I teach about shapes and colors! 💚 🔻 ◽ 💛 🔵 🔸 💜 In other words, I teach computational neuroscience (NEUR122, NEUR151, NEUR 189A) and vision science (NEUR042, NEUR 189B) at Pomona College.
Seeing & Believing
NEUR 042, spring semesters, open to all
vision science, modern AI, and creativity are inextricably interwoven. in this course we will discover how. We will approach this tapestry scientifically first and foremost, but also as scholars of the liberal arts.
As for the course title: seeing refers to the neurobiology of our visual systems and the psychology of perception. Seeing is a creative endeavor. I say so for two reasons: 1) our visual systems illustrate the ingenuity of natural selection; 2) we construct our perceptions based upon what we are believing of, and what we believe is molded by our societies, histories, and sciences. Visual art is often a manifestation of individual and societal beliefs. Seeing and believing go together because we hold our sense of sight in high esteem to inform what we regard as true. Generative AI calls this into renewed questioning.
Goals
- Appreciate the creativity and constructions of scientific discovery and our visual systems.
- Draw parallels and contrasts between biological and artificial neural networks.
- Critically examine the lineage and limits of contemporary AI.
- Build confidence in reading, discussing, and assessing research involving quantitative methods.
- Approach the future with clear eyes.
student reviews say:
- “I think the prof did an amazing job of giving purposeful work. None of the work was busy work – all really interesting and useful! I also loved the dissection lab – it was awesome to explore this topic from so many different angles including the kind of gross biological side.”
- “Her passion for the subject shone throughout the semester and made it an unforgettable class that changed how I think about vision.”
- “I believed the stuff I learn in this class will carry with me as I move on.”
below is an example of how a deep neural network’s “neurons” can develop representations that resemble those of real neurons in primate visual cortex. what does that even mean?? take this class to find out 🙂
Neural Computation
NEUR 151, spring semesters, upper-level seminar
In this research seminar we will explore two big questions:
- How can our understanding of the brain be enhanced by the use of neural network models?
- How can knowledge of neurobiology guide better AI development?
Along the way, we will build strong skills in reading and critiquing research papers and discussing their implications together. we will also gain some hands-on practice with building, training, and analyzing neural network models.
student reviews say:
- “Structured the course to allow helpful levels of independence and autonomy.”
- “She is very passionate and knowledgeable about this subject, gave us novel research papers to read that were exciting.”
- “Facilitated and encouraged the development of a community in the class.”
Check out some final projects from spring 2026!
Coding the Brain
NEUR 122, fall semesters, Python lab course
Everything we ever experience and enact is relayed via symphonies of little blips of electrical activity across our neurons. What does this activity mean? How do we know? What patterns or codes or consistencies exist in this symphony that allow us – or our nervous systems – to extract meaning and decide what to do next? And how do these patterns change over time with learning?
These are central questions to computational neuroscience and ones we will tackle together in this lab course as we explore the nervous system. We’ll dive into theories of neural encoding and decoding in different animal species, build computer simulations of neurons and networks, and explore models of learning. Since AI systems also encode and process information, we’ll see how research in AI and in computational neuroscience can be mutually beneficial.
student reviews say:
- “She doesn’t allow you to not understand something because of all the different ways she presents the material!”
- “No question was viewed as too small.”
- “Yuqing made sure people from neuroscience backgrounds could get on board with the math and coding and vice versa. As a math major, she made the class accessible to me (I didn’t know anything neuroscience related).”
- “Professor Zhu is the goattttt”
Below is an example of a class topic: modeling the brain as a dynamical system — specifically as a population of excitatory neurons (rE) and inhibitory (rI) neurons (the Wilson-cowan model). even though this is a simple model, the way population activity evolves over time (t) is pretty complex and cool, and depends a lot on initial conditions — this is the hallmark of a chaotic system. what is chaos really? come find out 🙂
Math Methods & Models in Neuroscience
NEUR 189A, fall 2023, 2024
(content now incorporated into NEUR 122)
Quantitative tools are becoming ever more crucial to neuroscientific research, and neuroscience is becoming ever more integrated with math-ier areas of inquiry. My goal is to make these tools and topics accessible through this course without needing to take a bunch of separate math and coding classes. If students are on better terms with math at the end of this course, then that is a win!
This course is divided into three parts:
- Linear algebra / dynamical systems
- Probability / statistics
- Machine learning
In tandem, we put these tools into practice through Python problem sets (psets).
student reviews say:
- “This was the most challenging course I have taken at Pomona, but one of the most rewarding at the same time! I really, really like professor Zhu and I think she is really talented in lecturing and explaining complex topics such as Machine Learning to beginners without taking away from the topic’s depth. 100% recommend to anyone I know.”
- “I came in as a complete beginner in statistics and coding, and I have learned so much in both!”
- “LVOE Yuqing <3”
Artificial & Biological Vision
NEUR 189B, spring 2024, 2025
(content now split into NEUR 042 (intro) and NEUR 151 (advanced))
In this course we explore the relationship between neurobiology and machinery by focusing on the sense of sight. We use this sense as an inroad to discussions of computation (how do we determine things?) and perception (how do we experience things?). The course is split into three modules.
Module 1: NATURE
we learn about vision in the natural world, including the optics of the eye and the neurobiology of visual systems across various organisms. Here we also lay down foundations of programming and mathematics for machine learning in Python. students begin to build simple machine vision models.
Module 2: machine
we learn about cutting-edge vision in machines, largely powered by neural network models. We see how computer scientists have applied knowledge of natural vision and the brain to build artificial vision systems, and we begin to make critical comparisons between biology and machinery. During this module, students build and train neural network models for machine vision in PyTorch.
Module 3: research
we bring neurobiology and machinery together by reading and discussing primary research at their intersection. students also apply their computational skills to a vision science research project of their own design.
student reviews say:
- “Learned more in this course than any other course before!”
- “had very little machine vision + bio vision experience before this, now is main academic interest”
Check out some final projects from spring 2025!
Looking forward to seeing you in class! Please get in touch if you have any questions.
***you do not have to be a neuroscientist to take my courses. if you are interested, then i am interested in having you in the class!***
Previously
School of the Art Institute of Chicago: Light & Vision
2019-2020: i was a lecturer in liberal arts at the School of the art institute of chicago. My course explored the physics of light and how visual systems allow animals to navigate the world using light. Two topics of special focus were color and evolution. We also delved into the history and philosophy of physics and neuroscience, and how artists have experimented with light and perception in their work.
University of Chicago
2018-2021: i was a teaching assistant for two undergraduate neuroscience courses at uchicago: systems neuroscience and fundamentals of neuroscience. we dissected a fair number of brains 🙂