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M. Sc. Philipp Rigoll

  • Forschungszentrum Informatik (FZI)
    Haid- und Neu-Str. 10 - 14
    76131 Karlsruhe

Research

Data analysis and data mining

Driven by digitalization, data is now central to a wide variety of areas of life and business. They appear in the most diverse forms and characteristics. Data is being collected, processed and stored in more and more places. Data analysis is concerned with extracting valuable information from this data. The focus of data mining is on using statistical methods to find and describe patterns and hidden relationships in the data. At FZI/ITIV, we are researching to perform these analyses as agnostically as possible. The primary goal is the comprehensibility of the analyses and, associated with this, an understandable presentation of the results.

Machine learning

Building on data analysis and data mining and the associated understanding of the data, machine learning goes one step further. Here, algorithms learn the regularities of the data as a statistical model and can generalize to further data after a learning phase. Especially in the form of artificial neural networks, machine learning has proven its worth and is used, for example, for the prediction of time series, the detection of anomalies and object detection. At FZI/ITIV, we develop and investigate these methods, for example, in the context of automated driving.

Augmentation with generating artificial neural networks.

Artificial neural networks, in addition to generalizing to unknown data, are capable of generating new data (the three images above were generated by text input using the stable diffusion architecture). In addition to purely artistic and creative creation, this approach also allows for the addition of missing data points in datasets. This data set augmentation is called augmentation. We at FZI/ITIV are researching the use of augmentation with generating artificial neural networks in the context of developing automated driving functions.

HiWi Positions
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