University of Pittsburgh

About ISP

Who Are We?

The Intelligent Systems Program (ISP) is a multidisciplinary graduate program at the University of Pittsburgh dedicated to applied artificial intelligence (AI).

Many of Pitt's acclaimed schools are represented through our associated faculty, including the School of Medicine, the School of Law, the School of Education, the School of Information Sciences, the Swanson School of Engineering, and the Kenneth P. Dietrich School of Arts and Sciences.

What Do We Offer?

  • Broadly interdisciplinary approach: We offer a strong, well balanced foundation in the fundamentals of AI and many opportunities for advanced research and training in many disciplines, including computer science, biomedical informatics, cognitive psychology, information science, education, law, and more.
  • Focused, customized curricula: Building on the core curriculum, students design their own personalized curricula that prepare them for interdisciplinary research in their areas of interest.
  • Collaborative atmosphere: Faculty members and students present their research in regular program seminars, exposing students to a broad range of research topics and methods and affording them the opportunity to present their own research.
  • Highly motivated faculty: Pitt's widely published ISP faculty are leaders in their fields. Drawing on the strengths of diverse sectors of the university, and participating in over thirty funded research projects, they support graduate students through collaborative research, personal mentoring, and external research funding.

Featured Research Group

Machine Learning and Decision Making

Researchers in the Machine Learning GroupThe Machine Learning and Decision Making Group develops new methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. They apply these methods to applications in disease outbreak surveillance, treatment error detection, high throughput genomic and proteomic data analysis, the monitoring and learning of traffic flows, diagnosis, and strategic financial planning within organizations.

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