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


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.


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.


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).


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.


NZZ Interview with Thomas Hofmann

The Neue Zürcher Zeitung conducted an interview with Thomas Hofmann on Artificial Intelligence and its implications. The arcticle is available in German "Oft entscheiden Menschen sehr schlecht".


Digital Shapers 2017!

Thomas Hofmann has been listed as a top 100 Digital Shaper in Switzerland 2017


A.I., Genie in the Bottle?

Thomas Hofmann has presented a talk on "A.I., Genie in the Bottle?" at the SwissRe ETH Risk Center Workshop on Autonomous Decision Making.


ACM SIGIR Test of Time Award

Thomas Hofmann’s 1999 paper "Probabilistic Latent Semantic Indexing" has been awarded the test of time award by ACM SIGIR, see here. It has received more than 5000 citations up to date.


Collaboration with CSEM

The Institute for Machine Learning at ETH Zurich is partnering with CSEM for exploring exciting cutting edge research for modelling temporal processes. This collaboration will lead to the joint supervision of a PhD student starting in the fall 2017.


Deep Learning for Observational Cosmology funded by SDSC

We are pleased to announce that our research proposal "Deep Learning for Observational Cosmology" was accepted by the Swiss Data Science Center (SDSC). This interdisciplinary project is a joint collobration between the Data Analytics Lab and the Cosmology group at ETH led by Prof. Alexandre Refregier. We will address some of the most important problems in the field of Observational Cosmology by capitalising on the latest developments in the field of deep neural networks.


AMiner Influential Scholars

Thomas Hofmann has been recognized on the AMiner most influntial scholar award list of 2016 for his contributions to Machine Learning and Information Retrieval.


Conversational Agents Project funded by SNF

Our SNF research proposal „Conversational Agents for Interactive Communication“ was approved for 36 months funding. The project will combine many areas of Machine Learning and Natural Language Processing to a system tackling the challenge of natural understanding.


Facebook Research GPU Partnership Program

Facebook AI Research selected the Data Analytics Lab to be part of their GPU Partnership Program. They will be distributing 22 high-powered GPU servers to 15 research groups across 9 European countries. Further details are available here.


Joint collaboration with Microsoft Research

We are pleased to announce that our group has been awarded funding for a research project with Microsoft Research. Our research project will focus on the development of generative probabilistic graphical models for modelling complex data such as text and images.


Carsten Eickhoff speaking at Swiss eHealth Summit

Carsten will present the Data Analytics Lab's line of work dedicated to patient-centric natural language processing and information retrieval techniques in clinical settings at the Swiss eHealth Summit 2016.


Rolf Jagerman speaking at Spark Summit Europe

As one of the invited speakers to this year's Spark Summit Europe, Rolf Jagerman will present Glint: An Asynchronous Parameter Server for Spark. The system was designed in the course of Rolf's MSc. thesis project in the Data Analytics Lab.


And the Qualcomm Fellowship 2016 goes to...

We are very pleased to announce that our PhD student Jason Lee was awarded a Qualcomm Fellowship 2016! Jason joined the lab in November 2015 and currently works on a unified neural language model for morphology grammar and coherence. Each winner will be awarded a 40,000 USD fellowship and receive mentorship from Qualcomm engineers.


Best Text Sentiment Analysis

Our lab's master students Jan Deriu and Maurice Gonzenbach won the 2016 SemEval text sentiment classification competition, placing first out of 34 teams from 24 countries. Maurice and Jan are supervized by Fatih Uzdilli, Valeria De Luca, Aurelien Lucchi and Martin Jaggi.


ACM SIGIR Best Paper

Carsten Eickhoff, Sebastian Dungs and Vu Tran receive an ACM SIGIR Best Paper Award honorable mention for their paper 'An Eye-tracking Study of Query Reformulation'.