Global Arc

This course considers the impact of medical history, its advances and effects, in various historical periods and in dialogue with literary representations of human experience. Starting with definitions of medical humanities (in technology, philosophy, social sciences and religious studies), we will explore medical history in diverse literary texts chosen from the sixteenth- to the twenty-first centuries. The potential interface between ethics (external, professional or societal codes) and morals (internal, conscience driven values) will be considered.

After the designation of Mexican drug cartels as Foreign Terrorist Organizations by the United States, it has become urgent to ask: how have Mexican drug cartels become an essential part of Mexico's governmentality, while also being perceived as a threat to other countries? In this course, we will address this question by arguing, both philosophically and historically, that Mexican narcos are neither mere criminals nor terrorist, but rather micro-sovereigns. Some of the thinkers and artists who will help us understand the concept of micro-sovereignty include Thomas Hobbes, Michel Foucault, Oswaldo Zavala, Luis Estrada and Mónica Ojeda.

The lie is as old as language, but reportage suggests something singularly dishonest about the present moment. Accusations of "fake news!" abound; journalism is threatened by censorship; AI plagiarism beleaguers universities; governments routinely use "truth commissions" to reckon with atrocity, even as those affected critique them as insincere. This seminar asks how we differentiate heretics and bluffers, kind-hearted tellers of white lies, conspiracy theorists and con artists. Is a falsehood uttered with conviction a lie? Is deceit ever justified? How do evidentiary regimes shape what knowledge is received as truth and what is disqualified?

Political campaigns in the 2020s are becoming more technology dependent, more confusing and difficult from a communications perspective and even more fraught with pitfalls and personal challenges. When deciding whether to run for any office all of these issues must be confronted in addition to the core considerations of issue positions, fundraising and the most important question of all: If I do win, what do I want to accomplish and what kind of leader do I want to be?

This course provides a broad overview of the field of population studies. Topics covered include: a survey of past and current trends in the growth of the population, analysis of the components of population change (fertility, mortality, migration) and their determinants, the social and economic consequences of population change (environmental, ageing and the welfare state), racial/ethnic and spatial inequality and population policy. The readings cover issues in industrialized as well as low-income countries, with a focus on the U.S. for several topics.

Collective behaviors are all around us, from bird flocking, to mosh pit dynamics, to how the cells in our bodies work together. In this course, we explore not only how to understand and quantify these behaviors, but also how we can start to engineer them to reduce traffic, heal faster, develop new materials, and introduce new robotics approaches. The course spans three modules: hands-on training in analyzing real-world swarming systems; fundamental concepts underlying collective behaviors; and key case studies in manipulating these systems.

Data-driven decision-making, research discovery, and technology development are everywhere. It is now more important than ever for individuals to understand how data are used for these purposes. This course will introduce the student to how statistical reasoning and methods are used to learn from and leverage modern data. The emphasis will be on concepts and strategies for learning from data, rather than on sophisticated mathematics. Students will be exposed to the basics of statistics, machine learning, and data science through real world problems and applications. Students will also analyze data sets using the computer.

Introduction to Data Science provides a practical introduction to the burgeoning field of data science. The course introduces students to the essential tools for conducting data-driven research, including the fundamentals of programming techniques and the essentials of statistics. Students will work with real-world datasets from various domains; write computer code to manipulate, explore, and analyze data; use basic techniques from statistics and machine learning to analyze data; learn to draw conclusions using sound statistical reasoning; and produce scientific reports. No prior knowledge of programming or statistics is required.

This course provides the training for students to be independent in modern data analysis. The course emphasizes the rigorous treatment of data and the programming skills and conceptual understanding required for dealing with modern datasets. The course examines data analysis through the lens of statistics and machine learning methods. Students verify their understanding by working with real datasets. The course also covers supporting topics such as experiment design, ethical data use, best practices for statistical and machine learning methods, reproducible research, writing a quantitative research paper, and presenting research results.

Project-based course in which students work individually or in small teams to tackle data science and ML problems based on real datasets. We will emphasize critical thinking about experiments and large dataset analysis along with the ability to clearly communicate one's research. This course is intended to support students in developing the analytical skills necessary for quantitative independent work; students should consult with their home department about how this course could appropriately complement, but not replace, their independent work requirements.