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  15. 5. pro 2019.

    Gleich beim Erwartungen an die Persona Data Scientist im "KI" Zeitalter.

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  16. proslijedio/la je Tweet

    The endless conspiracy theories and denial of facts. The lies, hate and bullying of children who communicate and act on the science. All because some adults - terrified of change - so desperately don't want to talk about the This is hope in disguise. We're winning.

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  17. 22. stu 2019.
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    This is why most research is so ineffective and how you can make yours more effective.

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  19. proslijedio/la je Tweet
    16. stu 2019.

    well interesting, yet disappointing, take on 'IDEs' these days

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  20. proslijedio/la je Tweet
    29. lis 2019.

    Slides for Linux Systems Performance at USENIX and the updated observability diagram

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