Spring 2026 Course Search

For students doing work-study or internships, we will focus on three core areas of professionalization. First, each week will journal our work weeks, discussing and sharing our work experiences in a round-table. Second, we will build our professionalization skills, especially networking (in person and on LinkedIn), resume writing, and doing practice interviews. Finally, we will work on writing 5-year plans, to help us figure out where we’d like to be a few years after graduation. More specifically

Following up on the fall course on web3, this course helps students learn to track transactions and actions across blockchains, which are large distributed censorship resistant databases. The course starts by exploring the fundamental nature of the blockchain: how data is stored, accessed, and traversed. It then introduces common patterns and software used for blockchain analytics.

How can we create machines that think, learn, and solve problems? This course explores the fascinating field of artificial intelligence (AI), introducing the fundamental concepts, techniques, and ethical considerations that drive this rapidly evolving discipline.

Building upon your programming knowledge, you will explore key AI paradigms including search algorithms, evolutionary algorithms, swarm intelligence, and machine learning.  You will implement AI solutions to real-world problems, and gain an understanding of how to think about contemporary AI development.

This course covers the breadth of university calculus: differentiation, integration, infinite series, and ordinary differential equations. It focuses on concepts and interconnections. In order to cover this much material, computational techniques are de-emphasized. The approach is historically based and classical, following original texts where possible.

Multivariable calculus is one of the core parts of an undergraduate mathematics curriculum. Introductory calculus mostly concentrates on situations where there is one input and one output variable; multivariable extends differentiation, integration, and differential equations to cases where there are multiple input and output variables. In this way, multivariable calculus combines calculus and linear algebra; the subject can also be called vector and matrix calculus.

Everything is geometry! This class is about two things: first, about how mathematicians have extended the concept of "geometry" beyond triangles and circles, into higher-dimensional spaces, curved spaces, spaces of functions, discrete spaces, and more. Second, about how this extension of "geometry" can allow us to apply our powerful geometric intuition to a wide range of problems that might not initially seem geometric, both within mathematics, and in physics, computer science, and elsewhere.

In this course, we will focus on developing the statistical skills needed to answer questions by collecting data, designing experimental studies, and analyzing large publicly available datasets. The skills learned will also help students to be critical consumers of statistical results. We will use a variety of datasets to develop skills in data management, analysis, and effective presentation of results.

In this class, students will start working on their artists' book documenting their ongoing MFA thesis research, process and practice, and we will discuss how this relates to potential ideas for Research As Action presentations. To make this possible, we will use software such as Adobe CC Indesign and Photoshop. Slide presentations, software demos, group and individual critiques will help students develop and shape their ongoing thesis research and actions. 

Discrete mathematics studies problems that can be broken up into distinct pieces. Some examples of these sorts of systems are letters or numbers in a password, pixels on a computer screen, the connections between friends on Facebook, and driving directions (along established roads) between two cities. In this course we will develop the tools needed to solve relevant, real-world problems. Topics will include: combinatorics (clever ways of counting things), number theory and graph theory. Possible applications include probability, social networks, optimization, and cryptography.

Throughout history, people have played games — games of chance and games of skill. Many of us grew up playing all kinds of different games, and most of those are infused with the core tenets of statistical reasoning and understanding: probability, risk assessment, expected value, and game theory. This course will look at statistics and probability through this lens. We will consider dice, cards, and several ‘classic’ board games. We will consider situations with both complete and hidden information and how to analyze those.

The term will be spent focusing on a teaching statement, evidence of teaching history, with a focus on intersectional Life writing. The continuation of the collection of documentation of professional activity, a full CV, an artist statement, and any other applicable statements will be added to the materials to create the fullness of the Portfolio book. An artist’s talk will conclude the portfolio process during the summer term.

The term will be spent focusing on a teaching statement, evidence of teaching history, with a focus on intersectional Life writing. The continuation of the collection of documentation of professional activity, a full CV, an artist statement, and any other applicable statements will be added to the materials to create the fullness of the Portfolio book. An artist’s talk will conclude the portfolio process during the summer term.

This course allows students more time to complete thesis project work.

This course allows students to self-design course work by combining topics and approaches from the Practice LABs and the Study LABs to meet required hours. The Individualized LABS take the form of a series of self directed intensive workshops and study immersions.

Variable Credit, 1-2 Credits

Through mentor approved independently paced work, students develop and schedule their own weekly, planned creative practices using student-initiated resources and/or classes. Mentors guide students through the designed plan that can include a combination of practices, techniques, technologies and methodologies. The study format should provide opportunity for varied approaches and choices.

This topic driven seminar focuses on current developments within the field of dance and performance. Students will learn to think of dance and performance through their own embodied experiences and by placing dance, movement, and performance in wider disciplinary, cultural and global contexts.

Where and how does study happen? What is the value of study and how do we recognize that value? What does it mean to think of our study of dance and performance as an encounter and how might that thinking offer up a chance for one to pay attention differently? Is it different from research?  Or, as Kevin Quashie suggests, does it perhaps re-situate the activities of research, scholarship, teaching and practice in an important way? These Labs take the form of intensive workshops and/or lectures.

Variable Credit, 1-2 Credits

What does studying together offer us critically that studying alone might not? Ariella Azoulay refers to studying with companions as a method of unlearning. What are the shifts experienced when you are studying with and alongside others? What conditions might group study provide that allow different questions and understandings to emerge? If, as Irit Rogoff states, “All research is collaborative,” how might these study groups expand our thinking through collaborative practices? What methodologies emerge?

The record of the processes and research practices take shape in the writing and designing of the artist’s book. The Research as Actions are discussed and planned. These actions are shared informally and at the conclusion of the term. 

Students propose, plan, discuss and develop a (research) thesis project. They choose a thinking partner to work alongside and begin the processes.

In this course, students will gain experience with using simple programmable robots and how they can be utilized in STEM education. The focus of this class will be on learning and designing lessons for K-12 students utilizing these robots. This class is accessible for students at all levels of computer programming experience (including none). 

Introduction to Computer Science 2 continues the design-recipe approach started in Introduction to Computer Science 1. We extend our toolkit from structural recursion into generative recursion, abstraction, and algorithmic problem-solving. Students move beyond simple data definitions to work with more sophisticated structures (trees, graphs, sets, maps) while beginning to reason about program efficiency and resource use.