At Johns Hopkins, the machine learning community aims to build systems that approach human intelligence, and which comb through massive datasets to answer questions that are beyond the capability of the unaided human mind.  JHU’s researchers are pushing the state of the art in core inference methods and domain-specific modeling techniques. Our faculty and students develop innovative algorithms and fit increasingly nuanced models to empirical data.

21st-century science, engineering, and medicine rely more and more on distilling knowledge and insights from digital data. Our goal as machine learning researchers is to understand underlying scientific phenomena and mechanisms, make optimal decisions, predict the future, detect anomalies, or compensate for noisy or missing data.

“Machine Learning @ JHU” is supported by the Office of the Provost.  It brings together dozens of faculty from across the university to share research problems and techniques; to develop courses that are open to the entire JHU community; to undertake projects together; and to coordinate activities such as invited speakers, faculty hiring, and student advising.