Statistics, Optimization and Machine Learning Seminar

Fall 2025 Update: the seminar is back!

Talks are mostly weekly, but the seminar is back to being an official CU seminar course for graduate students, so there are occasionally weeks with no talks.

Quick info

When there is a talk, we will meet Wednesdays 3:35-4:25pm MT in ECCR 257 (Engineering Center); this is the Newton Lab.

To sign up for the mailing list and receive announcements about talks (you do not need to be enrolled in the seminar), visit our google groups website and add yourself to the group

The seminar is organized by Cooper Doe (APPM PhD student), and technically Stephen Becker (APPM), but please direct questions/concerns to Cooper

The seminar is always open to the public! 

You do not have to be enrolled in the class ... but if you are enrolled in the class, for a passing grade, you must:

• 1. attend all the talks (missing an occasional talk is permissible if you have a valid reason, so email the instructor), and

  2. participate in journal clubs (please ask Cooper about this if you're unsure what it entails)

Upcoming Talks

SPEAKER: Francois Meyer (APPM)

TITLE: The Spectral Barycentre of a Set of Graphs with Community Structure

TIME: 3:35 PM, Wednesday, 24 September 2025

PLACE: ECCR 257

ABSTRACT: The notion of barycentre graph is of crucial importance for machine learning algorithms that process graph-valued data. The barycentre graph is a "summary graph" that captures the mean topology and connectivity structure of a training dataset of graphs.  The construction of a barycentre requires the definition of a metric to quantify distances between pairs of graphs. In this work, we use a multiscale spectral distance that is defined using the eigenvalues of the normalized graph Laplacian. The eigenvalues – but not the eigenvectors – of the normalized Laplacian of the barycentre graph can be determined from the optimization problem that defines the barycentre. In this work, we propose a structural constraint on the eigenvectors of the normalized graph Laplacian of the barycentre graph that guarantees that the barycentre inherits the topological structure of the graphs in the sample dataset.