Computer Science | July 5–24, 2026
Programming and Beyond
Computer Science is a rich academic field that studies the computational structures and processes that appear throughout the natural and human worlds. Seeking Computational Solutions: Programming and Beyond approaches computer science with a liberal arts perspective, looking more broadly than programming alone.
Students who attend the program will learn about finding computational solutions to two broad areas of interest: problems where the computer needs to perceive something about the world in which it exists, or alternatively, problems where the computer needs to respond appropriately given a complex scenario. Participants will do this by attending classes, participating in hands-on lab activities, and working in small teams on projects directed by college faculty and mentored by undergraduate teaching assistants.
The program culminates with a research symposium where students share the results of their work with each other and the broader community.
Course Pre-Requisite Guide
Students who enroll for SLAI CS are expected to have some previous experience in coding. Assess if you are prepared to apply using the SLAI CS Pre-Requisite Guide. If you have questions about your experience, please email summer@carleton.edu
Academic Structure
Of the multiple course topics listed on this page, Summer Carls will explore some topics in morning classes and one topic in an afternoon research group.
View SLAI’s Academic Structure Guide to learn more about how you can shape your program experience to fit your interests this summer.
Academic Credit
Summer Carls can earn up to six Carleton course credits (typically transfers as three semester credits) for successfully meeting faculty expectations and completing course requirements. In addition to receiving written feedback about course performance from faculty, students will receive one of the following three possible grade designations: satisfactory (S), credit (Cr), or no credit (NC). Formal academic transcripts are available upon request for Summer Carl alumni and will reflect the name of the course and grade earned.
Want to experience Carleton without a graded outcome? Check out our 1-Week Non-Credit Programs!
Program Director: Dave Musicant, Professor of Computer Science, Carleton College
Dave Musicant is a professor of computer science at Carleton, where he has been since 2000. He has been directing the SLAI Computer Science program for 13 years. He received his Ph.D. in computer sciences from the University of Wisconsin-Madison in 2000.
His research interests span across solving machine learning and data mining problems, collaborative human/computing systems, and computer science education. Dave regularly teaches Introduction to Computer Science, Data Structures, Artificial Intelligence, Database Systems, Programming Languages, Parallel and Distributed Computing, Data Mining, and “Art, Interactivity, and Microcontrollers.”
Courses and Research Topics
Click on each topic below to view the course description and faculty information.
Computer Vision
Computer vision examines how a computer can manipulate images to enhance them, to restore or combine images, and to extract meaningful information from images. Applications of computer vision are increasingly common in everyday life: face detection and recognition, matching photos containing similar objects, robot and autonomous vehicle navigation, or detection and tracking of objects in a video.
In this course, we’ll learn how to manipulate images to extract basic information. We will build toward more advanced computer vision topics, including classifying images or detecting objects within them. Students will work in teams to solve a computer vision problem.
Faculty: Susan Fox, Professor of Computer Science, Macalester College
Susan Fox has taught at Macalester College in Saint Paul since 1995. She has a BA in Computer Science from Oberlin College, and a Master’s and PhD in Computer Science from Indiana University.
Susan’s specialty is artificial intelligence, and she works with robotics, machine learning, and computer vision. She teaches introductory computer science, courses on algorithms and theory of computation, and advanced courses in artificial intelligence and robotics.
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