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
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.
excited to announce that this January we are relocating to Yale to start a theory initiative at the Center for Systems and Engineering Immunology (CSEI): @yalecsei.bsky.social
looking to hire 2 postdocs to join us in this endeavor.
more details can be found here: sites.google.com/uw.edu/statp...
The Mahan postdoctoral fellowship offers 21 months of support to develop your own research with Fred Hutch computational biology faculty-- lots of excellent labs to choose from!
Apply: apply.interfolio.com/172697
Faculty: www.fredhutch.org/en/research...
excited that this paper is finally out in @pnas.org :
www.pnas.org/doi/10.1073/...
Led by Gian Marco Visani (effort initiated by Michael Pun), fantastic collaboration with @pgtimmune.bsky.social @asya-minervina.bsky.social and Phil Bradley.
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.
check out our manuscript (led by Obinna Ukogu) on Design principles of cytotoxic T-cell response: www.biorxiv.org/content/10.1...
Wanted to highlight our latest preprint--a huge effort by multiple people and labs, but led primarily by @wsdewitt.github.io, Tatsuya Araki, and Ashni Vora, in a very close wet-dry collaboration with @matsen.bsky.social’s lab at the Hutch
www.biorxiv.org/content/10.1...
Recasting the perils of phylodynamic non-identifiability as a feature not a bug, we show that a unique forward-equivalent process enables exact and efficient simulation from arbitrarily large populations. With M Celentano, S Prillo, @yun-s-song.bsky.social www.pnas.org/doi/10.1073/pnas.2412978122
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
check out our new manuscript (led by Gian Marco Visani):
structure-based machine learning model for TCR-pMHC complexes, predicting T-cell affinity to peptide-MHC complexes, quantifying T-cell receptor specificity, and designing de-novo immunogenic peptides:
arxiv.org/abs/2503.00648
yes please.
yes please.
would like to be added
