Machine Learning, AI, & Data Science

The Alliance AI Lab develops algorithmic foundations of learning and interaction for safety-assured and collaborative embodied AI systems, working at the intersection of robotics, machine learning, and game theory to enable a future where humans and robots learn and grow together.

The Johns Hopkins Data Science and AI Institute focuses on research and education in data science, machine learning, and artificial intelligence across diverse fields, including neuroscience, precision medicine, climate resilience, sustainability, public-sector innovation, and social sciences.

The IDIES mission is to coalesce data-intensive science efforts at Johns Hopkins into a well-focused center of activity and to propel various fields towards new discoveries and breakthroughs.

Our machine learning community seeks to push forward the state of the art in terms of theory, algorithms, models, and applications with an eye toward solving the most important challenges of our day.

The Malone Center for Engineering in Healthcare brings together engineers, clinicians, and care providers who are leveraging data analytics in novel ways, pioneering new technologies, and applying systems engineering principles to speed the deployment of research-based innovations that will enhance the efficiency, effectiveness, and consistency of healthcare.

The Mathematical Institute for Data Science (MINDS) at Johns Hopkins University brings together a multidisciplinary team of mathematicians, statisticians, computer scientists, and engineers to develop the fundamental mathematical, statistical, and computational principles for the analysis and interpretation of massive amounts of complex, high-dimensional data.

Working at the intersection of embodied AI, machine learning, and computational social cognition, SCAI’s goal is to advance human-centered AI by engineering machine social intelligence to build socially intelligent systems that can understand, reason about, and interact with humans in real-world settings.