Armita Nourmohammad
@thearmita.bsky.social
physicist, interested in biology --- Assoc. Prof. of physics @UW
(Seattle)
(Seattle)
some relevant recent publications from the group:
Protein structure modeling with symmetry-aware neural nets:
www.pnas.org/doi/10.1073/...
designing T-cell antigens w/ our protein models:
www.pnas.org/doi/10.1073/...
Design principles of T-cell responses:
arxiv.org/abs/2509.22997
Protein structure modeling with symmetry-aware neural nets:
www.pnas.org/doi/10.1073/...
designing T-cell antigens w/ our protein models:
www.pnas.org/doi/10.1073/...
Design principles of T-cell responses:
arxiv.org/abs/2509.22997
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
www.pnas.org
October 27, 2025 at 8:47 PM
some relevant recent publications from the group:
Protein structure modeling with symmetry-aware neural nets:
www.pnas.org/doi/10.1073/...
designing T-cell antigens w/ our protein models:
www.pnas.org/doi/10.1073/...
Design principles of T-cell responses:
arxiv.org/abs/2509.22997
Protein structure modeling with symmetry-aware neural nets:
www.pnas.org/doi/10.1073/...
designing T-cell antigens w/ our protein models:
www.pnas.org/doi/10.1073/...
Design principles of T-cell responses:
arxiv.org/abs/2509.22997
Topics of interest:
(1) Physics-inspired machine learning for molecular immune-pathogen recognition.
(2) Modeling cell-fate decisions in dynamic environments to
build predictive, controllable models of immune responses across cellular and tissue conditions.
(1) Physics-inspired machine learning for molecular immune-pathogen recognition.
(2) Modeling cell-fate decisions in dynamic environments to
build predictive, controllable models of immune responses across cellular and tissue conditions.
October 27, 2025 at 8:47 PM
Topics of interest:
(1) Physics-inspired machine learning for molecular immune-pathogen recognition.
(2) Modeling cell-fate decisions in dynamic environments to
build predictive, controllable models of immune responses across cellular and tissue conditions.
(1) Physics-inspired machine learning for molecular immune-pathogen recognition.
(2) Modeling cell-fate decisions in dynamic environments to
build predictive, controllable models of immune responses across cellular and tissue conditions.
By exploring an ensemble of feedback control immune designs, we quantified trade-offs between effective pathogen clearance and immunopathology. We identified genetic signal-processing perturbations that could improve efficacy while limiting toxicity of T-cell immunotherapies against cancer.
October 6, 2025 at 10:31 PM
By exploring an ensemble of feedback control immune designs, we quantified trade-offs between effective pathogen clearance and immunopathology. We identified genetic signal-processing perturbations that could improve efficacy while limiting toxicity of T-cell immunotherapies against cancer.
this work was initiated by Mike Pun (now at Vant AI), and is built on his work on H-CNN: pnas.org/doi/10.1073/...
fantastic collaboration with @pgtimmune.bsky.social , @asya-minervina.bsky.social and Phil Bradley
fantastic collaboration with @pgtimmune.bsky.social , @asya-minervina.bsky.social and Phil Bradley
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
pnas.org
March 5, 2025 at 12:24 AM
this work was initiated by Mike Pun (now at Vant AI), and is built on his work on H-CNN: pnas.org/doi/10.1073/...
fantastic collaboration with @pgtimmune.bsky.social , @asya-minervina.bsky.social and Phil Bradley
fantastic collaboration with @pgtimmune.bsky.social , @asya-minervina.bsky.social and Phil Bradley
would like to be added
November 16, 2024 at 4:51 PM
would like to be added