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Computer Science Foundations: The Carnegie Mellon Perspective

This article provides an overview of the Computer Science Ph.D. program at Carnegie Mellon University, including program requirements, unique features, and student outcomes. It also examines the current state of the IT workforce and potential issues facing the field.

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Computer Science Foundations: The Carnegie Mellon Perspective

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  1. Computer Science Foundations for Ph.D. Students The Carnegie Mellon Perspective Randal E. Bryant Carnegie Mellon University http://www.cs.cmu.edu/~bryant

  2. CMU CS PhD Program Students • Demographics • Around 25 new students / year • From ~800 applicants • Approximately 50% US • Top foreign countries: India, China, Korea • Backgrounds • Most have undergraduate or master’s degree in computer science or related field

  3. Program Requirements • Courses • Eight PhD-level courses • One each from list of “star” courses in following areas • Algorithms & complexity • Programming languages • Artificial intelligence • Software systems • Computer systems • Skills • Writing, speaking, programming • Two semesters as teaching assistant • Research • Prepare & defend PhD thesis

  4. Unusual Features of Program • No Qualifying or Comprehensive Exams • Students are admitted directly to PhD program • Very selective admissions • Believe that courses are more useful than exams • Exams are an unreliable measure of understanding • Working on labs and projects more effective than reading a lot of books and papers • Have not found qualifying exams serve intended role • “Is student qualified to pursue a PhD?” • Student Progress Monitored Closely • Students assigned advisors after brief “Immigration” course • Advisor serves as mentor • All students reviewed 2X/year in “Black Friday” meetings • Student progress is collective responsibility of entire faculty

  5. All assume students have undergraduate preparation in subject Most courses targeted specifically to PhD-level students Algorithms & Complexity Algorithms Complexity Theory Artificial Intelligence Advanced AI Concepts Machine Learning Planning, Execution, and Learning Computer Systems Computer Architecture Optimizing Compilers for Modern Architecture Programming Languages Type Systems for Programming Languages Semantics of Programming Languages Software Systems Advanced Operating Systems and Distributed Systems Database Management Systems Networking Star Courses

  6. Outcomes • Graduation • Around 70% of entering students graduate • Average time between 6 & 7 years • Most students graduate as fully formed researchers • Typically 10–20 research publications • Ready to move right into faculty positions • Placements • Most stay in the U.S. • Academic positions • Industry research • Microsoft Research • IBM • Other industry • Google • Start-up companies

  7. IT Workforce Issues • US IT industry is still going strong

  8. US IT Workforce Supply • Perceptions • IT jobs are moving offshore • IT jobs are not exciting

  9. Issues • If Undergraduate Enrollments Continue to Decline … • Supply of U.S. graduate students will be limited • Will need to attract more non-U.S. students • Need for U.S. computer science faculty will decline • Hiring for academic jobs will decrease • But, will still have strong demand for PhDs from industry

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