While studying physics at HU Berlin, Michael was already contributing to CERN’s Root data analysis framework. With his coding abilities and his mathematical skills obtained during his dissertation in quantum physics, he supports the machine learning team.
Will developed his mathematical and coding skills during his studies in theoretical physics at Warwick and Cambridge Universities. Before joining dida, he did his PhD in string theory and quantum chromodynamics at the University of Southampton. Currently, Will specializes in computer vision.
After a decade long career as a full-stack web developer, Johan decided to switch fields and delve into data science. To ease the transition, Johan decided to pursue a Physics degree at TU Berlin, with minors in mathematics, probability theory and machine learning. He now applies his new found knowledge together with his project management experience at dida.
After her studies of mathematics (TU Gdansk, Poland) and statistics (HU Berlin), Ewelina is currently finishing up her PhD at TU Berlin in the field of nonparametric statistics. Her research work involves numerical simulations for optimal hyperparameter selection in statistical learning algorithms. Being naturally attracted to problems requiring mathematical skills, Ewelina is part of the machine learning team. Previously she worked as risk analyst in a global financial institution.
Acting as the point of contact among executives, employees, clients and other external partners, Astrid is responsible for managing the information flow at dida. After some years as an Executive Assistant in the software sector she’s supporting us with her experienced organizational and accounting skills.
Max makes sure that users are able to interact intuitively with our software products. As frontend developer/UX designer in one, he constantly strives to combine clean designs with uncompromising functionality.
Due to his studies of mathematics and philosophy (HU Berlin, Uni Bochum) combined with his interest in foreign languages, Fabian is naturally attracted to projects in the field of computational linguistics. Before joining dida, Fabian dealt with physical simulations at Max Planck Institute for iron research and at TU Berlin.
Robert is responsible for translating customer needs into software written by our Machine Learning experts. He is also responsible for Marketing and Sales. In the past ten years after his studies at HHL Leipzig and University of Maastricht, he led business units in various digital companies (e.g. Axel Springer).
Philipp advises our customers on which processes to automate. He makes sure that the value-add of the project materializes as planned. Before founding dida, the statistician (TU Berlin) was part of the management team which grew Beko Käuferportal GmbH from a small startup to 300+ employees.
During his PhD in computer science at the University of Porto he co-authored various papers in the field of image processing. As an external consultant he is our go-to guy when it comes to pattern recognition in any kind of image data.
During his studies in mathematics and computer science [FU Berlin], Lorenzo found his passion for machine learning and statistics. He is experienced in image recognition, regression problems and in working with time series data. His dev-ops skills earn him regular praise by his collegues.
After his studies of mathematics (FU Berlin), Mattes is currently pursuing his Phd doing research on machine learning models for time series problems in physics and signal processing. He has been involved in several large AI projects and is experienced in transforming recent research results and experimental solutions to production software.
With an original focus on stochastics and numerics (FU Berlin), the mathematician has been dealing with deep learning algorithms for some time now. Besides his interest in the theory, he has practically solved multiple data science problems in the last 5 years. Lorenz leads the machine learning team.
During his studies in mathematics at the TU Berlin, Lovis concentrated on stochastics and numerics, which brought him to the field of Machine and Deep Learning. He has worked in an IT consultancy where he gathered experience in software development and testing. Now he is using his mathematical knowledge and programming skills at dida.
Konrad cultivated his mathematical modeling skills while studying at HU Berlin. A graduate scholarship from Berlin Mathematical School led him to investigate the mathematical foundations of quantum fields. After transitioning from the quantum to the classical world, his interests have shifted to the analysis of probabilistic models and deep neural networks.
After some years in the software industry, Tobias decided to go into consulting. He holds a degree in pure mathematics from HU Berlin. Tobias is experienced in deploying machine learning models into production and he advises us in all things concerning natural text data.
After his studies (LMU München) and PhD (HU Berlin) in theoretical physics, Petar worked for several years as an IT consultant with projects at different DAX companies. In the last years he developed his passion for machine learning and specialized in this field. Petar is supporting the machine learning team as a developer and project manager.
After studying mathematics at the University of Bonn, Frank used Python and C++ for data analysis in several research groups: For computer vision at a Fraunhofer Institute in Sankt Augustin, during his PhD studies about the numerical simulation of dynamical systems in Cracow, Poland, and as a Post-doc for topological data analysis in Bremen. Having taught programming in Polish, his next challenge is teaching computers to understand language.
During his studies of physics in Oldenburg and Berlin, Matthias cultivated an active interest in computer science. He worked with DNA alignments and molecular dynamics simulations at GRIB in Barcelona, worked in C++ development at FU Berlin and is doing research in reinforcement learning and Bayesian deep learning.
Further support is provided by freelancers, phd’s and post-docs from the fields of mathematics, physics and computer science. This allows to cover a wide range of machine learning topics and to offer our clients state of the art software solutions.