Master of Science in Artificial Intelligence (AI)

Master of Science (On Campus or Online)
WPI Professor Elke Rundensteiner works with students on an NSF-funded project on fairness in artificial intelligence.

WPI Professor Elke Rundensteiner works with students on an NSF-funded project on fairness in artificial intelligence.

Prepare for the AI Career Opportunities of the Future 

We’re all experiencing the transformative impact artificial intelligence is having in our everyday lives and to nearly all sectors of the economy. Are you prepared to take advantage of the opportunities AI presents in your professional career? Leveraging decades of AI expertise, WPI is offering an MS in Artificial Intelligence. Through project-based courses and either a capstone or an MS thesis, you’ll grow your technical expertise in understanding, developing, deploying, and innovating AI techniques and systems with a responsible approach in this rapidly growing area.

WPI's MS in AI is available on campus or online.

Value Proposition Description

Program Highlights:

  • Build in demand skills sought by employers in machine learning, deep learning, natural language processing, generative AI, robotics planning, computer vision, responsible AI, and much more.
  • Benefit from WPI’s deep history of teaching and furthering artificial intelligence innovations through impactful project work with industrial partners.
  • Take advantage of WPI’s interdisciplinary approach and hone your AI skills in courses you are interested in selected from academic units across the entire campus.
  • Gain real-world experience as you learn to develop, deploy, and innovate with AI techniques and systems in your team-based capstone project working with industrial mentors.
  • Customize your degree with thirteen specializations, including AI & Security, AI & Health, AI & Software Systems, and many more.
  • Learn from world-class faculty who are industry and scholarly leaders working on cutting-edge research projects in areas critical to our economy and to society.
WPI students huddling around a robot on the ground.

CS Faculty Research

Faster, Fairer, and More Accurate AI Models with Graph Data

WPI Prof. Fabricio Murai discusses their current research related to FRV AI.

Extracting Knowledge with Natural Language Processing

Prof. Kyumin Lee describes his research on information retrieval and AI.

Better Data for Better AI Results

Prof. Roee Shraga describes his work to improve the data used by AI systems and algorithms.

Xiangnan Kong AI Research

Understanding Brain Networks using Deep Learning

WPI Prof. Xiangnan Kong discusses their current research related to AI.

CS Graduate Student Research

Clustering for Confidentiality

Adam Beauchaine presents his cyber security research: Clustering for Confidentiality, An Exploration of Unsupervised Learning for the Security of Data Assets. 

Reinforcement Learning for Education

WPI student Morgan Lee presents their CS research: Expert Features for a Student Support Recommendation Contextual Bandit Algorithm.

Predicting Material Properties using MXenes

WPI student Eric Warnemunde Vertina presents his AI research: Predicting Material Properties via Artificial Intelligence. 

Fair Learning-to-Rank

WPI student Oleseun Olulana presents her AI research: Hidden or Inferred, Fair Learning-to-Rank with Unknown Demographics.


Annual job openings through 2032

Bureau of Labor Statistics


Median salary for computer and information research scientists

Bureau of Labor Statistics (2022)

Preview WPI Professor Elke Rundensteiner works with students on an NSF-funded project on fairness in artificial intelligence.
WPI In the News

Professor Elke Rundensteiner Speaks with BestColleges

Elke Rundensteiner, a WPI professor of computer science, told BestColleges that WPI is uniquely positioned to help students excel in the rapidly changing AI landscape.

"In some sense, we've been doing that for the last 50 years ... When I came to WPI 30 years ago, we had already the AI course itself."


Curriculum for Master of Science in Artificial Intelligence 

Students must complete at least 30 credit hours of study in the MS program, which is equivalent to a minimum of 10 three-credit graduate courses. As part of these 30 credits, you may select the MS thesis-option, which requires a 9-credit master’s thesis, or the project-based capstone option, which requires a three-credit capstone project course, referred to as the Graduate Qualifying Project (GQP) or Capstone Project. Each student should carefully weigh the pros and cons of these alternatives in consultation with their academic advisor prior to selecting an option, typically in the second year of study. The AI department will allow a student to change between the thesis or GQP options only once.

All entering students must submit a Plan of Study identifying the courses to be taken. The Plan of Study must be approved by the student’s advisor and the MS in AI Graduate Committee and must include the minimum requirements listed below. These M.S. degree requirements have been designed to provide a comprehensive yet flexible program to students who are pursuing an M.S. degree exclusively and students who are pursuing a combined BS/MS degree.

Preparatory Courses 

You are encouraged to take preparatory courses that have been designed to help you fill limited background knowledge or skills fundamental to AI including programming or mathematical foundations. MS in AI students may take at most six graduate credits towards the degree of these preparatory courses:

  • CS 5007 Introduction to Programming Concepts, Data Structures, and Algorithms
  • CS 5008 Introduction to Systems and Network Programming
  • CS 5084 Introduction to Algorithms: Design and Analysis
  • DS 517/MA517 Mathematical Foundations for Data Science
  • DS 501 Introduction to Data Science
  • DS 577 Machine Learning for Engineering & Science Applications
  • MIS 587 Business Applications in Machine Learning

Core Courses

MS in AI students must complete a five-course core by taking one course each in the five core MS-AI bins in AI, Ethics & AI, Machine Learning, Knowledge Representation & Reasoning, and Interaction & Action. Students may choose to take additional core courses, beyond the five required core courses, from below bins:

Artificial Intelligence (Choose One Course)
  • CS 534 Introduction to Artificial Intelligence 
Fairness, Ethics, & Policy in AI (Choose One Course)
  • DS555/CS555 Responsible Artificial Intelligence
  • SS560 Artificial Intelligence: Exploring Technology and Policy
  • MIS520 Artificial Intelligence and its Ethical Application in Business
  • WR 513 Ethical Impact and Communication in Robotics and Artificial Intelligence Research
Machine Learning (Choose One)
  • CS 548 Knowledge Discovery and Data Mining
  • DS 502/MA543 Statistical Methods for Data Science
  • CS 539 Machine Learning
  • DS541/CS 541 Deep Learning
  • CS586/DS504: Big Data Analytics
  • DS551/CS551 Reinforcement Learning
  • ECE571 Machine Learning for Engineering Applications
  • ECE557/CS557/DS557 Machine Learning for Cybersecurity
  • ECE556/CS556/DS556 On-Device Deep Learning
  • RBE577 Machine Learning for Robotics
Knowledge Representation & Reasoning (Choose One Course)
  • DS553/CS553 Machine Learning Development & Operations (ML OPS)
  • CS542 Database Management Systems
  • CS585/DS503 Big Data Management
  • CS 509 Design of Software Systems
  • MIS 502 Data Management for Analytics
  • OIE 559 Advanced Prescriptive Analytics
  • RBE 550 Robot Motion Planning
  • RBE 575 Safety and Guarantees in Autonomous Robotics
  • RBE 511 Swarm Intelligence
Interaction & Action (Choose One Course)
  • DS 552/CS 552 Generative Artificial Intelligence
  • DS 554/CS 554 Natural Language Processing
  • DS 547/CS 547 Information Retrieval
  • CS 549/RBE 549 Computer Vision
  • RBE 526/CS 526 Human-Robot Interaction
  • ECE 545/CS 545 Digital Image Processing

Capstone Project or MS Thesis

MS in AI students must complete either a three-credit capstone project experience or a nine-credit MS thesis from the list below.

For the capstone project, the MS-AI student can select one of the three capstone courses based on their primary interest and with approval of their MS-AI advisor and the instructor of the course.

Options for Capstone and MS Thesis Experiences
  • DS 598 Graduate Qualifying Project in Data Science (3 credits)
  • CS 594/DS 594 Graduate Qualifying Project in Artificial Intelligence (3 credits)
  • RBE 594 Capstone Project Experience in Robotics Engineering (3 credits)
  • CS 599/DS 599/RBE 599 Master's Thesis (9 credits)
CS 594/DS 594 Graduate Qualifying Project in Artificial Intelligence (3 credits)

This three-credit graduate qualifying project, typically done in teams, provides a capstone experience in applying Artificial Intelligence skills to a real-world problem. It will be carried out in cooperation with an industrial sponsor and is approved and overseen by a core or collaborative faculty member in the Artificial Intelligence Program. This offering integrates theory and practice of Artificial Intelligence and includes the utilization of tools and techniques acquired in the Artificial Intelligence Program to a real-world problem. In addition to a written report, this project must be presented in a formal presentation to faculty of the AI program and sponsors. Professional development skills, such as communication, teamwork, leadership, and collaboration, will be practiced.  This course is a degree requirement for the Master of Science in Artificial Intelligence (MS-AI) and may not be taken before completion of 21 credits in the program. Students outside the MS-AI program must get the instructor’s approval before.

Prerequisite: Completion of at least 24 credits of the AI degree, or consent of the instructor. With permission of the instructor, the GQP can be taken a 2nd time for a total of 6 credits.

CS 599/DS 599/RBE 599 Master's Thesis

The MS thesis in the Artificial Intelligence Program consists of a research or development project worth a minimum of 9 graduate credit hours. Students interested in research, and in particular those who consider pursuing a Ph.D. degree in a related area, are encouraged to select the M.S. thesis option. The student can sign up for MS thesis credits such as CS599, DS599, or RBE599, as long as a faculty affiliated with the MS-AI program serves as thesis advisor and the thesis topic relates to AI. Students must submit a thesis proposal for approval by the program by the end of the semester in which a student has registered for a third thesis credit and by the advisor. Proposals will be considered only at regularly scheduled program meetings. Students funded by a teaching assistantship, research assistantship or fellowship are expected to pursue the thesis option. The student then must satisfactorily complete a written thesis and present the results to the AI faculty in a public presentation.

Meet Our World-Class Faculty

Elke Rundensteiner
Elke Rundensteiner
Professor, Computer Science, Program Head, Data Science, Computer Science

As founding Head of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, and new educational initiatives to our innovative industry-sponsored and mentored Graduate Qualifying projects at the graduate level.

read more
Kyumin Lee
Kyumin Lee
Associate Professor, Computer Science

Dr. Lee’s research interests are in information retrieval, natural language processing, social computing, machine learning, and cybersecurity over large-scale networked information systems like the Web and social media. He focuses on threats to these systems and design methods to mitigate negative behaviors (e.g., misinformation, hate speech), and looks for positive opportunities to mine and analyze these systems for developing next generation algorithms and architectures (e.g., recommender system, natural language understanding).

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Xiangnan Kong
Xiangnan Kong
Associate Professor, Computer Science

Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

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Carlo Pinciroli
Carlo Pinciroli
Associate Professor/Graduate Coordinator, Robotics Engineering

The focus of my research is designing innovative tools for swarm robotics. I am developing Buzz, a programming language specifically designed for real-world robot swarms. During my Ph.D., I have designed ARGoS, which is currently the fastest general-purpose robot simulator in the literature. Recent work focuses on human-swarm interaction and multi-robot learning. I am also working on swarm robotics solutions for disaster response scenarios, such as search-and-rescue and firefighting.

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Carolina Ruiz
Carolina Ruiz
Associate Dean of Arts & Sciences, Computer Science

Carolina Ruiz is the Associate Dean of Arts and Sciences and the Harold L. Jurist ’61 and Heather E. Jurist Dean's Professor of Computer Science. She joined the WPI faculty in 1997. Prof. Ruiz’s research is in Artificial Intelligence, Machine Learning, and Data Mining, and their applications to Medicine and Health. She has worked on several clinical domains including sleep, stroke, obesity and pancreatic cancer. Prof.

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Xiaozhong Liu
Xiaozhong Liu
Associate Professor, Computer Science

Dr. Xiaozhong Liu is an Associate Professor at Computer Science and Data Science, WPI. Before that, he was Associate Professor at School of Informatics, Computing and Engineering Indiana University Bloomington. His research interests include natural language processing (NLP), text/graph mining, information retrieval/recommendation, metadata, and computational social science. His dissertation at Syracuse University (advisor Dr. Elizabeth D. Liddy) explored an innovative ranking method that weighted the retrieved results by leveraging dynamic community interests.

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Jacob Whitehill
Jacob Whitehill
Associate Professor, Computer Science

My research interests are in applied machine learning, computer vision, data science and their applications to education, affective computing, and human behavior recognition. My work is highly interdisciplinary and frequently intersects cognitive science, psychology, and education. Before joining WPI, I was a research scientist at the Office of the Vice Provost for Advances in Learning at Harvard University. In 2012, I co-founded Emotient, a San Diego-based startup company for automatic emotion and facial expression recognition.

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Make Our Program Yours

Elective Courses

As an MS in AI student, you may choose to take additional elective or other AI-related courses from the two options below to reach the 30-credit requirement for the degree:

Other AI-Related Courses: With permission from your academic advisor, you may take any number of AI-related special topics courses, including CS525/DS595/RBE595, Independent Study (ISG), and Directed Research (CS598/DS597/RBE596). In order for these to be counted toward your degree, they must be offered by faculty with a core or a collaborative appointment in the MS in AI program.

Specializations: You may choose to take up six graduate credits in courses that are not part of the MS in AI core bins in any discipline and count them toward your degree. All requirements by the respective unit offering a course must be followed. Students may gain a specialization “AI&X” by taking six elective credits in a discipline thematically related to AI and approved by your advisor. These areas of specialization include, but are not limited to, the ones listed below:

  • AI & Business: ML for Business, Project Management, Supply-Chain Optimization
  • AI & Engineered Systems: Digital Signal Processing, Medical Signal Analysis, Foundations of Robotics, Sensor Engineering
  • AI & Foundations: Mathematical Optimization, Multi-variate Data Analysis, Advanced Statistics
  • AI & Game Development: Serious and Applied Games, Design of Interactive Experiences
  • AI & Global Development: Sustainability, Climate Change, Social Justice, Global Health
  • AI & Health: Bioinformatics, Health Sciences, Neuroscience, Biology
  • AI & Human Experiences: Human-Computer Interaction, Tangible & Embodied Interaction, Human-Robot Interaction
  • AI & Learning Sciences: Foundations of Learning Sciences. Learning Environments in Education.
  • AI & Material Sciences: Smart Materials, Nanomaterials, Manufacturing Processes
  • AI & Neuroscience: Computational Neuroscience, Brain-Computer Interaction, Advanced Psychophysiology
  • AI & Robotics: Robot Dynamics, Biomedical Robotics, Soft Robotics
  • AI & Security: Software Security Design and Analysis, Machine Learning in Cybersecurity, Cryptography
  • AI & Software Systems: Adv. Software Eng., Algorithms, Mobile & Ubiquitous Computing, Distributed Systems

Note 1: Less than 50% of the credits in the MS in Artificial Intelligence can be taken from the Business School, that is, a maximum of 14 credits of a 30-credit program.  For 3-credit courses, a maximum of 4 courses may be taken from the Business School (any course with a prefix of ACC, BUS, ETR, FIN, MIS, MKT, OBC, or OIE).

Note 2: A single course cannot be used to meet two or more requirements of the MS-AI degree. For instance, if a course is used to meet one particular bin requirement, it cannot also be used to meet a second bin requirement, nor can it be counted towards fulfilling a thematically related specialization.

Important Dates

Next Start: August 22, 2024 

Application Deadline: Apply anytime! 

How WPI Professors Teach AI Classes

Teaching in AI and ML

Prof. Kyumin Lee talks about classes for recommendation systems, machine learning, and artificial intelligence.

Fusing Project-Based Learning into AI Education

Prof. Rodica Neamtu talks about how she incorporates projects into her classes and projects.

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