Lead research and applied ML initiatives focused on building and scaling genomic foundation models, generative design of CRISPR enzymes for gene editing, and high-throughput in silico target screening supporting biological discovery, predictive design, and product development.
Key contributions:
•Led a small, cross-functional team in the design and training of a biologically-informed foundation model for plant genomics, improving predictive performance of gene regulation and enabling automated screening and design workflows across multiple research programs.
•Architected and deployed an internal ML platform integrating protein structure prediction, generative modeling, and workflow orchestration to support high-throughput biological screening in a secure environment.
•Built scalable protein-protein interaction screening and prioritization pipelines that significantly reduced experimental load and accelerated target discovery cycles.
•Developed cross-species transfer learning approaches to integrate and enrich heterogeneous biological datasets, improving inference quality in data-sparse regimes.
•Automated quantitative phenotyping workflows using computer vision and graph-based analysis to transform large collections of noisy images into interpretable features.
