Skip to Content

Biomedical Data Science

Biomedical Data Science involves the analysis of large-scale biomedical datasets to understand how living systems function. Our academic and research programs in Biomedical Data Science center on developing new data analysis technologies in order to understand disease mechanisms and provide improved health care at lower costs.

Education in Biomedical Data Science

Our curriculum trains students to extract knowledge from biomedical datasets of all sizes in order to understand and solve health-related problems. Students collaborate with faculty throughout the schools of Medicine and Engineering to develop novel cloud-based technologies and data analysis methods that will improve our ability to diagnose and treat diseases.

Research in Biomedical Data Science

Our students and faculty are pioneering new methods to analyze large-scale biomedical datasets, shedding new light on the function of living systems. Key research areas include:

  • Computational Science

    We are working at the interface between computer science, mathematics, and biomedical engineering to advance computing technology to address broad questions in personalized medicine.

  • Machine Learning and Data Science

    We are creating high-throughput software for extracting symbolic and ontologic information from massive datasets using machine learning.

  • Biomedical Data

    We are integrating biomedical data with high-performance computing to analyze several terabytes of data involved with modern machine learning and artificial intelligence tools.

  • Science as a Service

    We are delivering scientific solutions integrated in software by developing novel cloud-based technologies for sharing datasets and tools.

  • Biomedical Clouds

    We are building key resources to improve the quality of healthcare delivery. For example, Computational Critical Care Medicine and PhysioCloud have become models for critical care units, and MRICloud provides a high-throughput magnetic resonance image analysis technology for integration into radiological workflows.

Read the Johns Hopkins University privacy statement here.