Shaadi Mehr
Dr. Shaadi Mehr is an informatics project lead and interdisciplinary scientist specializing in biomedical data systems, genomics, and AI-driven research infrastructure. At Harvard University, she leads the development of VenomsBase, a cloud-native, FAIR-compliant knowledgebase designed to integrate multi-omics data, including genomics, transcriptomics, and proteomics, across diverse venomous species for comparative analysis and therapeutic discovery.
Her work focuses on building scalable architectures that unify heterogeneous biological datasets through standardized metadata, ontology-driven design, and reproducible workflows. She is leading collaborative efforts with AWS and a multidisciplinary team to develop and implement cloud-based pipelines leveraging AWS infrastructure (S3, RDS, Batch) and workflow orchestration platforms (e.g., Terra, Nextflow) to support data ingestion, annotation, curation, and cross-species analysis.
As part of this effort, she leads the development and operationalization of VenomView, an interactive RShiny application that enables visualization, exploration, and querying of integrated venom datasets, supporting both research use and broader community access.
Dr. Mehr is particularly interested in advancing the integration of AI and agentic systems in genomics, exploring how large language models and autonomous workflows can enhance biological data interpretation, automate annotation processes, and enable intelligent knowledge discovery. Her work bridges bioinformatics, data engineering, and AI to support next-generation biomedical research platforms.
Prior to Harvard, Dr. Mehr held leadership roles at the Multiple Myeloma Research Foundation (MMRF), Genentech, and Velsera, where she managed large-scale, multi-institutional precision medicine programs and developed data platforms integrating clinical and molecular datasets. She has over a decade of experience in translational research, biomarker development, and cross-functional program leadership.
Dr. Mehr is also an educator and is committed to training the next generation of scientists in data-driven biology.