Princeton Splash
Welcome to Princeton Splash, a student-run organization at Princeton University

Splash Biography

EMILY DE JONG, Junior in Chemical Engineering--I like math!

Major: Chemical & Bio Engineering

College/Employer: Princeton

Year of Graduation: 2019

Picture of Emily de Jong

Brief Biographical Sketch:

I'm a junior passionate about applied mathematical modeling and optimization, particularly related to energy. How fast do chemicals absorb into the ground? How do we minimize waste and maximize profit from oil refineries? How can we efficiently use first principles to simulate atoms? These are all examples of problems I've investigated through my coursework, research, and internships during college.I'm always curious about how to abstract really hard problems into something a computer can solve. I find myself at the intersection of chemical engineering and mathematics, and it's a great place to be!

Outside of class I enjoy playing in the orchestra as well as cycling, running, and playing tennis. Music and sports are also big parts of my life and something I love to talk about as well.

Past Classes

  (Clicking a class title will bring you to the course's section of the corresponding course catalog)

E616: Bike Maintenance and Safe Cycling in Splash Spring 2019 (Apr. 27, 2019)
Learn how to keep your bike in tip-top condition and ride like a pro! This class will teach you how to repair and tweak brakes, tyres & tubes, gearing, and more. We will also look at techniques for staying safe on the road and general road riding etiquette. By the end, you should be able to safely enjoy all the freedom that two wheels can offer you!

M511: Genetic Algorithms for Nonconvex Optimization in Splash Spring 2018 (Apr. 21, 2018)
What happens when you take the principles of natural selection and apply them to computer science? Evolutionary algorithms! This course will discuss the concept of "convexity" and what it means to find a local versus a global minimum on a computer. It turns out that finding a global minimum can be a lot harder than it sounds...enter genetic algorithms. You'll learn how to mate and mutate functions to approximate real-world data and find optimal solutions; we'll cover an overview of this problem-solving approach including an interactive example! Never heard of convexity of optimization? No problem--this course is intended as a high level overview of the field and a closer inspection of one really neat class of algorithms. No computer science or coding experience required. Some mathematical background would be helpful.