Computer Science

Meet Our Faculty

Chair, Associate Professor of Computer Science

Alistair Campbell

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

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.

Assistant Professor of Computer Science

Thomas Helmuth '09

Thomas Helmuth ’09 focuses his research on genetic programming, a subfield of artificial intelligence.

Visiting Assistant Professor of Computer Science

David Perkins

Among David Perkin's favorite academic inventions are courses that link mathematics to other disciplines.

Assistant Professor of Computer Science

Darren Strash

Darren Strash researches graph algorithms for large social networks and web-crawl graphs.

Professor of Computer Science (retired)

Richard Decker

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

Professor of Computer Science Emeritus (retired)

Stuart Hirshfield

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

A Sampling of Courses

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.

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.

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.

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.

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.

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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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 123S

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 240S

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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Today and Tomorrow

Connections and Careers

Because Hamiltonians are Tech Leaders: Eseosa Asiruwa ’18

Read The Story

Dave perkins comp sci Dara Levy '23, Yassine Dhouib ’24 , Dave Perkins

Research and Discovery

Rewriting the Algorithm Industry Standard

“I kid you not — we are doing something that’s never been done before,” said Yassine Dhouib ’24 about the research that he, Dara Levy ’23, and Professor of Computer Science Dave Perkins are conducting this summer. The trio are working on two different projects in the field of computer science, aiming to improve and streamline industry-standard algorithms.

Connections and Careers

Software Engineer at Bloomberg is Next for Friedman ’21

When Ryan Friedman ’21 entered Hamilton, he had no idea what he wanted to study, much less pursue after college. Now, the computer science major knows what he is passionate about and is an incoming software engineer at Bloomberg.

Hamilton College

About the Major

Careers After Hamilton

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