MATJ5123 MA4: Gaussian Multiplicative Chaos, Quasiconformal Techniques and Discrete Models (JSS34) (2 cr)

Study level:
Postgraduate studies
Grading scale:
Pass - fail
Language:
English
Responsible organisation:
Faculty of Mathematics and Science
Curriculum periods:
2025-2026

Description

This course will investigate recent developments in three interlinked fields: Gaussian multiplicative chaos, conformal weldings and discrete models including discrete complex analysis. Their study brings together a rich set of techniques from probability, harmonic analysis, quasiconformal analysis and fractal geometry. The goal of this course is to showcase some of the most exciting recent developments, focusing on the interplay of different techniques and allow the participants a chance to learn a new topic at the forefront of research. The participants will be assigned a paper to read before the course, will write a short summary and then give a 2x45 minutes lectures on the topic. There will be opportunities to receive remote individual guidance before the in person course. The course participants present 15 papers on gaussian multiplicative chaos, quasiconformal techniques and discrete models.

Learning outcomes

Students learn a new topic from reading and discussing contemporary research papers, and an opportunity to network with early career researchers.

Description of prerequisites

Students from a variety of backgrounds are invited, but the following are helpful: Probability theory, Complex Analysis.

Completion methods

Method 1

Description:
Oral presentation and written summary of read literature. Before the in person course, students can meet remotely with their mentor to discuss their assigned paper.
Evaluation criteria:
Pass/Fail
Time of teaching:
Period 1
Select all marked parts
Parts of the completion methods
x

Participation in teaching (2 cr)

Type:
Participation in teaching
Grading scale:
Pass - fail
Evaluation criteria:
<p>Pass/Fail</p>
Language:
English
Study methods:

Oral presentation and written summary of read literature. Before the in person course, students can meet remotely with their mentor to discuss their assigned paper.

Teaching