The Computer Science master's program provides students with the tools to develop innovative solutions for today's digital challenges. From building applications to developing large-scale software systems, you'll gain new skills and experience unique learning opportunities across an array of areas.

Program Highlights

The Department of Computer Science provides an open, friendly, diverse, and collaborative atmosphere in which to study computer science. Students personalize their path of study and regularly access professors both in small classes and out of class. With a long history of interdisciplinary learning and research collaboration, the program brings together faculty, students, alumni, staff, and guests from across the field and beyond.

Students in the program can choose to complete a master's thesis or a project-based study track. The program can be completed in as little as one year, or two years with an optional thesis.

30 credits are required, of which at least 24 credits must be from approved courses. The remaining 6 credits may be earned from approved courses, or from conducting approved research or independent study, or from a combination of the two.

Career Outcomes

The master's program is designed for students seeking the skills and credentials to reach new job opportunities, to be promoted within their current organization, earn a higher average salary, or to pursue advanced research within a doctoral program. Students graduate from the program prepared to become leaders in industry, academia, and research.

Application Requirements

Application Fee
Resume/CV
Personal Statement
Transcripts
Three letters of recommendation
Official GRE scores (if applicable)
- GRE General Test scores not required for applicants who will have received a degree from a U.S. or Canadian institution by time of enrollment. GRE scores required for all other applicants.
Official TOEFL, IELTS, or Duolingo test scores (if applicable)
Portfolio (optional)

Tuition and Financial Aid

We recognize that attending graduate school involves a significant financial investment. Our team is here to answer your questions about tuition rates and scholarship opportunities.

Please contact us at gradadmissions@tufts.edu.

Faculty

Jeffrey Foster

Professor and Chair of Computer Science

Programming languages, software engineering, security

Martin Allen

Associate Teaching Professor

Artificial intelligence, machine learning, reinforcement learning.

Remco Chang

Associate Professor

Data visualization, visual analytics, human-computer interaction, databases, computer graphics

Ming Chow

Associate Teaching Professor

cyber security, web engineering

Lenore Cowen

Professor

data science, graph algorithms, distributed algorithms, approximate routing, classification and clustering for high-dimensional data, coloring and its generalizations, computational molecular biology

Ethan Danahy

Research Associate Professor

design, implementation, and evaluation of different educational technologies

J.P. de Ruiter

Professor of Psychology and Computer Science

Cognition and Psycholinguistics

Fahad Dogar

Associate Professor

Improving performance and reliability of networked systems, specifically cloud-based systems, mobile and wireless systems, and the Internet. Also, interested in designing technologies for developing regions.

Karen Edwards

Assistant Teaching Professor

low-dimensional geometric topology

Samuel Guyer

Associate Professor

Programming languages, program analysis, software reliability, application security, runtime systems, memory management, domain-specific programming languages, compilers, compiler optimizations, program performance

Soha Hassoun

Professor

computational methods for systems biology and metabolic engineering, computer-aided design for integrated circuits

Bert Huang

Assistant Professor

Machine learning, structured output learning, algorithmic fairness, weak supervision, probabilistic inference, graph mining, social media analysis, data science, big data, computational social science.

Michael Hughes

Ann W. Lambertus and Peter Lambertus Assistant Professor

Machine learning : probabilistic models, Bayesian inference, variational methods, time-series analysis, semi-supervised learning Clinical informatics : electronic health record analysis

Robert Jacob

Professor

human-computer interaction, new interaction modes and techniques, implicit brain-computer interfaces, user interface software

Academic Leave

David Lillethun

Assistant Teaching Professor

computer science education, distributed systems, operating systems, networked systems, software development, secure systems and networking

Liping Liu

Assistant Professor

Data Science, Machine Learning, Bayesian Methods, Deep Learning, Graph

Noah Mendelsohn

Professor of the Practice

distributed systems, operating systems, World Wide Web

Megan Monroe

Associate Teaching Professor

data, visualization, language

Norman Ramsey

Associate Professor

Programming languages and systems; functional programming

Academic Leave

Raja Sambasivan

Ankur and Mari Sahu Assistant Professor

Cloud computing, evolvability, debugging distributed systems.

Matthias Scheutz

Karol Family Applied Technology Professor

Artificial intelligence, artificial life, cognitive modeling, foundations of cognitive science, human-robot interaction, multi-scale agent-based models, natural language understanding.

Mark Sheldon

Associate Teaching Professor

programming languages, software systems, concurrency, distributed information systems

Elaine Short

Clare Boothe Luce Assistant Professor

Algorithmic human-robot interaction for robust and socially appropriate assistance to human users, especially users with disabilities.

Jivko Sinapov

Assistant Professor

Artificial Intelligence, Developmental Robotics, Computational Perception, Robotic Manipulation, Machine Learning, Human-Robot and Human-Computer Interaction

Academic Leave