Artificial Intelligence & Machine Learning

  • deep learning
  • supervised classification
  • unsupervised classification (clustering)

  • regression

  • development of prototypes
  • development of production models

My working principles:

  • As simple as possible, as complicated as necessary: Your data-driven predictive models will be perfectly tailored to data situations and business problems.

  • Robust Data Science solutions require a solid foundation: design and optimization of predictive models are done with scientifically sound methods.

  • Quality through high engineering standards: Code is documented in detail, and comprehensive unit tests ensure robust functionality.

Related Projects

  • Data Scientist and Data Engineer
    2021 Medium-sized Trading and Logistics Company

    Development of AI based sales prediction models.

    Design and implementation of an AI-supported prediction model for sales of print media in magazine distribution, prototyping, implementation and deployment of the productive system.

    Tasks:
    • Conception of a deep learning model for the prediction of sales figures
    • Development of a data model and ETL processes for processing raw data
    • Implementation of the automated prediction service based on an AI model

    Tools:
    TensorflowPostgresMS SQL ServerPythonSqlalchemyAlembicDockerdocker-composegit

  • Data Scientist / Python Developer
    2019

    Development of a machine learning application for the analysis of sensor data.

    Highly noisy time series data are recorded via a sensor within a measuring apparatus. In these time series, sporadically occurring events are to be automatically detected and characterized. For this purpose, a machine learning software with MVC architecture was implemented in Python. The software includes the following functionality: Filter and normalize the raw data, calculate robust metrics based on the pre-processed data, provide a graphical user interface to display data and interactively create training data sets, automatic detection of events using a supervised machine learning method, export of the result data.

    Tools:
    PythonTkinterScikit-Learngit

  • Python Developer / Project Lead
    2016

    Development of YAPiC, an open source software for analyzing biomedical image data using deep learning.

    Tasks:
    • Project management
    • Conception and algorithm development
    • Presentation of the software at international conferences
    • Deployment, development of CI/CD pipelines
    • Management of the further development of the tool by the open source community

    Tools:
    PythonTensorFlowTravis-CIGitHub

    YAPiC Website
  • Data Scientist / Image Processing Specialist
    2017

    Automated characterization of tissue samples using deep learning

    As part of a medical research project, tissue samples were photographed with an automated microscope. Based on Python and Tensorflow, software was developed to identify and classify specific cell types in the tissue. In this way, different cell types could be counted automatically for user-defined tissue regions.

    Tools:
    PythonTensorFlowgit