University of Chicago

Cryo-EM pose estimation conditioned on a reference volume handed in at inference, assigning particle orientations zero-shot on unseen structures.

Biomolecular sequence-structure co-design that plans over frozen folding and inverse-folding models with Monte Carlo tree search, training nothing.

Vision-language model for neuroblastoma pathology that reads H&E slides with their reports to grade tumors, infer biomarkers and stratify risk.

Transformer that predicts protein-protein interactions at residue resolution, spanning mutations, PTMs, peptide-MHC binding, and disease variants.

Large language model trained on functional genomics data to prioritize novel therapeutic targets from genome-wide CRISPR knockout screens.

Masked language model for T-cell receptor and peptide-MHC binding prediction, with compositional pretraining and non-autoregressive decoding.

Structure-conditioned protein sequence design, pairing a three-track architecture with discrete flow matching for fast, few-step inverse folding.

Generative histopathology foundation model: a diffusion transformer trained on 30M H&E tiles, conditioned on self-supervised slide embeddings.

Autoregressive transformer pretrained on 2,000 hours of intracortical spiking activity, decoding motor intent across subjects, species, and tasks.

Cell-type-specific gene expression prediction from DNA sequence, mapping Enformer epigenomic features to pseudobulk expression for cell-resolved TWAS.

Virtual multiplex immunofluorescence staining from H&E histopathology, imputing the expression and spatial localization of 50 protein biomarkers.

Text-guided protein design that generates functional sequences from natural language prompts through a contrastive protein-text embedding space.

Breast cancer recurrence risk read from an H&E slide and six routine clinical variables, using a frozen pan-cancer pathology encoder.