Yi Jia

Director, Predictive Modeling @Inari

Strategy development for novel breeding methodologies with machine learning and genome editing - Collaborate cross-function teams to develop data and machine learning strategy for product development at Inari- Explore AI/ML frameworks to build biology informed genome prediction model and genome association models for insights and prediction- Genome characterization with both genotype and phenotype data to develop targeted breeding strategies with genome editing. Optimize data and prediction models to maximize the selection response in genomic selection - Characterize and optimize machine learning training set for genomic selection- Develop strategies to integrate external partners’ germplasm and breeding data for genome selection- Improve hybrid prediction models with integrating non-additive effects, multi-trait and multi-env data. Drive genome prediction applications to accelerate both population and product development - Optimize breeding cross prediction model with rapid recycling to fasten line development- Enhance hybrid product evaluation by integration of agronomic trait and yield stability prediction- Collaborate with breeders to optimize process & design trials based on genome prediction- Build genome prediction models for both maize and soybean transformability to support editing pipeline. Organization effectiveness - Build and develop the predictive modeling team by talent recruitment and development- Drive innovation and implementation in collaboration with cross function teams- Lead by influence by effective communication with both team members and leadership.