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[theory-seminar] Practice job talk

Kevin Tian ktian6 at gmail.com
Mon Mar 14 14:02:15 PDT 2022


Hi,

Just wanted to send a reminder that this is happening tomorrow at 11 AM PST.

Zoom link:
https://stanford.zoom.us/j/92359543849?pwd=MjdCSFl1bkRVNzBYYzZEajRQK3NDZz09
Password: 124075

Anonymous feedback form:
https://docs.google.com/forms/d/e/1FAIpQLSeTCnGeGMRsh_xPiW4Cg_vk96feR3DeBfTbamz6KO9Q0yA88Q/viewform

Thanks,
Kevin

On Tue, Mar 8, 2022 at 4:28 PM Kevin Tian <kjtian at stanford.edu> wrote:

> 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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