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Carnegie Mellon University

Computer Science Foundations for Ph.D. Students The Carnegie Mellon Perspective. Randal E. Bryant. Carnegie Mellon University. http://www.cs.cmu.edu/~bryant. CMU CS PhD Program Students. Demographics Around 25 new students / year From ~800 applicants Approximately 50% US

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Carnegie Mellon University

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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. CS PhD 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

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