Foundations of Data Science Boot Camp, V

This is the fifth (and final) entry to summarize talks in the “boot camp” week of the program on Foundations of Data Science at the Simons Institute for the Theory of Computing, continuing this post. On Friday, we heard talks from Ilya Razenshteyn and Michael Kapralov. Below, I link videos and provide brief summaries of their talks.

Ilya Razenshteyn — Nearest Neighbor Methods

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Foundations of Data Science Boot Camp, IV

This is the fourth entry to summarize talks in the “boot camp” week of the program on Foundations of Data Science at the Simons Institute for the Theory of Computing, continuing this post. On Thursday, we heard talks from Santosh Vempala and Ilias Diakonikolas. Below, I link videos and provide brief summaries of their talks.

Santosh Vempala — High Dimensional Geometry and Concentration

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Foundations of Data Science Boot Camp, III

 

This is the third entry to summarize talks in the “boot camp” week of the program on Foundations of Data Science at the Simons Institute for the Theory of Computing, continuing this post. On Wednesday, we heard talks from Fred Roosta and Will Fithian. Below, I link videos and provide brief summaries of their talks.

Fred Roosta — Stochastic Second-Order Optimization Methods

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Foundations of Data Science Boot Camp, II

 

This is the second entry to summarize talks in the “boot camp” week of the program on Foundations of Data Science at the Simons Institute for the Theory of Computing, continuing this post. On Tuesday, we heard talks from Ken Clarkson, Rachel Ward, and Michael Mahoney. Below, I link videos and provide brief summaries of their talks.

Ken Clarkson — Sketching for Linear Algebra: Randomized Hadamard, Kernel Methods

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Foundations of Data Science Boot Camp

I’m spending the semester at the Simons Institute for the Theory of Computing as part of the program on Foundations of Data Science. This was the first day of the “boot camp” week, which was organized to acquaint program participants with the key themes of the program. Today, we heard talks from Ravi Kannan and David Woodruff. Below, I link videos and provide brief summaries of their talks.

Ravi Kannan — Foundations of Data Science

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Recent Advances in Packing

Last week, I co-organized (with Joey Iverson and John Jasper) a special session on “Recent Advances in Packing” for the AMS Spring Central Sectional Meeting at the Ohio State University. All told, our session had 13 talks that covered various aspects of packing, such as sphere packing, packing points in projective space, applications to quantum physics, and connections with combinatorics. It was a great time! And after the talks, we learned how to throw axes!

What follows is the list of speakers and links to their slides. (I anticipate referencing these slides quite a bit in the near future.) Thanks to all who participated!

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Tight Frames and Approximation 2018

I just returned from an amazing workshop in New Zealand organized by Shayne Waldron. The talks and activities were both phenomenal! Here’s a photo by Emily King that accurately conveys the juxtaposition:

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A few of the talks gave me a lot to think about, and I wanted to take a moment to record some of these ideas.

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