>> AI ALLIANCE RP > AI RESEARCH > AI RESEARCH PROFESSORS > PROF. DR. MAIK KSCHISCHO

Prof. Dr. Maik Kschischo

  • Professor of AI in Healthcare

    Research interests

    • Machine Learning
    • Statistical Analysis of Complex Data
    • Mathematical Modelling

    in Medicine and Biologe

Contact

Basic Research

  • We are devising and applying artificial intelligence (AI) techniques to solve problems within the field of biomedicine. We are developing and implementing machine learning algorithms, statistical methods and mathematical models to analyse biomedical data ranging from electronic health records to cancer genomic data. We closely collaborate with researchers, clinicians, and industry experts in biology and medicine. We want to contribute to a better understanding of biological complexity and of medical decision-making.

    Currently, there are two major research directions:

    • Machine Learning (ML-) Methods for learning causal dynamic models from data
    • High dimensional data analysis and modelling in cancer biology

Application related Research

  • Application areas:

    • Cancer research (in particular chromosomal instability, drug resistance, tumour heterogeneity)
    • Clinical decision support using AI methods

Activities as a Reviewer

  • Reviewer for reserach proposals at 

    • BBSRC
    • DFG
    • BMBF
    • EU

    Reviewer for several journals including 

    Cancer Research, Nature, Bioinformatics, PLOS Com-
    putational Biology, BMC Bioinformatics, Microarrays,
    European Physical Journal (EPJ) E, Bulletin of Math-
    ematical Biology, Journal of Statistical Software, Brief-
    ings in Bioinformatics, Physics Letters A, Physical Re-
    view E, IEEE Transactions on Automatic Control 

Received Awards, Prizes, Honors

  • Lion Bioscience Innovation price (2002)

Special Expertise

  • Basic Research

    • Machine Learning (ML): Self-Supervised Learning, (Semi) Supervised Learning, Artificial Neural Network (ANN), Decision Tree, Bayesian Neural Networks, Model Based, Generative Models, Dimensionality Reduction, Feature Engineering/Feature Extraction
    • Knowledge-Based Systems: Causality
    • Robotics: Control Algorithms, Simulation Technology

  • Application related Research

    • Smart Assistant Systems: Predictive Analysis, Digital Medicine
    • Perception and Sensor Fusion: Diagnostics
    • Information Retrieval (Knowledge / Data Management and Analysis): Knowledge Discovery in Databases (Data Mining), Decision Support
    • Technology Analysis: Sociological Aspects

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