Skip to main content
Cornell university
Cornell Statistics and Data Science Cornell Statistics and Data Science
  • About Us

    About Us
    Cornell's Department of Statistics and Data Science offers four programs at the undergraduate and graduate levels. Undergraduates can earn a BA in statistical science, social...

    Welcome to the Department of Statistics and Data Science
    History
    Facilities
    Statistics Graduate Society
    Recently Published Papers
  • Academics

    Academics

    Undergraduate
    PhD
    MPS
    PhD Minor in Data Science
    Courses & Course Enrollment
  • People

    People

    Faculty
    Field Faculty
    PhDs
    Emeritus Faculty
    Academic Staff
    Staff
    Research Areas of Expertise
    Statistical Consultants
  • News and Events

    News and Events

    Events
    News
  • Resources

    Resources

    Professional Societies and Meetings
    Affiliated Groups
    Career Services
    Cornell Statistical Consulting Unit
  • Alumni

    Alumni
    Cornell's Statistics and Data Science degrees prepare students for a wide variety of careers, from academia to industry.  See the After Graduation page for a general overview of...

    Alumni Profiles

Search form

You are here

  1. Home 
  2. Events 
  3. Statistics Seminars

Statistics Seminar Speaker: Sidhanth Mohanty, 09/14/2022

Event Layout

Wednesday Sep 14 2022

Statistics Seminar Speaker: Sidhanth Mohanty, 09/14/2022

4:15pm @ 301A Malott Hall
In Statistics Seminars

Sidhanth Mohanty is a PhD student in the Theory Group at UC Berkeley, advised by Prasad Raghavendra. Sidhanth is interested in average case algorithms & complexity, random matrix theory, and spectral graph theory.

Talk: Testing thresholds for sparse random geometric graphs

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.

Event Categories

  • Statistics Seminars
  • Special Events

Image Gallery

Sidhanth Mohanty
  • Home
  • About Us
  • Contact Us
  • Careers
© Cornell University Department of Statistics and Data Science

1198 Comstock Hall, 129 Garden Ave., Ithaca, NY 14853

Social Menu

  • Facebook
  • Twitter
  • YouTube
Cornell Bowers CIS College of Computing and Information Science Cornell CALS ILR School

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact web-accessibility@cornell.edu for assistance.