Schubert Lab


The immune system, primarily the adaptive immunity, plays a significant role in many disease etiologies including that of cancer, autoimmune diseases, and infectious diseases.

Our group works on developing and applying methods from bioinformaticsmachine learning, and combinatorial optimization to gain a deeper understanding of the immune system involvement in disease and to aid in the design of new immunotherapies

Our focus currently lies on:

  • Dissecting the immune microenvironment in a disease context by studying the immune cell composition and T-cell repertoire through single-cell experiments.
  • Developing epitope and immunogenicity prediction models using modern Deep Learning and Kernel-based methods that not only have high accuracy but also can quantify their prediction uncertainty.
  • Developing computational models for vaccine and biotherapeutic design by combining machine learning models with combinatorial optimization techniques to efficiently explore vast design spaces to find high-quality therapeutic candidates.




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