Christoph Düsing, M.Sc.

About Me

I am a PhD student in computer science working at the Semantic Computing Group at Bielefeld University, Germany. I am working towards improving Clinical Decision Support Systems within the KINBIOTICS project funded by the German Federal Ministry of Health. As part of my individual research, I am investigating applications of Federated Learning, particularly within the healthcare domain. Moreover, I am curious to find determinants for the success and failure of Federated Learning in certain applications. In a broader sense, I seek to utilize methods from data science and machine learning to solve real-world problems, mostly related to the healthcare domain.

Originally from a small town of North Rhine-Westphalia, Germany, I received my B.Sc. in Business Information Systems at FHDW Paderborn, Germany and my Master's degree (M.Sc. Business Information Systems, focusing on Data Science) at Paderborn University. During that time, I worked at Bertelsmann SE & Co. KGaA for four years and joined the Social Computing Group of Paderborn University as Student Assistant.

Research Interests

My research interests include:

  • Federated Machine Learning
  • Clinical Decision Support
  • Counterfactual Explanations
  • Natural Language Processing
  • Applications and Diffusion of AI

We share some research interests? Feel free to contact me to exchange some ideas!

Publications

Recent Publications

Federated Learning to Improve Counterfactual Explanations for Sepsis Treatment Prediction

AIME 12.06.2023

Counterfactual explanations are usually generated with the help of generative models. In order to receive such models, we propose to use federated leaning among hospitals in order to overcome the issue of limited data while data privacy is maintained. Check out our demo to find out more.

On the Trade-off Between Benefit and Contribution for Clients in Federated Learning in Healthcare

ICMLA 14.12.2022

In this paper, we measure Data Imbalance, Benefit and Contribution of hospitals participating in a Federated Learning cohort in order to improve their quality of service. Our findings reveal a trade-off between Benefit and Contribution for hospitals which favors low-imbalance clients in terms of Benefit, whereas high-imbalance hospitals remain crucial for the success of the FL cohort.

Recent Presentations

Predictive Diagnostics for Federated Learning: A Privacy-Preserving Toolbox Towards Successful Federated Learning

ECML PKDD PhD Forum 18.09.2023

In order to increase both fairness and success of federated learning prior to actual participation and computation, I aim towards providing privacy-preserving tools in my dissertion, tentatively titled "Predictive Diagnostics in Federated Learning".

All Publications

Teaching

Although I am currently not involved in any lectures or seminars, I actively participate in maintaining academic operations in the following capacities:

Contact

  • Mail
    cduesing@techfak.uni-bielefeld.de
  • Phone
    +49 521 106 12143
  • Postal
    Christoph Düsing
    Semantic Computing Group
    CITEC - Bielefeld University
    Inspiration 1
    33619 Bielefeld
    GERMANY