Focuses on developing advanced computational models to accurately predict gene structures and protein abundance in plants, enhancing our understanding of plant genomics and providing valuable insights into crop biology and evolution.

• Gene Structure Prediction: Utilize large language models to predict plant gene structures, aiming to improve the accuracy and performance of genome annotation tools compared to traditional methods.
• Protein Abundance Modeling: Extends to modeling the protein abundance of highly expressed genes, incorporating evolutionary insights to predict protein levels across diverse plant species, with a special focus on agriculturally important crops.

Analysis of IbHypSys-mediated MicroRNAs upon Wounding in Sweet Potato (Ipomoea batatas cv. Tainung 57)

• Target Gene Development: Design and implement algorithms to accurately identify and predict plant microRNA target genes from sequencing data.
• Experimental Validation: Clone microRNA precursors and target genes into vectors and perform transgenic over-expression to evaluate the inhibitory effects of microRNAs on target gene expression.

I have a double minor in Management Information Systems and Applied Economics.

• Management Information Systems: Gained expertise in data management, systems analysis, and information technology strategies to optimize business processes.
• Applied Economics: Developed strong analytical skills through economic modeling, data analysis, and the application of economic theories to real-world scenarios.

Research in Agronomy

• Experimental Design and Implementation: Conducted controlled experiments to assess the impact of various hydrolysis salts on the osmoregulation of rice seedlings under stress conditions.
• Data Analysis and Interpretation: Analyzed experimental data to determine the effectiveness of hydrolysis salts in mitigating osmosis stress, contributing to improved agronomic practices for rice cultivation.