Xinyu Jiang

This paper explores the scalability of random forest models in myoelectric control, showing they can be reduced in size with minimal accuracy loss and benefit from higher input dimensions.

This paper introduces a self-calibrating random forest model for myoelectric control that improves performance over time with minimal data and user adaptation.

PackDock predicts accurate protein-ligand complex conformations using a diffusion model for side-chain packing, validated through multiple docking experiments.

This paper develops a one-shot random forest model calibration method for hand gesture decoding using minimal user-specific EMG data, achieving higher accuracy than benchmark models.