#### Michael Borinsky (PhD)

###### External Advisor

###### Intermediate

openCV, TensorFlow ###### Expert

C, Python, Mathematical Statistics, SciPy, Computer Vision, OpenGL 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.

#### William Clemens (PhD)

###### Intermediate

Numerical Optimization, Computer Vision, Shell Scripting, R ###### Expert

Cuda, C, Torch, TensorFlow, Deep Learning, Python, Mathematical Statistics 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.

#### Astrid Gansekow

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 Glass

###### Intermediate

PHP, Electron, vue.js, Angular, AJAX, JavaScript ###### Expert

HTML, Sass, UI, UX 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.

#### Fabian Gringel

###### Intermediate

Java, Python, NLP ###### Expert

Mathematical Modeling, Optimization, Physical Simulations 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 Heesen

###### Basic

Python, SQL 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 Jackmuth

###### Intermediate

NumPy, SciPy, R ###### Expert

Python, SQL, Pandas, Data Analysis, Regression Models Philipp advises our customers on which processes to automate. He make sure that the planned value-add of the projects materialize. 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.

#### Nelson Martins (PhD)

###### External Advisor

###### Intermediate

Python, PyTorch, Reinforcement Learning ###### Expert

Computer Vision, Image processing & segmentation, openCV, C, TensorFlow 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.

#### Lorenzo Melchior

###### Intermediate

TensorFlow, C, C++, Java, R ###### Expert

Python, PyTorch, NumPy, Pandas, Linux 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.

#### Mattes Mollenhauer

###### External Advisor

###### Intermediate

Computer Vision, Signal Processing, Optimal Control ###### Expert

Python, C, Applied Probability Theory, Deep Learning, Software Engineering, Nonparametric Models 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.

#### Lorenz Richter

###### Intermediate

R, non-convex optimization, Computer Vision ###### Expert

Statistical Learning Theory, Deep Learning, Python, Mathematical Statistics, Julia, TensorFlow 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.

#### Konrad Schultka

###### Intermediate

NLP, Computer Vision, Optimization, TensorFlow ###### Expert

Probability Theory, Python, Deep Learning, PyTorch 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.

#### Tobias Sterbak

###### External Advisor

###### Intermediate

Speech Recognition, DevOps, Natural Language Generation, Docker, Spark ###### Expert

Python, Keras, TensorFlow, NLP, PyTorch, Software Architecture, SpaCy, NLTk 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.

#### Frank Weilandt (PhD)

###### Intermediate

Deep Learning, Keras, NLP, Computer Vision ###### Expert

Python, C, Topological Data Analysis 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.

#### Matthias Werner

###### Intermediate

Sequence Alignments, Markov-Chain-Monte-Carlo, Computer Vision ###### Expert

C++, C, Python, Reinforcement Learning, Bayesian Deep Learning 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.

#### Team

###### Expert

Machine Vision, NLP, Algorithms, Optimization 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.