Tim O'Donnell

@timodonnell.bsky.social

Computational immunologist. ML for science @OpenAthena. Previously @OpenVax @MountSinai @DEShawResearch

One thing that helped with was implementing a simulated gameplay. Have Claude run 100s of games, get feedback and update ai.js. It is still doing some silly things... but getting better. Perhaps a future direction would be to let multiple LLM compete with eachother by building better ai.js?🤠 (3/4)

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I'm super excited to announce the first preprint of my PhD, together with Chenxi Ou and @sokrypton.org! ML has revolutionized protein modeling, but crucial challenges remain. For example, we can't reliably predict complicated protein structures without MSAs, which limits what we can design.

Newbie antibody design q: if I take a mouse IgG1 antibody and express its VH/VL on a human IgG1 constant framework, how likely is it to still bind the target? Does that kind of thing usually work?

Missed this when it came out this summer - high doses of MHC II neoags in a peptide vaccine can be counterproductive due to a population of FOXP3− CD4+ regulatory cells that *kill* cDC1s. Big implications for peptide cancer vaccines if true www.nature.com/articles/s41...

Neoantigen-specific cytotoxic Tr1 CD4 T cells suppress cancer immunotherapy - Nature

Type 1 regulatory T cells (Tr1) represent a major obstacle that compromises naturally occurring and therapeutically induced tumour-specific immunity.

nature.com

Say I have a deep learning model that runs on pairs of things M(a,b), and I want to distill it into three models X,Y,Z where Z(X(a), Y(b)) approximates M(a,b), and hopefully Z can be shallow and fast. Is that problem studied? What should I google?