Now Enrolling for Fall 2027!

The Master of Science in Computational Humanities program is enrolling its inaugural fall 2027 cohort!

Apply by January 6, 2027.

Application Instructions

Master of Science in Computational Humanities

Bridging the gap between traditional humanities disciplines and 21st century technologies.

Digital ecosystems are revolutionizing the media, business, politics and academia, and the humanities are no exception. These phenomena demand new critical frameworks and technological literacies. The Master of Science in Computational Humanities (MSCH) will equip you to navigate and influence these powerful technologies and apply them to humanities scholarship.

The MSCH combines Carnegie Mellon University’s unique strengths in AI, machine learning and human-computer interaction with the deep research and critical thinking skills fostered by the humanities. By integrating advanced digital methodologies and tools with humanities research, the MSCH will prepare you for roles in academia, industry, libraries, museums, non-profits and beyond.

Why Study the Computational Humanities?

  • Interdisciplinary Expertise: Develop a versatile skill set combining technological skills, critical thinking, digital literacy and humanities expertise.
  • Enhanced Employability: Equip yourself with highly sought-after digital competencies and apply your skills in academia, industry and cultural institutions.
  • Innovation in Research: Foster research methodologies that leverage digital tools to make new discoveries in history, language and culture.

The MSCH is completed in 12 months, with a required summer practicum. 

Current CMU undergraduates may also be eligible for a 4+1 pathway.

The MSCH offers an interdisciplinary program drawing on Carnegie Mellon’s diverse strengths. 

Fall Term 

  • 76-894: Introduction to Computational Humanities: A full-semester, team taught, cohort-building course that introduces students to core issues and methodologies in computational humanities. 
  • 36-600: Statistical Learning for Non-Statistics Graduate Students: Covers foundational statistical learning and modeling methods. Designed for graduate students without prior statistics training. 
  • 76-788: Coding for Humanists: Introduces students with little or no programming background to basic coding concepts through hands-on exercises in Python, focusing on practical applications for text analysis. By the end of the course, students will be able to create simple computational tools, evaluate open-source software, and build a foundation for further coding study in the humanities. 

Spring Term 

  • 76-714: Data Stories: Examines how data is collected, maintained and narrated across cultural and historical contexts, asking students to analyze the relationship between data and storytelling. Through case studies and a long-form research project, students learn to critically engage with contemporary writing about data and to craft their own narratives in ways comparable to Michael Pollan’s approach to food.
  • 82-813: AI for Humanists: Explores the intersections of artificial intelligence with language, arts and culture through three modules on language learning, generative artistic tools, and cultural immersion. Combining lectures, hands-on projects, and critical reflection, the course equips students to understand and creatively apply AI in humanities contexts while assessing its possibilities and limitations. 

Summer Term 

  • Project Management: Introduces the fundamentals of managing digital projects, including planning, execution and team leadership. 
  • Summer Practicum: A hands-on, client-facing project applying computational humanities in real-world contexts. Students will complete a project that combines humanities research with computational methodology. 

Electives 

  • 76-780: Methods in Humanities Analytics: Introduces a toolkit for computer-aided text analysis, combining qualitative and quantitative approaches across fields such as corpus linguistics, distant reading, and social media analysis. Students design their own projects, applying statistical methods to real-world language data. 
  • 76-234: Media: Past, Present, and Future: Explores media revolutions from the rise of the novel to today’s digital and social media, asking how narrative and storytelling shape each technological transformation. Students compare historical anxieties about media with contemporary debates. 36-668: Text Analysis: Covers methods for analyzing linguistic data, with attention to sampling challenges, feature distributions, and statistical modeling. Students work in R to develop tools and carry out independent text-based research projects. 
  • 76-890: Digital Rhetorics: Examines how digital technologies such as AI, VR, blockchain, and IoT transform rhetorical practice, public discourse, and socio-political systems. Includes opportunities to create digital artifacts and critique digital communication. 80-831: Responsible AI: Investigates ethical and policy implications of AI systems in socially consequential domains such as law, education, medicine and criminal justice. Students assess pathways to bias, privacy concerns, and possible remedies. 
  • 11-830: Ethics, Social Biases, and Positive Impact in Language Technologies: Explores ethical and social challenges in NLP and language technologies, including bias detection, propaganda, privacy, accessibility, and applications for social good. Involves advanced techniques and multidisciplinary perspectives. 
  • 05-660: Interaction Design Fundamentals: Introduces principles and methods of human centered interaction design, with team-based projects that develop prototypes and design artifacts. Covers visual design, critique, and creative experimentation. No coding required. 
  • 05-618: Human AI Interaction: IIntroduces the design and evaluation of AI systems that meaningfully support human needs and experiences. Students engage core topics such as bias and transparency, trust and explainability, human-AI collaboration, and mixed-initiative systems, while building hands-on projects across domains including speech, vision, recommendation, and data science. 
  • 84-650: A Strategist’s Introduction to Artificial Intelligence: How do AI technologies influence politics, governance, and conflict? This course provides an overview of AI technologies, beginning with unpacking the term ’artificial intelligence’ itself. 
  • 84-656: Applied Political Data Analytics: This course introduces students to data analytics in political science, with a focus on developing practical data skills for applied research and real-world problem solving. Students will acquire foundational Python programming skills for analyzing and visualizing political data, including polling data, survey responses, and political text. 
  • 84-668: Technology Ethics: Churchill once complained that war, once ”cruel and magnificent,” had been ”completely spoiled” due to the involvement of scientists in creating new military weaponry. Scientific-military collaborations are hardly new, however. This course provides an introduction to the politics of technology ethics.
  • 84-671: International Governance of Artificial Intelligence: How do global institutions seek to govern AI? This course examines AI policy from an international perspective.

Applications to the MSCH are accepted online. The application website contains detailed instructions for entering your information and uploading documents. Candidates should be prepared to submit:

  • Online application form
  • Statement of Interest
  • Resume or CV
  • Three letters of recommendation
  • Unofficial transcripts (official transcripts requested upon admission)
  • Writing sample(s)
  • Official IELTS or TOEFL scores (as applicable)
  • Application fee

A link to the application and more detailed instructions are available on the application website.