I joined the Applied Bioinformatics Group in 2026 for my PhD project. My previous research focused on computer vision and digital phenotyping, including the development of lightweight and mobile deep-learning tools for wheat disease assessment, hierarchical leaf-vein segmentation, and image-based livestock phenotyping. My PhD research focuses on interpretable and robust genomic prediction in wheat, which stands at the intersection of artificial intelligence, quantitative genetics, and crop breeding.
| Title | Year | Published by | Link |
|---|---|---|---|
| Heterogeneous Deep-Ensemble Framework for Sentiment Analysis of Movie Reviews Based on Stacking and Voting | 2026 | Concurrency and Computation: Practice and Experience | DOI |
| FHBDSR-Net: Automated Measurement of Diseased Spikelet Rate of Fusarium Head Blight on Wheat Spikes | 2025 | aBIOTECH | DOI |
| Smartphone-based digital phenotyping for genome-wide association study of intramuscular fat traits in longissimus dorsi muscle of pigs | 2024 | Animal Genetics | DOI |
| Revealing Hierarchical Structure of Leaf Venations in Plant Science via Label-Efficient Segmentation: Dataset and Method | 2024 | Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence | DOI |
| StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust | 2024 | Plant Phenomics | DOI |