I teach about shapes and colors! 💚 🔻 ◽ 💛 🔵 🔸 💜 In other words, I teach math for neuroscience (NEUR 189A) and vision science (NEUR 189B) at Pomona College.
Math Methods & Models in Neuroscience
NEUR 189A, fall semesters
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”
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 🙂
Artificial & Biological Vision
NEUR 189B, spring semesters
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”
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 🙂
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 🙂