Momchil Minkov

@momchilmm

Currently . All things photonic: crystals, inverse design, machine learning, topological stuff.

Vrijeme pridruživanja: svibanj 2019.

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  1. 30. sij
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    29. sij

    Integrated Photonics Technical Group is excited to host our first webinar! We are honored to have Prof. Shanhui Fan from as our speaker and he will discuss Nontrivial Topology Induced by Dynamic Modulations.

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  3. 25. sij

    Happy Friday night. That kid can drum.

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  4. 24. sij

    "Backprop is just the chain rule" is what I used to say when I had no idea what I was talking about.

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  5. 23. sij

    Pretty cool illustration. Also, reminds me of an unforeseen weakness Germans had with their tanks in WW2

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  6. 22. sij
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  7. 22. sij

    My slight issue with the Science article is calling the structures subwavelength. The wavelength in the material is ~500nm... There are multiple other papers that do the same thing of course, I just feel like we should, collectively, stop.

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  8. 22. sij

    Second-harmonic generation using a quasi-bound state in the continuum. Cool stuff. My recent paper is along the same lines, and we have some experimental results coming up!

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  9. 17. sij
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  10. 13. sij

    Not forcing any conclusions on anybody, just pointing out some conclusions could be drawn.

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  11. 13. sij

    Photonics Online Meetup streaming now at and around the world! Join us at Shriram SB33.

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    10. sij
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  13. 11. sij

    I never metamaterial I didn't like.

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  14. 10. sij

    And checkout the ongoing online (twitter-based!) poster session under Lastly, if you want to listen but can't join in person, just go to the site, register, and stream for yourself. It's free!

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  15. 10. sij

    January 13, 11-16:30h, Shriram SB33, and I will be live streaming the first all-online conference on photonics . Join us! The full program with many many interesting talks can be found here

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  16. 7. sij

    This is a great initiative-I'm sure that many people like me are worried about unnecessary traveling and its toll on the Earth (and our bodies). Hope to see much more of that! Shout out to the organizers

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  17. 7. sij
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  18. 3. sij
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  19. 2. sij

    And here's our most recent contribution along the same lines:

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  20. 2. sij

    This is not ML applied to "learn" physical models. This is using TensorFlow as a general-purpose differentiable programming library to write your physics code and efficiently compute gradients.

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