Research & Development

Our Research Philosophy

Our research philosophy is based on open collaboration and exchange of ideas.

We especially enjoy bringing together expertise from different areas, be it highly specialized application domains, cutting-edge (academic) research. Our focus are highly interdisciplinary projects with partners who contribute deep domain expertise that we can complement with our computer science, data analytics, and software development expertise.

Working collaboratively together in this joint expert approach allows to achieve outcomes that individually would not be possible.

See below some examples for how we exchange our ideas on conferences, in talks and publications, or open source software releases.

Our Open Source Software


ipyannotator – the infinitely hackable annotation framework. Even though much less glamorous than developing new machine learning models, the annotation process and the required tooling is often one of the most critical aspects of real world Machine Learning projects.

Checkout on Github


This repository allows you to use Factorization Machines in Python (2.7 & 3.x) with the well known scikit-learn API. All performance-critical code has been written in C and wrapped with Cython. fastFM provides stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference.

Checkout on Github


  • ECML PKDD 2020 - Palaimon presented the winning solution for the Discovery Challenge ChAT

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place from the 14nd to the 18nd of September 2020.

Video from talk
  • JupyterCon 2020 - Palaimon prensented ipyannotator – the infinitely hackable annotation framework

Online Conference 5-9 October: Tutorials 12-16 October: Conference 17 October: Sprints

Video from talk