About me

I’m Nicole Seifert, a postdoctoral researcher in medical bioinformatics working to understand how immune states form, adapt, and fail across cancer, infection, and aging. I develop explainable AI models that reconstruct and simulate complex immune processes from low-resolution data, with the goal of making immune-state modeling feasible in real clinical settings where high-resolution measurements for every patient are not realistic.

My scientific background spans both experimental and computational biology. I started in molecular virology, working on nanoparticle-based HIV-1 vaccine strategies and protein engineering, and later moved into cancer research, studying stress-induced resistance to chemotherapy using multi-omics data. During my PhD, I focused on lymphoma, integrating clinical information, gene-expression profiles, histopathology, and T-cell receptor sequencing to study prognosis, immune escape, and T-cell exclusion, and I developed a tool for TCR repertoire analysis that does not require large computational ressources or programming skills.

In my postdoctoral work, I focus on method development in computational immunology. I build deconvolution and modeling approaches that learn immune structure from high-resolution data and project this knowledge onto bulk and other routinely collected datasets. These methods are applied across immune-related diseases, including lymphoma and exploratory work in Long COVID, to study immune composition, dysregulation, and treatment response in real-world cohorts.

Across these directions, my research is guided by a central idea: complex immune states should be computationally accessible — even when only routine clinical data can be collected. Looking ahead, I am extending this line of work toward immune aging and antigen specificity, modeling how immune states change across the lifespan and complementing repertoire analyses with peptide–receptor docking for T- and B-cell receptors to better connect immune patterns to the antigens they recognize.

On this site, you can explore my research projects, publications, teaching activities, software tools, and collaborations. The page is continuously updated.

Thank you for visiting — I welcome scientific exchange, collaboration requests, and new research connections.