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[theory-seminar] Practice job talk

Kevin Tian kjtian at stanford.edu
Tue Mar 8 16:28:16 PST 2022


Hi all,

I will be giving a practice job talk on 3/15 at 11-12 AM PST -- you are all
invited, and I would really appreciate it if you can make it. The talk will
be virtual (since I'm not on campus right now), and closer to the date of
the talk, I will send out an anonymous feedback form and a Zoom link.

Thanks a lot in advance everyone! Here is the talk description.

Title: Iterative Methods and High-Dimensional Statistics
Abstract: Algorithmic primitives such as stochastic gradient descent,
discretized Langevin dynamics, regression, and clustering have emerged as
powerful workhorses enabling many recent advances in data science and
machine learning. These ubiquitous methods have well-understood analyses in
classical regimes; however, in prominent modern applications going beyond
these regimes, the theoretical guarantees of basic algorithms and their
analyses may often leave much on the table. In this talk, I examine how
tools originally developed in the fields of iterative methods and
high-dimensional statistics can be combined and reimagined to build a
modern theory of reliable, scalable, and accurate algorithm design. As case
studies, I will mainly focus on two lines of research I have pushed forward
in my Ph.D. work, spanning the modern algorithmic theories of robust
statistical estimation and high-dimensional sampling.

Cheers,
Kevin
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