Hausdorff School Algorithmic Data Analysis

We have decided to reschedule the Hausdorff school “Algorithmic Data Analysis: to next year (2021). Once the new dates have been decided, this webpage will be updated.

Venue: Lipschitz Lecture Hall, Mathematics Center, Endenicher Allee 60

Organizers: Anne Driemel, Melanie Schmidt

Confirmed lecturers:

  • Ken Clarkson (IBM Research, US)
  • Ioannis Emiris (University of Athens, Greece)
  • Robert Krauthgamer (Weizmann Institute, Israel)
  • David Mount (University of Maryland, US)
  • Jeff Phillips (University of Utah, US)
  • Ruth Urner (York University, Canada)

In this Hausdorff school we study algorithmic aspects of foundational methods to learn and generalise from data. While the nexus is algorithmic, this area of research is a rich and vibrant field within theoretical computer science which draws from deep connections to statistics, geometry, and combinatorics.

This Hausdorff School is intended for motivated graduate or postdoctoral students of mathematics or computer science. Leading experts discuss geometric and probabilistic approximation techniques and their connections to learning theory. Each invited speaker gives a mini-course spanning three lectures ranging from introductory material to advanced topics and current research.