Fall 2026 Supplement and Course Offerings

Section 02 of this course is now offered for non-first year students!

Instructor: Jon Adler

Credits: 4 AHS

Registration Note: Students who previously took AHSE 1155 as a first-year cannot repeat this class.

Course Description: Perhaps the most fundamental question any developing individual asks himself/herself is: Who am I? The ways we answer this question have evolved over the course of history as the dominant ways of knowing (epistemologies) have shifted. Indeed, the question of how we come to know ourselves has captivated Western scholars since the days of Descartes, but a look at the last fifty to sixty years has also seen enormous changes. Many people invoke psychological and philosophical perspectives in describing their identity, focusing on their personality, their developmental history, and their place in society. But the explosion of neurobiological research has introduced a new and viable outlook: explaining identity at the chemical and electrical level of the brain. There is good reason to think that these different perspectives on identity are mutually exclusive and this tension will underlie everything we discuss in this interdisciplinary course. Indeed, when it comes to a topic as fundamental to human existence as identity, it is absolutely essential to wonder not only "who am I?" but to also ask "how do I know?" In this course, we will approach the question of identity from multiple perspectives, including psychology, postmodern philosophy, and neuroscience. In the process, we will critically examine not only the conception of identity that each perspective supports, but also the assumptions and limitations of each epistemology. This course focuses more on the science of psychology and neuroscience, while AHSE 1150: What Is "I"? is more focused on philosophy and artificial intelligence.

Instructor: Lis Sylvan, Caitrin Lynch

Credits: 4

Curriculum Role: AHS Elective or Design Depth

Registration Notes: 

• Students must have taken or currently be taking Collaborative Design. 
• Students who took Generative AI in Fall 2025 (AHSE2199C or ENGR2299) are not eligible to take this class. 

Course Description: Cut through the hype and excitement surrounding generative AI by understanding for yourself what these tools can and cannot do; what the societal implications of these tools are; and how you can develop your own decision-making frameworks for when, whether, and why to make use of the tools. Through this course, students will learn to understand, design, and build with generative AI and, as they do, will grapple with the inevitable ethical concerns that these tools present. Depending on student interest and the changing nature of these tools, our activities may include partnering with generative AI to improve our workflows, create speculative and practical designs or 3D renderings, vibe code apps, organize and study datasets and research articles, and generate illustrations of concepts. As we create, students will examine the quality of the outputs and concerns that arise about bias, hallucinations, efficiency, and more. Readings and discussions will address the ethics and societal impacts of AI, responsible use of generative AI, and design implications for engineers, designers, and other makers. A typical class will involve a brief instructor-led overview of the topic of the day, active learning with AI tools, and full-group discussions about the primary topic, emergent topics, and active learning experiences. No previous experience with either the theory or use of AI is required for this class; students will learn to use different tools in and outside of class. Students will use their experiences in class, through coursework, and beyond to develop conceptions about how they, personally, want to use (and not use) AI as responsibly as possible. 

Instructor: Caitrin Lynch

Credits: 4

Curriculum Role: AHS Elective

Course Description: This course is for students who are excited to learn about learning, contribute to the design of experiences for future Olin students, and integrate their own learning into tangible outputs to carry into the future. As we revitalize Olin’s educational approach, a recurring topic has been to create opportunities for students to collate and reflect on their learning, making connections that integrate Olin successes (and failures!) into new contexts at Olin and beyond. One potential way to achieve this goal is student Portfolios. What are Portfolios, why do they matter, and what might they look like for Olin students? A recent higher ed publication describes Portfolios (in their digital form) as follows: “ePortfolios digitally curate student work to provide an authentic representation of learning outcomes. From compiling course-related essays to documenting volunteer experiences and employment history, ePortfolios capture learning wherever it occurs.… Building an ePortfolio leads students to naturally make valuable connections between various courses, assignments, and cocurricular activities through reflection of their past work. Whether showcasing students’ knowledge and skills for graduate school or a job application, ePortfolios paint an accurate portrait” (AAC&U https://www.aacu.org/trending-topics/eportfolios). In this course, students will study the topic of Portfolios and student learning, build from that understanding to create their own Portfolios, and share insights that may impact the potential use of Portfolios in Olin’s future. 

Instructor: Daniela Faas

Credits: 2

Curriculum Role: Design Elective

Registration Notes: 

• Prerequisite: Collaborative Design
• Recommended Requisite: Experience with computer-aided design and digital fabrication

Course Description: This course focuses on the pedagogical and logistical framework required to design, launch, and manage effective makerspace workshops. We will explore how to translate technical skills (like 3D printing or woodworking) into structured learning experiences that cater to diverse skill levels. Developing a workshop is a unique challenge: you aren't just teaching a skill; you are orchestrating an environment where people feel safe enough to fail and creative enough to innovate. Students will learn to curate educational experiences that empower learners while simultaneously designing physical environments that promote safety, flow, and collaborative "messing about." We move from the why of making to the where and how.

Instructor: Goenka, Chhavi

Credits: 4 (4-0-8)

Curriculum Role: ECE Requirement (Digital Signals Processing or Analog & Digital Communications), or ECE Elective

Registration Notes:

• Recommended Requisite - Coding (either MATLAB or Python, but MATLAB preferred). 
• Pre-requisite ESA (Signals).  If you have not taken Signals but believe you have other relevant experience please contact Chhavi.
• This course can be used as a designated alternative to the ENGR3415 (Digital Signals Processing)/ENGR3420 (Analog & Digital Communications) requirement for the ECE major.  If taken in addition to DSP or ADC, it may be used as the ECE elective course)
• Students who took Image Processing previously (as ENGR3499) are not eligible to take this class.

Course Description: : Imaging, Imaging algorithms and imaging systems are being used every day to analyze and interact with the world around us, from facial recognition to medical data collection, from search & rescue to surveillance, from autonomous vehicles to assistive devices. In this course, we will learn about the basic concepts of image processing, image reconstruction from incomplete data and image analysis to obtain meaningful information from imaging data. We will also study how and where there is a possibility of biases being introduced into the entire imaging process - from acquisition to interpretation. The specific topics (as they apply to imaging) that we will cover include but are not limited to sampling, linear transformation, geometric transformation, convolution, change detection, edge detection, quantization, filtering, compression, color spaces, image segmentation, image reconstruction, classification, feature extraction.

Note about conceptual overlap with DSP: Since images are signals that have two spatial domains, image processing is an application of digital signal processing. If you want to learn concepts from DSP, you can take image processing and learn not all but quite a few of those. Some of these concepts are: Linear time-invariant systems, Fourier transforms, sampling & aliasing, convolution & deconvolution, filtering, data compression, feature detection, histogram processing and analysis, representation of signals in frequency domain or other transform domains.

Instructors: Jean Huang, Rob Martello

Credits: 8 (4 AHS, 4 SCI)

Curriculum Role: AHS Elective and Bio Foundation

Course Description: Penicillium. Vibrio cholerae. Escherichia coli. Yeast. The Archaea. Microbes surround us, and impact our lives, our health, our societies, and our environment. Research with microbes, the smallest of all living creatures, has enabled discovery and understanding of the fundamental workings of life, opens up rich historical narratives of diseases and cures, and may provide sustainable solutions to problems we face from bioremediation to bioenergy. We will use six influential microbes as a window into a rich study of the interactions between science and societal context. This course connects biological concepts and historical knowledge through discussions, integrated assignments, presentations, and hands-on laboratory activities. Let's explore the thrill of biology and history, together.



Students receive 4 AHSE credits and 4 Science credits for this course.

Instructor: Zachary del Rosario

Credits: 4 (2 MTH, 2 SCI)

Curriculum Role: ProbStat requirement or ECE Elective or Math general credit

Registration Note: students who previously took MTH2131 and ENGR3531 are not eligible to take this class

Course Description: Data Science is a powerful toolkit for using data to answer questions and guide decision making. It involves skills and knowledge from statistics, software engineering, machine learning, and data engineering. In this class, students work on data science projects that involve collecting data or finding data sources, exploratory data analysis and interactive visualization, statistical analysis, predictive analytics, model selection and validation. Course work involves readings and case studies on ethical practice in data science. This course may be used to satisfy the Probability and Statistics requirement.



This course awards 2 Math credits and 2 Science credits

Instructor: Orion Taylor

Credits: 4

Curriculum Role: Advance Math Mech E, Advance Math E:Robo, Math general credit

Registration Note: students who previously took this course as MTH3199 are not eligible to take this class.

Course Description: This course will be a mix of the standard advanced math topics for engineering students, including (but not limited to): numerical methods, ordinary and partial differential equations, optimization, algorithms, nonlinear dynamics, and linear algebra. Coursework will be split up into 1-2 week modules, which will be in the form of either mini-projects or problem sets (these will be the primary form of assessment). Class will be a combination of lecture and solo/group work time.

Instructor: Joanne Pratt

Credits: 4

Curriculum Role: Bio Foundation

Course Description: When the immune system functions properly, it identifies and eliminates infectious pathogens such as bacteria and viruses, as well as emerging cancer cells. When it malfunctions, however, the consequences can be severe: harmless microorganisms may trigger illness, autoimmune diseases and allergies can arise, and cancer cells may grow unchecked. In this foundational biology course, we will explore how the components of the immune system work together to protect the body. We will also examine cutting-edge technologies that harness this knowledge to transform modern medicine. You will design and carry out a lab-based project using immunological techniques, learning directly from the data you generate.

This course is still pending approval from the ARB.  If/when it is approved, information will be updated here.  If the class runs, it will be scheduled for MW 6:00pm - 7:40pm.

Instructor: Brad Minch, students

Credits: TBD

Curriculum Role: TBD

Course Description: TBD

If this course runs, it is currently slotted for TF 2:50pm - 4:30pm.

Instructor: TBD

Credits: 4 (either Engineering or Advanced Biology)

Curriculum Role: Advanced Biology, E:Bio

Course Description: TBD