You can download a preliminary programme here
Slides from speakers:
Marco Fraccaro (Website)
I am Chief Scientific Officer at Unumed, where I develop machine learning algorithms for healthcare applications. I obtained a PhD in machine learning at the Technical University of Denmark (DTU), working on deep generative models. My main research interests lie at the intersection of deep learning and probabilistic modelling, focusing in particular on the development of deep generative models for sequential data and semi-supervised learning where deep neural networks are used to define flexible probably distributions. In the past, I have also worked on recommender systems and several Bayesian Inference techniques such as variational inference, Markov Chain Monte Carlo methods and sequential Monte Carlo methods.
Associate Professor Søren Hauberg (Website)
Søren Hauberg is an Associate professor in the Section for Cognitive Systems at the Technical University of Denmark.
His research interest lie in the span of geometry and statistics. He develops machine learning techniques using geometric constructions, and works on the related numerical challenges. He is particularly interested in non-parametric smooth metric learning.
Assistant Professor Wilker Aziz (Website)
I am an assistant professor in computational linguistics at the Institute for Logic, Language and Computation working on machine learning for natural language processing. Some of the problems I’ve looked into are machine translation, word alignment, textual entailment, paraphrasing, and question answering. My interests sit at the intersection of disciplines such as formal languages, machine learning, approximate inference, global optimisation, and computational linguistics.
Recently, I’ve developed quite an interest in Bayesian deep learning. In particular, I’m developing probabilistic neural network models that reason with and induce forms of discrete generalisation such as trees and graphs.