Teaching

HIST 1993 | Introduction to Digital History

This course introduces students to the emerging field of digital history. We explore the current and potential impact of digital methodologies on the theory and practice of history. The course trains students in the acquisition, analysis, and visualization of data in a careful, theoretically informed approach that tailors digital tools and methodologies to the unique concerns of the discipline. It covers the central aspects of data processing that ultimately underlie digital research: acquisition and management, analysis, visualization, and presentation. During the semester, students acquire practical technical skills, but more importantly, the course is designed to make them conversant across the spectrum of digital techniques so that they can critically evaluate the potential and limitations of new technologies, integrate them into a sound research program, and fruitfully interact with experts to produce results. Syllabus (PDF)

HIST 2029 | Digital Methods and Primary Sources

This seminar teaches participants to use digital tools to design and implement data collection strategies that anticipate their future analytical needs and maximize the research potential of the sources being collected. We cover the process of conducting initial evaluations of collections and determining their suitability to address specific historical questions, implementing workflows for digitization and data mining that follow established standards and best practices, and designing storage strategies around the data model that best suits the material and its intended use. Participants also learn advanced approaches to managing large collections and extracting information from different source types—documents, correspondence, tables, photographs, cartography—alongside tools and strategies for project management, quality control, and documentation, as well as the common pitfalls in each. The seminar involves significant hands-on work, but its aim is to see past the instrumental aspects of tools and platforms and focus on how they can be adapted, in a theoretically informed manner, to the ways scholarly disciplines approach their subject. It therefore attends closely to the production of historical knowledge: the nature and types of historical evidence, the formation processes that shape archives and the structural bias they introduce, and the fundamentals of source criticism as they apply to born-digital and digitized primary sources. Some meetings are held in libraries and archives. Syllabus (PDF)

HIST 1039 | First Empires: Power and Propaganda in the Ancient World

This course traces the continuum of socio–political and cultural developments in the Near East that led, over the course of three millennia, from stateless societies to the emergence of the Assyrian Empire, the first such entity in history. The class focuses on the long-term history of power centralization and the role of ideology and propaganda in overcoming resistance to this concentration of authority. The course material covers a broad evidentiary and chronological range. We will employ textual, visual, and archaeological sources to explore the evolution of the political and cultural landscape in the Near East and the Mediterranean, from the emergence of the first city–states in the late fourth millennium BCE to the early development of the Roman imperial ideology around the start of the Common Era. Syllabus (PDF)

HIST 2039 | History from Things

This seminar focuses on the use of material culture as a primary source for constructing historical narratives that access aspects of the past not covered by written words. We will look closely at how materiality theory relates to the methodologies and conceptual categories used by historians to understand the past. Students will also be trained in the specific skills needed for interpreting material sources. Because material history relies on methodologies and theoretical approaches that transcend the fields that define our discipline, the course’s coverage will be broad across both time and space, allowing for participants to explore corpora of materials according to their own interests and expertise. Syllabus (PDF)

HIST 92r | History Lab

History Lab is a course that offers students a chance to spend a semester collaborating on History faculty research projects. Students receive ordinary course credit and must produce a final product comparable in scope to a major term paper. These products can take myriad forms (eg online exhibition, visualization) depending on the instructor. History Lab is designed as a safe communal space where students can experiment and learn to embrace failure. It offers them the chance to confront the wild diversity of primary materials in their raw state, and learn how to forge the very tools they will use to shape them, free of the burden of getting the answers "right".

HIST 3010 | Directed Study in Digital/Computational History

This directed study supports students preparing for the comprehensive examination in digital and/or computational history. Organized as a tutorial with weekly meetings and a tailored reading plan, its purpose is to cultivate substantive field literacy: the ability to orient quickly within unfamiliar sub-areas, identify foundational bibliographies and source bases, explain how scholars have approached key problems, and assess the strengths and limits of influential approaches. Because the field is broad and rapidly evolving, the course is customizable—each student determines, in consultation with the instructor, the relative emphasis placed on theory, methodology, and instrumental skills. The field spans digital history and its institutional histories; debates over method, evidence, validity, and interpretation; distant reading and the relationship between scale and historical argument; data and data structures; digital archives and digitization, including provenance, selection effects, and archival bias; quantitative methods; spatial history; text analysis and natural language processing; image analysis; machine learning and large language models in historical research; and reproducibility, transparency, and research ethics. Syllabus (PDF)

AM 91r | Supervised Reading and Research

A directed study oriented toward students writing a senior thesis in Applied Mathematics. In lieu of a conventional syllabus, students develop a thesis proposal in consultation with the instructor. Recent supervised theses include a computational study of personal satire in Aristophanes (Hoopes Prize, 2024), prototyping natural language processing workflows for entity extraction from highly specialized corpora, and quantifying provenance to model historic prices in the eighteenth-century French art market.

Workshops and the Certificate Program in Digital Scholarship

The Certificate Program in Digital Scholarship is designed as a structured pathway for graduate students to build competency in digital methods and demonstrate it with a public work sample. The curriculum is organized around a research data lifecycle—acquisition, analysis, presentation—with three foundational workshops (common core), distributional requirements across the three areas, and a capstone assessed by a committee of both digital scholarship and subject-area experts.

Common core

Fundamentals of Digital Scholarship: Introduces the core stages of a digital scholarship workflow: acquiring data, preparing and manipulating it, analyzing it, and presenting results. Designed for faculty, students, and staff making early decisions about methods and research design. Covers the data formats common in humanities research, tidy-data principles, visualization for exploratory analysis, an overview of spatial, network, image, and text methods, and publishing results on the web. No programming experience required.

Working with Data: Practical fluency in independent data work. Participants learn to assess a dataset in the wild—structure, gaps, inconsistencies, provenance, documentation needs—then clean, reshape, and enrich tabular data using spreadsheets, OpenRefine, and regular expressions, before moving to programmatic wrangling and to working with APIs and JSON. The emphasis is on transferable techniques rather than mastery of any single tool. Materials

Digital Communication for Academics: How research is presented, and how it is made discoverable and reusable over time. Covers slide design; choosing digital tools that match the structure of the underlying evidence; and building a sustainable scholarly presence, including research outputs beyond publications (data, code, workflows, digital projects), FAIR principles, the distinction between sharing and full openness, and repositories and persistent identifiers.

Advanced workshops

Introduction to Python: A biweekly group open to all, aimed especially at humanities and social science graduate students learning to code. No prior experience with Python, mathematics, or computer science is presumed. Each unit centres on an interactive Jupyter notebook containing explanations, live code, and practice problems, along with a larger project completable using only the skills covered so far. Participants are encouraged to bring their own research questions. Materials

Web Scraping: Automating the extraction of data from websites into well-formed local datasets. Begins with how web pages are structured—HTML, URLs, the DOM—and moves from browser-based scraping workflows through programmatic approaches in Python with BeautifulSoup and, where necessary, browser automation with Selenium. Includes the rules of the road for responsible collection: rate limiting, avoiding unnecessary load, and documentation. Materials

Understanding AI—A Practical Workshop for Humanities and Social Science Researchers: A grounded introduction to contemporary AI systems: what they are, how they differ from traditional computing, what they can and cannot do, and how to assess their outputs critically. The first day covers model categories, how training data shapes behaviour, evaluation concepts, and common failure modes including hallucination, bias, and prompt injection. The second moves to Python workflows with pre-trained models, with participants producing their own ground truth and evaluating results quantitatively. Materials