Public profile
Research areas
Theoretical Computer Science, Statistical Inference, Learning Theory, Differential Privacy, Hypothesis Testing
Computer Science
Michael B. Yuen and Sandra A. Tsai Assistant Professor
Member, Ken Kennedy Institute
Average rating
4.0
3 temporary mock ratings
Difficulty
2.8
course-linked average
Courses
5
in seeded sections
Theoretical Computer Science, Statistical Inference, Learning Theory, Differential Privacy, Hypothesis Testing
COMP 382
Writing algorithms is fun, but how are you sure that the algorithm you wrote is flawless? Are there computing tasks for which it is impossible to produce an efficient algorithm, or, for that matter, any algorithm? To answer these questions, you have to learn to perform mathematical reasoning about algorithmic problems and solutions COMP 382 is an introduction to such reasoning techniques. Topics covered would include elementary logic, analysis of the correctness and efficiency of algorithms, and formal computational models like finite automata and Turning machines. On the way, you are also going to learn some new algorithm design techniques. Cross-list: COMP 382.
COMP 390
Theoretical and experimental investigations under staff direction. Repeatable for Credit.
COMP 590
Advanced theoretical and experimental investigations under staff direction. The student must have a full-time internship to receive 4 credits for this course. Repeatable for Credit.
COMP 800
Repeatable for Credit.
ELEC 800
Repeatable for Credit.