(Arnold) Yanuo Zhou

Hi! I’m (Arnold) Yanuo Zhou (周亚诺), a Researcher and ML Engineer specializing in AI for health, drug discovery, and digital phenotyping. I recently defended my MSc thesis in Precision Health and Medicine at the National University of Singapore.

I am actively seeking Research Associate (RA) and PhD opportunities to advance AI-driven innovations in biomedicine and precision health.

Research Interests

• AI for Health (AI4Health): Applying machine learning to healthcare challenges, from digital phenotyping to drug discovery and precision medicine.
• AI-Driven Drug Discovery (AIDD): Graph neural networks for drug-disease link prediction and repurposing using biomedical knowledge graphs.
• Digital Phenotyping & Wearable Sensing: ML models for continuous health monitoring, circadian rhythm analysis, and behavioral pattern recognition from wearable devices.
• Bioinformatics & Omics Analysis: RNA-seq biomarker discovery, pathway enrichment, and cross-cohort validation for precision health.
• Biomedical Signal Processing: Real-time physiological signal analysis (ECG, EEG) with embedded systems and sensor fusion.
• Wearable Non-Invasive Diagnosis & Therapeutics: Developing wearable biosensors and microfluidic devices for real-time health monitoring and point-of-care diagnostics.

Current Work

I’m currently engaged in cutting-edge AI and health research:

• PrimeKG-RGCN Drug Discovery (2025-present): Implementing relational graph convolutional networks for drug-disease link prediction using biomedical knowledge graphs, with GPU-accelerated processing and attention visualization.
• Circadian Health AI (TUM-CREATE, 2024-2025): Developing ML models for wearable spectral data classification achieving AUC 0.93+ with 288-configuration grid search and audit-ready pipelines.
• Psychiatric Biomarker Discovery (NUS, 2024): RNA-seq analysis for suicide risk biomarkers in depression with cross-cohort validation and KEGG pathway enrichment.

Key Technical Skills

AI/ML: PyTorch • PyTorch Geometric • scikit-learn • GNN (RGCN) • CNN (U-Net) • Bayesian Optimization
Bioinformatics: RNA-seq • Knowledge Graphs (PrimeKG) • KEGG Enrichment • NGS Pipelines
Signal Processing: Wearable Sensors • ECG/EEG • Multi-sensor Fusion • Real-time Processing
Reproducibility: Git • Docker • Config-driven Experiments • HPC • Audit-ready Workflows

Selected Awards

• 🏆 PRECISE Scholarship, National University of Singapore (2023)
• 🥉 3rd Prize, 10th National Student Optoelectronic Design Competition (2022)
• 🥈 2nd Prize, 8th National Student Optoelectronic Design Competition (2020)
• 🥈 Silver Award, 12th “Challenge Cup” Jiangxi Student Entrepreneurship Competition (2020)

Get in Touch

Feel free to reach out via email at yanuo.zhou@outlook.com or connect with me on GitHub @arnold117.