Bridging the gap between academic research and at-scale development

Currently, there is a significant gap between specialized academic research on deep learning and the at-scale application of these concepts to solving real problems. At Merantix, our goal is to aggregate concepts and technical research and use them to create scalable and relevant deep-learning solutions. In order to reach these goals, our team is closely connected to researchers at major academic institutions, with whom we collaborate on various topics. We recognize the importance of bringing groundbreaking research on deep learning to life and we strive to realize with our constantly evolving products and solutions.

Applied Research Topics

Reinforcement Learning

How software agents ought to take actions in an environment so as to maximize a suitable notion of cumulative reward.

Bayesian Deep Learning

Provides a deep learning framework which can also model uncertainty. BDL can achieve state-of-the-art results, while also understanding uncertainty.

Introspective Analysis of Neural Networks

Debugging neural networks can be challenging, even for experts. With millions of parameters involved, even one small change can cause errors. At Merantix, we develop methods to visualize neural nets to make this process smoother.

Continuous Learning

Learning continuously and adaptively about the external world and enabling the autonomous, incremental development of ever more complex skills and knowledge.

Neural Network Compression

Compression of deep neural networks for memory-efficient, compact feature representations becomes a critical problem when these networks are deployed on resource-limited platforms.

Label Noise

Well-annotated datasets can be prohibitively expensive and time-consuming to collect. Our research explores the usage of larger albeit noisier datasets that can be more easily obtained.

Modularization of ML Pipelines

Our team is experimenting with the modularization of machine-learning pipelines in order to achieve maximum efficiency throughout complex processes.

Improving Experimentation Cycles

Experimentation is a crucial part of our work at Merantix. We are constantly improving experimentation cycles to be able to run more and more experiments.

Initiator and Member of

Horizon 2020 (biggest EU Research and Innovation programme ever)

Research Member of SECREDAS Project

Merantix was selected by the European Commission to take part in a Horizon 2020 project on Cyber Security for Cross Domain Reliable Dependable Automated Systems (SECREDAS). Within SECREDAS Meratnix is a core contributor developing robust and secure image segmentation algorithms, that function reliably in corner-case situations as well as when faced with real-world adversarial attacks. Horizon 2020 is the financial instrument implementing the Innovation Union, a Europe 2020 flagship initiative aimed at securing Europe's global competitiveness. Seen as a means to drive economic growth and create jobs, Horizon 2020 has the political backing of Europe’s leaders and the Members of the European Parliament. The goal is to ensure Europe produces world-class science, removes barriers to innovation and makes it easier for the public and private sectors to work together in delivering innovation.

VDA (German Association of the Automotive Industry)

Member of the VDA Lead Initiative for Safeguarding Autonomous Vehicles

Merantix is a member of the VDA Lead Initiative on safeguarding autonomous vehicles, which aims to build both technology and industry standards to enable the scaled development and deployment of autonomous systems. The German federal government of has set the goal of ensuring Germany’s pioneering role in self-driving technology. Working closely with all major German OEMs and Tier 1 suppliers, Merantix is contributing to make detection and segmentation algorithms robust. Furthermore we are collaborating to develop semantics and standards for safe autonomous vehicles as well as concepts for scalable testing environments.


Merantix Fellows

Our Fellowship program is an opportunity for the brightest minds in deep learning from across the world to collaborate and bridge the gap between academia and industry. The goal is that this mutually beneficial relationship between Merantix and our Fellowship members will help accelerate deep learning research and apply it to real-world use cases.

  • Peter Neher
    Peter Neher KIT, DKFZ
  • David Dohan
    David Dohan Princeton University, now @ Google Brain
  • Martin Engelcke
    Martin Engelcke University of Oxford
  • Christopher Jones
    Christopher Jones University of Oxford, Now @ HSBC
  • Paul Jaeger
    Paul Jaeger KIT, DKFZ
  • Markus Wulfmeier
    Markus Wulfmeier University of Oxford
  • Sam Shleifer
    Sam Shleifer Yale University, now @ Kensho
  • Jonas Koehler
    Jonas Koehler University of Amsterdam, MPI
  • Torsten Reil
    Torsten Reil University of Oxford, Founder NaturalMotion
  • Simon Kohl
    Simon Kohl KIT, DKFZ, DeepMind
  • Josh Chen
    Josh Chen Princeton University, now Founder @ Basis
  • Louis Kirsch
    Louis Kirsch University College London
  • Ryan Holmdahl
    Ryan Holmdahl Stanford University
  • Christian Schroeder de Witt
    Christian Schroeder de Witt University of Oxford
  • Eirikur Agustsson
    Eirikur Agustsson ETH Zurich, now @ Google Research
  • Benedikt Schifferer
    Benedikt Schifferer Columbia University, McKinsey & Company
  • Tim Rudner
    Tim Rudner University of Oxford
  • Denny Britz
    Denny Britz Stanford University, Google Brain