Eric Chen

About Eric Chen

I design and deploy large-scale, GPU-accelerated AI systems that translate advanced machine learning into usable tools for healthcare and life sciences. My work sits at the intersection of foundation models, high-performance computing, and clinical data, with a focus on making complex AI systems reliable, scalable, and decision-supportive in real-world settings.I hold a Ph.D. in Computational Biology from Carnegie Mellon University’s School of Computer Science, where my work focused on developing and evaluating machine learning models for spatial and multimodal biological data. At St. Jude Children’s Research Hospital, I lead the development and benchmarking of GPU-accelerated AI platforms that support clinicians and researchers, bridging model development, infrastructure, and downstream adoption.Beyond building models, I work closely with domain experts to translate requirements into scalable AI systems, evaluate tradeoffs, and communicate system capabilities and limitations to technical and non-technical stakeholders. I’ve also contributed open-source tools used by the research community and mentored early-career scientists on applying AI in biomedical contexts.

Experience

1. Senior Bioinformatics Research Scientist

   St. Jude Children's Research Hospital

   Dec 2023 — Present · Memphis, TN, US

   Designed and owned end-to-end AI systems for digital pathology, spanning data ingestion, GPU-accelerated model inference, evaluation, and interactive visualization to support clinical and research workflows.• Built an AI-guided annotation platform integrating active learning and GPU-optimized inference to accelerate expert labeling and reduce manual review effort in digital pathology workflows.• Developed and operationalized scalable AI/ML pipelines for large-scale biomedical imaging and spatial omics, balancing model performance, computational cost, and usability for downstream clinical research teams.• Evaluated and optimized foundation models for visual search and representation learning in biomedical imaging, translating research models into benchmarked, deployment-ready components within real-world systems.

Education

• Carnegie Mellon University School of Computer Science

  PhD, Computational Biology

  2019 — 2023

• Texas A&M University

  MS, Electrical Engineering

  2015 — 2018

• South China University of Technology

  BE, Electrical Engineering

  2011 — 2015