Machine Learning Research

Advancing novel machine learning techniques and building the bridge between research and application.

The practical success of machine learning is mostly founded in theoretical and methodological advances achieved by the scientific community in recent time. In an increasingly expanding field, one must therefore catch up with the latest scientific developments when aiming to solve challenging problems. dida considers itself as a research-driven company, having strong ties to academia and publishing own research papers.

Scientific publications

We conduct machine learning research ourselves and publish papers at renowned AI conferences such as NeurIPS or ICML. Our research includes methodological as well as rather applied fields - see here for a detailed listing.

Cross-institutional collaboration

dida collaborates with renowned research institutes in applied and theoretical research projects. The goal of such collaborations is to connect recent theoretical AI concepts to industrial applications across a wide network of experts and research teams.

A team of machine learning scientists

We encourage our team of scientifically trained employees to keep track of current research results, e.g. by organizing regular internal seminars and reading groups, in which we discuss details of latest machine learning papers.

Our research partners: