Passionate students who have had at least one year of university level math courses are encouraged to apply. The principal criteria for selection will be enthusiasm and compatibility with the interests of the mentors. You can find more information about the program below.
Please fill out the application form below if you are interested!
https://forms.gle/t2qJ3nJhjxWynfwt6
A directed reading program is an extracurricular program organized by the graduate students of the math department for the purpose of fostering mentorship and a sense of community between the graduate students and undergraduates. More specifically, a DRP pairs undergraduate math students with graduate students so that they can work on a topic of mutual interest. Typically, the undergraduates work through a book or a paper and meet roughly once a week with their mentors to discuss their progress and ask questions.
To provide a fun and stress-free environment for undergraduates to learn math in the style of a one-on-one reading course with a mentor.
To give undergraduates the ability to explore topics they might not see in the standard curriculum.
To expose undergraduates to mathematics outside of a classroom setting.
To give a less intimidating avenue for students to learn topics that might have seemed out of reach.
To give undergraduates experience in learning independently and presenting mathematics.
Participants (both mentors and mentees) are expected to attend the kick off event. (The date will be announced later)
Undergraduate students are expected to work for approximately 5 hours a week : 4 hours of independent reading and 1 hour in a meeting with their mentor.
Undergraduate students should meet with their mentor roughly once a week to discuss their progress.
At the end of the semester, the students are expected to give a presentation to the other students and mentors. This could be a short exposition of an interesting result, or a broad overview of the subject of their project.
This list reflects past projects starting in Spring 2024. Not all projects are necessarily included in this list, as it was made with the permission of the participants.
Machine learning Basics: A journey into a shallow neural network,
Emily Chandran, advised by Byeong-Ho Bahn
Differential Equations, Brownian motion, and Ito's lemma: Derivation of the Black Scholes Equation and the application to linear function
Jahnavi Anand Modi, advised by Byeong-Ho Bahn
Mordell's theorem
Yeju Shin, advised by SeongEun Jung
Special cases of Fermat's last theorem
Minh Do, advised by Benjamin Levine
Introduction to Knot Theory
Cullen Anderson, advised by Oskar Bzoma
Reciprocity laws, from quadratic to Artin
Wenshi Zhao, advised by Brody Lynch
Exploring and addressing the shortcomings of GPT-PINN
Shubhankar Tripathy, advised by Byeong-Ho Bahn
Machine learning, gradient descent and the PL inequality
Michael Weagle, advised by Byeong-Ho Bahn
Murmurations of Elliptic Curves
Dania Rustom and Maya Kandeshwarath, advised by SeongEun Jung
Geometric solutions to problems in number theory
Natalie Welling, advised by Brody Lynch