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AI-based Applications


AI for Software
Image and Data Analysis
in Medicine
Data Science
for Biomedical Applications
AI for


Machine Learning for IT-Security (Prof. Bogdan)

  • Objective 1: Efficient accelerators
    • Break down Deep Learning to reduce power and memory footprint
    • Design efficient circuits for embedded systems
  • Objective 2: Secure computation
    • Obfuscate computations for privacy in untrusted environments
    • Improve resilience of algorithms against intended manipulations 



Biomedical Image Data Science (Prof. Sbalzarini)

  • Content-adaptive convolutional neural networks
    • Develop CNNs for irregular spatial sampling
    • Apply to adaptive image representations in microscopy


Image: Representing an image by a content‐adaptive particle representation instead of pixels improves processing

  • Hybrid learning of mathematical models from image data
    • Develop hybrid learning algorithms using structured sparsity norms
    • Apply to learning differential equation models from image data
  • Robust learning by design centering
    • Replace optimization by design centering when training supervised learners
    • Evaluate and compare the approaches 



Coding AI: An AI for Code Suggestions (Prof. Siegmund)

  • Motivation: Developers frequently trigger code completion functionality of IDEs to get code suggestions for solving
    an implementation task
    • Contemporary code suggestions are inaccurate, very limited in scope, and fail to recognize the programmer’s problem
  • Goal: Vastly speeding up development time by supporting the programmer with highly accurate code suggestions for her current implementation task
  • Key Idea: Use a generative adversarial network architecture to train a deep generative network such that the generated code suggestions outperform actual suggestions



BigData and Machine Learning applications for advanced image and data analysis in medicine (Prof. Neumuth)

  • Within the clinic specific, high-performance AI-based algorithms for data analysis and image reconstruction are required.
  • For medical AI, we need: suitable training data, international benchmarking and technical-methodical framework conditions
  • Goal: a modular analysis pipeline for multimodal medical data (imaging data as well as other data)
    • ML-based preprocessing
    • ML-based feature extraction
    • ML-based classification
  • We will focus on Hyperspectral imaging (HSI), which combines imaging with spectroscopy

Image: The tissue is illuminated, and the reflected light is acquired by a camera and analyzed by a spectrometer. The generated 3D data are called hypercubes with two spatial and one spectral dimension. The technology is used in particular for the classification of tissue (also in situ).