Shaun Chen shaunfchen

Multimodal AI Scientist specializing in the intersection of Multi-omics and Precision Medicine. I architect explainable AI models and scalable bioinformatics pipelines to transform high-dimensional biological data into clinical insights. Lead author of the Nature Medicine cover study on CAD risk prediction using 1M+ biobank records. Currently driving GenAI-enabled target-indication reporting, multi-omics biomarker discovery, and data-lake initiatives at Takeda, building agentic AI workflows and scalable bioinformatics infrastructure that connect multi-modal data to translational strategy and portfolio decision-making. Selected Impact: - Nature Medicine (2025): Developed explainable meta-prediction framework integrating polygenic and clinical risk. - 4x Faster Imputation (2022): Engineered a deep learning architecture for reference-free genotype imputation. - Clinical Scale: Implemented production pipelines delivering real-time results for the MyGeneRank clinical platform. - Previous Industry Projects: Led multi-omics biomarker discovery and production-grade analytics module development, including PrismatiQ™, to support translational and clinical decision-making. Technical Stack: Python, R, Nextflow, AWS, Docker, HPC/Slurm, Scikit-learn, LLM/agentic workflows, foundation models, knowledge graphs, multi-omics pipelines.

Cambridge, MA, US

Senior Scientist, Neuroscience Computational Biology

Takeda

Takeda (United States)

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