ESP Biography
CLARA FANNJIANG, ESP Teacher
Major: Computer Science College/Employer: Stanford Year of Graduation: 2016 

Brief Biographical Sketch:
Not Available. Past Classes(Clicking a class title will bring you to the course's section of the corresponding course catalog)B4329: How Eye See: The Biology of Vision and Perception in Splash Spring 2015 (Apr. 11  12, 2015)
All day long, our retinas are bombarded with endless streams of photons. How does the eye and the brain translate these signals into meaningful, recognizable objects and scenes, allowing us to recognize a fourlegged blob as a dog despite innumerable variations in shape, viewpoint, and lighting? We will paint a broad picture of the mechanisms that allow humans to see, and more importantly, understand what we see. First, we will explore how the eyes and the brain learn to talk to each other during the first year of a child’s life. Second, we will discuss how the brain integrates information from individual neurons to represent objects, and we’ll see how functional imaging can reveal how the brain encodes what someone’s seeing. Throughout, we will emphasize how scientists designed the critical experiments to make these discoveries, and we’ll try our hand at brainstorming experiments ourselves!
M4053: For the Love of Optimization in Splash Fall 2014 (Nov. 08  09, 2014)
Pretty much anything important in life—the stability of the Golden Gate bridge, the quality of a compressed image, the edibility of a vegan ice cream, your happiness, or the likelihood of winning the lottery on your birthday—can be modeled as a mathematical function of one (or many more!) variables. Say we want to optimize of one of these functions. Easy, you say—set the derivative to zero! Duh. But what if we don't even have an formula for the function? What if our function lives in multidimensional space and checking every single point where the derivative is zero would take eons? What if all you have to guess what the function is, is a pile of super noisy data? What if the derivative isn't even computable?
What your calculus teachers have been hiding from you is that there exists an elegant framework for minimizing or maximizing functions, even when we can't describe what the function or its derivative looks like (or if it even has a derivative). In the age of big data, that's all you need to solve problems from 3D protein folding and predicting the next stockmarket crash, to filling in a damaged image or audio clip and designing a machine learning model for identifying faces. We'll show you the secrets of this magical optimization framework, and peer into the world of how to apply them to these awesome problems.
Core ideas: theory of mathematical optimization, optimization algorithms, machine learning, big data, and applications to science and engineering.
