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Computer Science

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Virtually all your courses will be a research experience within the lab-based curriculum. You will learn in a department that keeps up with the evolution of computer science yet provides a foundation in its underlying principles: mathematics, logic and language.

About the Major

Computer science is the study of how information is organized and processed and addresses the design, analysis, implementation, efficiency and application of algorithms and data structures. The question at the root of computer science is – what can be automated? Hamilton students explore that question through hands-on courses and research that are – like the field itself – constantly evolving. The department regularly revises every course and introduces new ones to examine emerging theories and technologies.

The faculty in the computer science department are really great instructors, and have helped me learn a lot of material, but almost more importantly, are approachable. I feel comfortable going to any of them with questions on their class material.

Katherine Droppa ’16 — computer science and art major

Students focus on both the experimental and theoretical sides of computer science, but they also consider the growing place computing has in the modern world. What are the ethical and social risks and benefits of such technology, and how do we manage them?

Careers After Hamilton

  • Emergency Preparedness Officer, International Atomic Energy Agency
  • Senior Technical Program Manager, amazon.com
  • Vice President, Goldman Sachs
  • Engineering Project Manager, Apple Computer
  • Director of Global Relationship Management, International Lawyers Network
  • Aviator, U.S. Marine Corps
  • Product Manager, YouTube, Google
  • Elearning & Multimedia Developer, Coca-Cola
  • Software Engineer, Monster.com

Contact Information

Computer Science Department

198 College Hill Road
Clinton, NY 13323
315-859-4136 computerscience@hamilton.edu

Meet Our Faculty

Mark Bailey
Chair, Professor of Computer Science

Mark Bailey

Mark Bailey's intro computer security course, Secrets, Lies, and Digital Threats, pairs students with high school classes to teach about dangers of the digital world.

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Alistair Campbell
Associate Professor of Computer Science

Alistair Campbell

Alistair Campbell's work on bioinformatics has appeared in the proceedings of international conferences and workshops.

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Richard Decker
Professor of Computer Science

Richard Decker

Richard Decker and Hamilton Professor of Computer Science Stuart Hirshfield are co-authors of several widely used computer science textbooks.

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Stuart Hirshfield
the Stephen Harper Kirner Professor of Computer Science

Stuart Hirshfield

Stuart Hirshfield was part of a consortium that developed what became the model curriculum for a bachelor's in computer science. 

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A Sampling of Courses

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Contemporary Computing Concepts 100F

The course demonstrates how modern, familiar instances of computing technology–Siri, jpeg files, streaming data, the cloud, hacking, social media, drones, self-driving cars and Watson–all derive from the “big ideas” that make up the field of Computer Science. Topics include what it means to “compute,” building machines to compute, how humans communicate with computers, computer networks, computer security, current and future computer applications. Students will use a variety of programs to experiment with all ideas presented. No knowledge of computer programming required. Quantitative and Symbolic Reasoning.

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Introduction to Computer Science 110FS

The first course in computer science is an introduction to algorithmic problem-solving using the Python programming language. Topics include primitive data types, mathematical operations, structured programming with conditional and iterative idioms, functional abstraction, objects, classes and aggregate data types. Students apply these skills in writing programs to solve problems in a variety of application areas. No previous programming experience necessary. Quantitative and Symbolic Reasoning.

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Discrete Mathematics 123FS

Study of mathematical models and techniques commonly used in computer science. Emphasis on analytical and logical skills, including an introduction to proof techniques and formal symbolic manipulation. Topics include set theory, number theory, permutations and combinations, mathematical induction and graph theory. Topics will be reinforced with hands-on experiences using the ML programming language. Appropriate for students with strong pre-calculus backgrounds. No previous programming experience necessary. Quantitative and Symbolic Reasoning.

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Computer Organization and Assembly Language 240F

A study of the connection between high-level programs and the machines on which they run by means of extensive programming experience using assembly language. Topics will include translation of high-level language idioms into assembly language, number systems and representation schemes, exceptions, interrupts, polling, and an introduction to the structure of the underlying hardware. In the final project, students develop an assembler.

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Operating Systems 340S

Study of the design and implementation of computer operating systems. Students will develop at least four significant projects related to the topics of process scheduling, interprocess communication, memory management, file systems, access control, device drivers and security. Programming intensive.

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Artificial Intelligence 375S

Exploration of AI theory and philosophy, as well as a variety of algorithms and data structures, such as heuristic strategies, logic unification, probabilistic reasoning, semantic networks and knowledge representation. Topics include application areas such as natural language understanding, computer vision, game playing, theorem proving and autonomous agents. Programming intensive.

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