Nele Brügge

Hej, I'm Nele

I’ve always been interested in making medical care more intelligent, accessible, and humane through machine learning. Over time, my focus has expanded beyond accuracy alone – toward understanding how we can design machine learning models that earn and preserve human trust. As a researcher and PhD student at DFKI, I contribute to the AnoMed project, where I work at the intersection of fundamental research on differential privacy and real medical challenges.

My current research explores the intersection of machine learning, differential privacy, and representation learning:

• Privacy-friendly feature extraction methods that support robust model performance in complex tasks under privacy constraints.
• Differentially-private data synthesis to produce realistic synthetic datasets with formal privacy guarantees.
• Weakly supervised learning frameworks that utilize limited or noisy labels.

Vitæ

• DFKI 2022 - now

  Researcher and PhD Student
  Differential Privacy in the Medical Domain
• University of Luebeck 2021 - 2022

  Research Scholarship
  AI-based medical image and video analysis for tic detection in Tourette syndrome patients
• Drägerwerk AG & Co. KGaA 10/2020 - 05/2021

  Master's Thesis
  Deep sleep stage clustering and multi-task Gaussian process regression for the detection of sleep cycles in premature infants, Paper (on Gaussian Process regression)
• University of Luebeck 2019 - 2021

  Master of Science
  Medical Engineering Science
  Specialisation: Medical image processing
• Drägerwerk AG & Co. KGaA 10/2019 - 03/2020

  Intern
  Explainable AI for estimating the respiratory drive of ventilated patients from sEMG
• University of Luebeck 2015 - 2019

  Bachelor of Science
  Medical Engineering Science
  Bachelor Thesis: Reccurent Neural Networks for Hand Gesture Recognition Based on sEMG and IMU Data, Paper

Publications

Most recent publications on Google Scholar.
^‡ indicates equal contribution.

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