Prof. Dr. Maik Kschischo

  • Professor of Biomathematics

    Research interests

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

    in Medicine and Biologe


Basic Research

  • We devise Machine Learning methods, analyse data and develope mathematical and statistical models for biological and medical applications. 

    In particular, we work on methods for

    • time series data and learning of differential equation models (model errors, Neural ODEs)
    • Data assimilation methods
    • data driven control of dynamic systems 
    • methods for integrating multimodal and high dimensional biomedical data
    • Cancer computational 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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