Scholar

Xiaowei Yu

Google Scholar ID: Kc1FjToAAAAJ

Assistant Professor, Missouri University of Science and Technology

brain-inspired AIartificial general intelligencemedical AIsignal processing

Citations & Impact

All-time

Citations

1,128

H-index

i10-index

Publications

Co-authors

list available

Contact

Publications

20 items

Browse publications on Google Scholar (top-right) ↗

Resume (English only)

Academic Achievements

Publications include:
- Domain-Adaptive Diagnosis of Lewy Body Disease with Transferability Aware Transformer
- Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation
- Core-Periphery Multi-Modality Feature Alignment for Zero-Shot Medical Image Analysis
- InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning
- Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices
- Eye-gaze Guided Multi-modal Alignment Framework for Radiology
- CP-CLIP: Core-Periphery Feature Alignment CLIP for Zero-Shot Medical Image Analysis
- Gyri vs. Sulci: Core-Periphery Organization in Functional Brain Networks
- NoisyNN: Exploring the Impact of Information Entropy Change in Learning Systems
- Robust Core-Periphery Constrained Transformer for Domain Adaptation
- Supervised Deep Tree in Alzheimer’s Disease
- Disentangling Spatial-Temporal Functional Brain Networks via Twin-Transformers
- Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network
- Free water in T2 FLAIR white matter hyperintensity lesions

Research Experience

Assistant Professor in the CS department at Missouri University of Science and Technology

Education

Ph.D. in Computer Science from UT-Arlington; Master's degree from Shanghai Jiao Tong University; Bachelor's degree from Northwestern Polytechnical University

Background

Research Interests: The intersection of artificial intelligence and brain science, with an emphasis on brain-inspired AI and AI for Biomedical and Health Sciences. Current research covers brain network analysis, classification, domain adaptation/generalization, semi-supervised learning, and LLMs.

Miscellany