Our research focuses on developing novel computational and experimental strategies for designing next-generation therapeutics, diagnostics and vaccines by combining high-throughput single-cell antibody and T-cell receptor sequencing with artificial machine learning and high-dimensional statistics. Our ultimate aim is to improve public health by decreasing the burden of infectious and autoimmune diseases and cancer.

•  Machine learning methods for deciphering the human immune repertoire language. Deciphering the immune information (immune status, antigen binding) encoded into antibody and T-cell repertoires is of paramount importance for the development of vaccines, diagnostics and therapeutics and requires machine learning approaches (artificial intelligence).  We focus on developing deep learning approaches to learn how to read and write the immune repertoire language.
• Single-cell immune repertoire and immuno-mass spectrometry methods. Our research focuses on developing experimental platforms that link droplet-based high-throughput single-cell sequencing with the large-scale mass-spectrometry analysis of serum antibody repertoires in order to resolve the diversity and specificity of the effector serum antibody repertoire at single antibody resolution.

Contact Victor Greiff

Contact Victor Greiff