Scalable Split Learning and Quantization for Vision–Language Models in Medicine
Studying multi-server split learning, low-bit representation transfer, and privacy–utility trade-offs in medical visual question answering.
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1 min read
Studying multi-server split learning, low-bit representation transfer, and privacy–utility trade-offs in medical visual question answering.
Using structured LLM simulations, bandits, and budget constraints to study data-driven incentives for heterogeneous travelers.
Fine-tuning EPCOT on single-cell multi-omics data to predict gene expression from chromatin accessibility, with reproducible preprocessing and cell-specific evaluation.
Building a full-stack workflow for materials-science data preparation, interactive model fitting, and human-verified AI assistance.
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