The Data Science group in the Applied Mathematics and Statistics Department at Johns Hopkins University is a vibrant community of researchers whose interests span classical statistics, machine learning, optimization, network inference, and computational biology. Our group’s interdisciplinary approach incorporates theory and practice and unites researchers across different backgrounds to develop cutting-edge methodologies for analyzing complex data sets. From working on real-time delivery of precision healthcare to identifying structural changes in billion-node networks to helping food banks operate more effectively to everything in between, the Data Science group at Johns Hopkins is at the forefront of the field, advancing our understanding of data science and its potential to shape our world for the better.
Research Affiliations at Johns Hopkins University
- JHU Applied Physics Laboratory
- Institute for Computational Medicine
- Center for Imaging Science
- Institute for Data Intensive Engineering and Science
- Mathematical Institute for Data Science
- Ralph O’Connor Sustainable Energy Institute
- Malone Center for Engineering in Healthcare
- 21st Century Cities Initiative of the President
- Precision Medicine Center of Excellence
Related Courses
Complete descriptions appear in the course catalog.
View the semester course schedule.
EN.553.767: Iterative Algorithms in Machine Learning: Theory and Applications
EN.553.669: Large-Scale Optimization for Data Science
EN.553.432/632: Bayesian Statistics
EN.553.733: Nonparametric Bayesian Statistics
EN.553.743: Equivariant Machine Learning
EN.553.413/613: Applied Statistics and Data Analysis
EN.553.450/650: Computational Molecular Medicine
EN.553.742: Statistical Inference on Random Graphs
EN.553.740: Machine Learning I
EN.553.741: Machine Learning II
EN.553.662: Optimization for Data Science
EN.553.436/636: Introduction to Data Science
EN 553.633: Monte Carlo Methods
EN 553.763: Stochastic Search & Optimization
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Research and academic opportunities in data science