Monday - Friday
Apr
17 - 21
2023
12:00PM - 1:00PM
Video Playlist Day 1 Lecture Slides Day 1 Homework Day 2 Lecture Day 2 Homework Day 3 Lecture Notes Day 3.2 Lecture Notes Day 4 Lecture Notes
Jiaming Xu

To view full video playlist, click here.

Monday, April 17 to Friday, April 21, 10am-12pm

Location: See below*

Speaker: Jiaming Xu

Recent Results in Planted Assignment Problems

Abstract: Motivated by applications such as particle tracking, network de-anonymization, and computer vision, a recent thread of research is devoted to statistical models of assignment problems, in which the data are random (weighted) graphs correlated with the latent permutation. In contrast to problems such as the planted clique or stochastic block model, the major difference here is the lack of low-rank structures, which brings forth new challenges in both statistical analysis and algorithm design. In this short course, we will discuss a couple of prototypical problems in this research thread: the planted matching problem (linear assignment) and the network alignment problem (quadratic assignment). We will focus on both the information-theoretic and algorithmic aspects of these problems, introducing the needed mathematical machinery along the way and discussing various open problems.

Schedule:

This is a four-part course with an office-hour session, held 10am-12pm every day.

  1. Mon, Apr 17: Lecture 1, 10am-12pm, Skiles 006 (Coffee and snacks will be provided)
  2. Tue, Apr 18: Lecture 2, 10am-12pm, Groseclose 119 (Lunch will be provided)
  3. Wed, Apr 19: Office Hour, 10-11am, Groseclose 347
  4. Thu, Apr 20: Lecture 3, 10am-12pm, Groseclose 119 (Lunch will be provided)
  5. Fri, Apr 21: Lecture 4, 10am-12pm, Groseclose 402 (Coffee and snacks will be provided)

Bio: Jiaming Xu is an associate professor at the Fuqua School of Business at Duke University. He received a Ph.D. degree from UIUC in 2014, an M.S. degree from UT-Austin in 2011, and a B.E. degree from Tsinghua University in 2009, all in Electrical and Computer Engineering. His research interests include high-dimensional statistics, networks, information theory, convex and non-convex optimization, and queueing theory. He received a Simons-Berkeley Fellowship in 2016 and an NSF Career Award in 2022.