The Data Analytics Laboratory investigates topics related to data analysis and organization at large scale. We are especially interested in machine learning, natural language processing and understanding, data mining and information retrieval. In all of these areas, the combination of well-informed theoretical models empowered by large-scale resources allows for exciting insights and applications.

Featured publications

Deep State Space Models for Unconditional Word Generation

  • authors F Schmidt, T Hofmann
  • published In NeurIPS'18: Conference on Neural Information Processing Systems , 2018


Hyperbolic Neural Networks

  • authors OE Ganea, G Bécigneul, T Hofmann
  • published In NeurIPS'18: Conference on Neural Information Processing Systems , 2018


Escaping Saddles with Stochastic Gradients

  • authors H Daneshmand, J Kohler, A Lucchi, T Hofmann
  • published In International Conference on Machine Learning (ICML), 2018

Latest news

18.09.2019

Artificial intelligence probes dark matter in the universe

Our collaboration with the Institute for Particle Physics and Astrophysics led to the developement of a new approach to the problem of dark matter and dark energy in the universe. The work was published in Physical Review D and is summarized on the ETH News website.

09.06.2019

AICosmo 2019 workshop

Thomas Hofmann and Janis Fluri gave talks at the Artificial Intelligence Methods in Cosmology Workshop held this summer at the Congressi Stefano Franscini (CSF). By bringing together researchers from Cosmology and Machine Learning, the workshop enabled a new level of connection between these two disciplines.

06.02.2019

AI challenges for companies

Are certain business models more suitable for AI companies and projects than others? Thomas Hofmann answers this topic and other related questions in his interview with Big Data Insider.

29.01.2019

ELLIS Society Founded

Top European Machine Learning Scientists join forces to establish European Excellence Network for Public Artificial Intelligence Research. More information on the initiative termed European Laboratory for Learning and Intelligent Systems (ELLIS) can be found here.

20.01.2019

Thomas Hofmann co-authored for an official statement of the German National Academy of Sciences

Thomas Hofmann contributed to the paper "Privatheit in Zeiten der Digitalisierung" published by the German National Academy of Sciences (publication in German only).

11.01.2019

Template Pictorial and Textual Representations Project funded by SNF

We are pleased to announce that our research proposal "Deep Learning for Generating Template Pictorial and Textual Representations" was approved by the Swiss National Science Foundation (SNF). The project concerns research on the design of novel algorithms that jointly process natural language and visual data focusing on deep learning methods. This is a joint project with the Language Intelligence and Information Retrieval research lab at KU Leuven. In this regard we warmly welcome our new team member Dario Pavllo, who will dedicate himself to this project.