Machine Learning @ CMU: From Theory to Impact

The Machine Learning Department (MLD) at Carnegie Mellon University advances the science, empirics, and real-world applications of machine learning. For more than 25 years, the department has helped define the field of machine learning and artificial intelligence, consistently driving ideas from foundational research into widely used systems.

A defining characteristic of machine learning at CMU is the speed with which theory becomes practice. As department head Zico Kolter explains, “It’s almost hard to talk about there being a separation between research and applications when it comes to a field like AI and machine learning these days.” Advances move rapidly from core innovation directly to tools that shape industry and society.

Research in the department spans a broad range of domains while remaining grounded in fundamental machine learning innovation. “We really touch on almost everything you can imagine in terms of applications,” Kolter notes, including research at “the intersection of AI and biology, of AI and health, of AI and education.” This work is united by its impact, offering students and faculty the ability to translate theoretical advances into technologies that improve lives.

This focus can potentially connect the MLD department to nearly every discipline across CMU. “AI touches on every single field there is right now,” Kolter says, which makes it impossible to view machine learning in isolation. At CMU, machine learning functions both as a foundational science and as a unifying force, shaping research, education, and real-world impact across the university and beyond.