Click on your instructor’s name to access your section’s course information. Section Time Location Instructor 01: Lecture MWF 11:00-11:50 AM DANA 227 Fuchsberger 02: Lecture MWF 3:00-3:50 PM DANA 227 Havill L60: Lab T 1:00 – 2:50 PM BRKI 164 Fuchsberger L61: Lab T 3:00 – 4:50 PM BRKI 164 Havill

Distributed Computing Catalog Description An introduction to concurrency, communication, and fault-tolerance. Students learn fundamental models of distributed computing and use them to study classic problems and their solutions or impossibility. Examples include consensus, mutual exclusion, distributed data structures and more. We focus primarily on theoretical results, also applying them in practical implementations. Prerequisite: CSCI 311 Schedule Section Time Location Lecture MWF 2-2:50PM Dana 134 Syllabus Syllabus PDF Here Lecture Notes These notes are based on Hagit Attiya and Jennifer L. Welch’s Distributed Computing: Fundamentals, Simulations, and Advanced Topics, as well as the linked papers. You are welcome to use these […]

Course information Syllabus Daily schedule All of the assignments for the course will be listed here. Please bookmark this page and refer to it daily. Getting help Helpful Python websites About me A long time ago, I was a CS major at Bucknell!  (I also majored in Religion.)  After graduation, I went to graduate school for CS at The College of William and Mary.  My research was in the design and analysis of online algorithms.  After earning my Ph.D., I taught for many years at a small liberal arts college in Ohio.  Now I am thrilled to be back at Bucknell. […]

Course information Syllabus Course work All of the assignments for the course will be listed on the daily schedule. Please bookmark this page and refer to it daily. The reading assignments are from the textbook listed below. The homework assignments and labs are in PrairieLearn. Getting help TA hours (in Dana 325) Textbook We will be using a draft of the third edition of Discovering Computer Science this semester. It is intended for use only in this class this semester; please do not post or redistribute in any form. Helpful Python web sites Exam review Final project About me A […]

Course information Syllabus Course work All of the assignments for the course will be listed on the daily schedule. Please bookmark this page and refer to it daily. The reading assignments are from the chapters listed on the general course page and below. The homework assignments and labs are in PrairieLearn. Getting help Draft textbook chapters The links below are to drafts of the third edition of Discovering Computer Science. They are intended for use only in this class. Files and links referenced in the text can be found here. Helpful Python web sites Exam review Final project About me […]

Fill out this Google Form when you are ready to submit your proposal for a CSCI project. Proposals are due by August 15, 2025! The Department of Computer Science at Bucknell University invites partners in academia and industry to submit project proposals for student teams to work on as part of their senior design experience. Projects must be significant in scope to cover an entire academic year (Fall 2025 through Spring 2026) for CSCI 475/476. Senior design is the final culminating experience for our students, bridging the transition from academia to professional practice. Your projects offer students perhaps their most […]

A Reel Discovery A Bucknell team is preserving rare, fragile Japanese films, making cinematic history available to a new generation. Computer science students Yuhan Chen ’23 and Jackson Rubiano ’27 developed software to recognize frames and stabilize the images, ensuring the films could be projected in a fluid sequence.

Data’s Creative Twist Data science isn’t just for engineers or analysts. It has the power to advance many different fields of study, sometimes in surprising ways. For example, it can help psychologists better understand children’s movement patterns or inspire creative writing. Showcasing the versatility of data science is a goal of Kelly McConville, director of the Dominguez Center for Data Science. Students who participate in the Data Science Student Fellows program are matched with project stakeholders and data-science mentors who guide fellows through technical and theoretical approaches to data-centric problems. Fellows are encouraged to apply their diverse perspectives to develop solutions. […]

“Are behavioral biometrics systems biased toward different demographics? Are they robust enough to withstand sophisticated attacks and secure private information? These are the questions I am attempting to answer with my students here at Bucknell.” Technology is such a ubiquitous part of modern life that our devices often serve as extensions of our physical selves. In fact, many of us have already linked our unique physical characteristics to our phones and laptops, with fingerprint scanning and facial recognition constituting widely adopted methods for enhancing device security. These physical biometrics, however, are not infallible. Instead, the future of biometrics could lie […]

Getting students to buy into a class, getting them to buy into the content, pushing them beyond what they think they’re capable of — that is what drives my work. Professor Sam Gutekunst, the John D. and Catherine T. MacArthur Assistant Professor of Data Science, greets everyone who enters his office with the same cheerful invitation: “Would you like a Spindrift?” https://www.bucknell.edu/meet-bucknell/bucknell-stories/faculty-stories/sam-gutekunst-mathematics-and-computer-science