[Theory] Re: [Theory Lunch] Sidhanth Mohanty, Wednesday 5/11 12:30pm-1:30pm, JCL 390.

[Olga Medrano Martin del Campo]

Dear all,

Thanks a lot for coming to Sidhanth's talk today! It was great.

Someone left their Mac charger in the room. You can find it with Jose in the second floor desk.

Best wishes,
Olga


________________________________
From: Adela DePavia <adepavia at uchicago.edu>
Sent: Monday, May 9, 2022 11:04 AM
To: theory at mailman.cs.uchicago.edu <theory at mailman.cs.uchicago.edu>
Cc: Olga Medrano Martin del Campo <omedranomdelc at uchicago.edu>; Sidhanth Mohanty <sidhanthm at cs.berkeley.edu>
Subject: Re: [Theory Lunch] Sidhanth Mohanty, Wednesday 5/11 12:30pm-1:30pm, JCL 390.

Reminder: happening today in one hour!
________________________________
From: Adela DePavia
Sent: Monday, May 9, 2022 10:02 AM
To: theory at mailman.cs.uchicago.edu <theory at mailman.cs.uchicago.edu>
Cc: Olga Medrano Martin del Campo <omedranomdelc at uchicago.edu>; Sidhanth Mohanty <sidhanthm at cs.berkeley.edu>
Subject: [Theory Lunch] Sidhanth Mohanty, Wednesday 5/11 12:30pm-1:30pm, JCL 390.

Date: May 11th, Wednesday
Time: 12:30pm CT
Location: JCL 390

Speaker:  Sidhanth Mohanty

Title: Testing thresholds for sparse random geometric graphs

Zoom: [link<https://uchicago.zoom.us/j/93265734038?pwd=SkRTSTF3a0lOQVg4MWdxVS9Lb2tPdz09>]

Abstract:  In the random geometric graph model, we identify each of our vertices with an independently and uniformly sampled vector from a high-dimensional unit sphere, and we connect pairs of vertices whose vectors are sufficiently close.

A fundamental question is: when is a random geometric graph a faithful model for its underlying geometry?  As the dimension grows relative to the number of vertices, the edges in the graph become increasingly independent, and the underlying geometry becomes less apparent.  This talk will cover some recent progress on this question: we show that in sparse random geometric graphs, if the dimension is at least polylogarithmic in the number of vertices, then the graphs are statistically indistinguishable from Erdős-Rényi graphs, i.e. the underlying geometry disappears.

Based on joint work with Siqi Liu, Tselil Schramm, and Elizabeth Yang (https://arxiv.org/abs/2111.11316).

COVID Policy: As per university policy, masking is not currently required for in-person attendance. Please note that we will have fully masked and social-distanced tables available to accommodate any attendees who would prefer such arrangements. Please contact us if you have any questions or feedback.

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