Designed for students with a strong background in computer science, mathematics, or a related technical field, the MS in artificial intelligence curriculum covers core AI concepts and techniques — including machine learning, deep learning, natural language processing, computer vision, and knowledge representation.
Students gain hands-on experience through projects, co-ops and internships, learning to apply AI algorithms and tools to real-world programs. Coursework balances the theoretical foundations of AI with the practical skills, ethics and societal impacts of our world today.
The program includes a core curriculum and department-specific tracks. Track One is housed in the Department of Computer Science and Track Two is housed in the Department of Electrical and Computer Engineering.
Core Curriculum: Foundational concepts in artificial intelligence, machine learning, ethics, and mathematics.
Track One: Â Foundations and applications of machine learning and artificial intelligence from a computational perspective, with a strong emphasis on the broader social context in which AI technologies are developed and deployed.
Track Two: Core principles of machine learning and artificial general intelligence with specialized engineering domain knowledge, covering both fundamental and systems concepts in AI and how to apply these methods to diverse domains.
Students will develop an ability to understand, implement, and deploy a wide range of AI technologies across disciplines, and they'll have the opportunity to work closely with faculty every step of the way. Collaborate with and learn from some of the most renowned experts in the country, all while seamlessly transitioning from graduate studies to a full-time career.
The School of Engineering's Graduate Cooperative Education (Co-Op) Program provides students with the opportunity to apply the theoretical principles they have learned in their coursework to real-world engineering projects. Gain up to six months of full-time work experience, build your resume, and develop a competitive advantage for post-graduation employment. Learn more about the Co-Op Program.
In recent years, the prevalence of AI has skyrocketed, with a wide range of industries adopting AI technology. The U.S. Bureau of Labor Statistics estimates that the field has grown 28% annually since 2014. An master's degree in artificial intelligence from Tufts University will give you a competitive advantage in a dynamic job market, all while giving you an opportunity to help develop a responsible AI future.
Graduates of the MS in AI program will be capable of understanding, implementing, and deploying a wide range of AI technologies, with an eye towards the underlying ethical and social contexts. Graduates will be poised to go directly into both industry and science, or continue on to do advanced research at a doctoral level.
What do you get at Tufts? A rigorous engineering education in a unique environment that blends the intellectual and technological resources of a world-class research university with the strengths of a top-ranked liberal arts college. Tufts University is one of the nation’s top research universities, earning a "tier 1" classification from the Carnegie Foundation and membership within the Association of American Universities (AAU).Â
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.
Average Salary: $106,386
Tufts University Alumni: 125,000+ worldwide
*Source: Average salary statistic is derived from recent AI Engineering job listings submitted to ziprecruiter.com.
Research/Areas of Interest: Programming languages, software engineering, security
Research/Areas of Interest: Interaction of light with matter, physics of nanostructures and interfaces, metamaterials, material science, plasmonics, and surfactants, semiconductor photonics and electronics, epitaxial crystal growth, materials and devices for energy and infrared applications.
Research/Areas of Interest: data science, statistical signal processing, inverse problems, compressed sensing, information theory, convex optimization, machine learning, algorithms for geophysical signal processing, compressed sensing architectures and evaluation, video and image data acquisition and processing
Research/Areas of Interest: Artificial intelligence, machine learning, reinforcement learning.
Research/Areas of Interest: Statistical- and physics-based signal and image modeling and processing, tomographic image formation and object characterization, and inverse problems. Applications explored include human performance assessment, materials science, airport security, medical imaging, environmental monitoring and remediation, unexploded ordnance remediation, and automatic target detection and classification.
Research/Areas of Interest: Machine learning : probabilistic models, Bayesian inference, variational methods, time-series analysis, semi-supervised learning Clinical informatics : electronic health record analysis
Research/Areas of Interest: Optimization and Control, Machine Learning, Signal Processing, Graph Theory, Decentralized Algorithms
Research/Areas of Interest: design of silicon-based mixed-mode VLSI systems (analog, digital, RF, optical), analog signal processing, and optoelectronic system-on-chip modeling and integration for applications in optical wireless communication and biomedical imaging
Research/Areas of Interest: Artificial intelligence, artificial life, cognitive modeling, foundations of cognitive science, human-robot interaction, multi-scale agent-based models, natural language understanding.
Research/Areas of Interest: human-robot interaction, accessibility, robotics, human-in-the-loop machine learning, assistive technology Applying human-centered design and disability community values to the development, deployment, and evaluation of AI and machine learning for robotics, including: human-centered human-in-the-loop machine learning; disability-friendly assistive robotics; autonomous HRI in groups, public spaces, and other human-human contexts; and accessibility and disability inclusion in robotics education and the computing research community.
Research/Areas of Interest: Artificial Intelligence, Developmental Robotics, Computational Perception, Robotic Manipulation, Machine Learning, Human-Robot and Human-Computer Interaction
Research/Areas of Interest: Cognition and Psycholinguistics