Algorithms and Complexity This course focuses on the development of correct and efficient algorithmic solutions to computational problems, on the underlying data structures to support these algorithms, and on the social implications of algorithms. Topics include computational complexity, analysis of algorithms, proof of algorithm correctness, some advanced data structures, algorithmic techniques including greedy and dynamic programming, and the consequences of real-world applications of algorithms. The course complements the treatment of NP-completeness in CSCI 0301.

Computational Complexity We will investigate the computational power of various resources. Examples include determining whether a computer with limited time or limited space can solve more difficult problems, attempting to characterize creativity as a resource, and comparing probabilistic and quantum computation. We will learn why we do not yet have answers to many of the most fundamental questions in computational complexity (like whether P=NP), and we will think critically about the value of studying these topics. Students enrolled in the College Writing (CW) section of the course will explore these ideas through writing, in particular, in three contexts that are critical for theoretical computer science: the proof (expert audience), the review paper (non-expert computer science audience), and the popular science article (educated public audience). (CSCI 0301).

Quantum Computing In this course we will explore how quantum mechanics can be applied to problems in communications, algorithms, detection, and cryptography. We will learn how features such as entanglement, superposition, and no-cloning can sometimes give quantum systems an advantage over standard “classical” computers. We will also discuss the current situation and challenges facing experimental quantum computers, as well as the limits of quantum computing. No previous experience with quantum mechanics is required. (MATH 0200) 3 hrs lect./disc.

Shelby Kimmel

Reviews and ratings for Shelby Kimmel at Middlebury College.Shelby Kimmel teaches Algorithms and Complexity, Computational Complexity, Quantum Computing and more.  Explore top rated instructors and find the best for you.Is Shelby Kimmel good ? Find out on MiddCourses.

Kimmel was awesome, she moved quickly but also always made sure that everyone understood the concepts. She also allowed for unlimited revisions of work before the end of the semester and we didn't have exams which made the course a lot more low stress.

Professor Kimmel grades based on effort. That makes getting a good grade in the class very manageable as long as you stay on top of assignments. The topics covered in class are inherently challenging, but the group work and lectures set you up pretty well. I would recommend taking algorithms with Professor Kimmel 100%.

Professor Kimmel is an extremely accommodating professor in the computer science department. I did not expect to learn as much as I did for the course, but by the end, I had learned a great deal. Ungrading leans on the intrinsic motivation of students to learn and I surprisingly found this within myself in a theory heavy course. 