Armita Nourmohammad

Dr. Nourmohammad studies how biological systems learn, adapt, and evolve functional molecular programs, with a special focus on the immune system. Using interdisciplinary approaches that integrate machine learning, information theory, statistical physics, and control theory, her lab seeks to understand and predict adaptive immune responses to guide the rational design of next-generation immune engineering strategies.

Current projects include developing physics-inspired machine learning models to map the immune recognition shape space, and to uncover the dynamic landscape of immune decision-making in health and disease.

• NSF CAREER award (2021)
• NIH MIRA award (2021)
• Early Career Award from American Physical Society (APS), Division of Biological Physics (DBIO) (2021)
• Klaus-Liebrecht award from the University of Cologne (2012)

• Visani, M. N. Pun, A. Minervina, P. Bradley, P. Thomas, A. Nourmohammad(2025) T-cell receptor specificity landscape revealed through de novo peptide design,  Proc Nat Acad Sci  122 (42) e2504783122
• M.N. Pun, A. Ivanov, Q. Bellamy, Z. Montague, C. LaMont, P. Bradley, J. Otwinowski,  A. Nourmohammad  (2024) Learning the shape of  protein micro-environments with  a holographic convolutional neural network. Proc Nat Acad Sci  121 (6) e2300838121 
• C. LaMont, J. Otwinowski, K. Vanshylla, H. Gruell, F. Klein, A. Nourmohammad  (2022) Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1. eLife 11:e76004 
• G. Isacchini, A. Walczak, T. Mora, A. Nourmohammad (2021) Deep generative selection models of T and B cell receptor repertoires with soNNia, Proc Nat Acad Sci 118 (14) e2023141118
• O. Schnaack, A. Nourmohammad (2021) Optimal evolutionary decision-making to store immune memory, eLife 10:e61346