Quantitative assessment of brain histology is often constrained by predefined feature sets and labor-intensive manual annotations. To overcome these limitations, we employed unsupervised deep learning to automatically extract and quantify tissue orga... read more
Across many brain areas, neurons produce heterogeneous, seemingly disordered responses. Yet the circuits these neurons comprise cannot be purely random; they must possess some structure to generate representations and computations underlying behavior... read more
The growing number of spatial transcriptomics (ST) datasets enables comprehensive multi-modal characterization of cell types across diverse biological and clinical contexts. However, integration across patient cohorts remains challenging, as local mi... read more
Recent advances in large-scale self-supervised learning have led to the emergence of foundation models capable of extracting transferable visual representations from high-dimensional image data. In computational pathology, such models are increasingl... read more
Emerging and evolving viral diseases, such as SARS-CoV-2, continue to pose significant global health challenges, underscoring the urgent need for rapid and scalable antigen discovery pipelines. This work presents a computational pipeline that integra... read more
Natural evolution is high-dimensional; organisms adapt to many pressures at once, across substrates, environments, and genetic backgrounds. Yet most directed evolution methods flatten this landscape to a single selection axis, hiding tradeoffs, and l... read more
Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. W... read more
Planning is a key executive function that enables humans to anticipate future outcomes by mentally simulating action sequences and balancing immediate gains against long-term goals. While we developed a partial understanding of neural mechanisms of p... read more
Reasoning has emerged as a key capability of large language models. In linguistic tasks, this capability can be enhanced by self-improving techniques that refine reasoning paths for subsequent finetuning. However, extending these language-based self-... read more
Multimodal Image Fusion (MMIF) integrates complementary information from various modalities to produce clearer and more informative fused images. MMIF under adverse weather is particularly crucial in autonomous driving and UAV monitoring applications... read more
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