This weekend I’ll be in Nebraska (for the first time) for the Nebraska Conference for Undergraduate Women in Mathematics. I’m happy to be attending this event as an invited graduate student, as several of my friends in grad school benefitted from it as undergraduates!

Heading to Baltimore this week for the AMS short course on Sum of Squares. While I’m in the city, I’ll also be visiting the theory group at Johns Hopkins for a day.

Next week, I’ll be at the Women in Theory Workshop in Boston. Right after, I’m heading to STOC Theory Fest in LA.

I’m continuing to work with FEPPS this summer as an instructor for Math 107, Math in Society. The course samples from a collection of topics like set theory, algebra, finances, and even math history.

Today I start volunteering with FEPPS, the Freedom Education Project for the Puget Sound. I’ll be at the Washinton Corrections Center for Women on Saturday mornings to tutor in study halls for women enrolled in FEPPS’s math courses.

I received the McKibben and Merner Endowed Fellowship in Mathematics for my performance on my preliminary exams and first year courses.

I’m relieved to say that I passed my prelim exams!

Big thanks to my friends in the math department for their collaboration and to my first year professors for their support and availability.

In the trace reconstruction problem, an unknown binary string on n nodes is put through a deletion channel, which deletes each bit with some constant probability, and noisy traces are output. The central question is how many noisy traces are needed to reconstruct the original string with high probability? We study a generalization of the problem on trees. For many classes of trees, including complete trees and spiders, we provide algorithms that reconstruct the labels using only a polynomial number of traces. This exhibits a stark contrast to known results on string trace reconstruction, which require exponentially many traces. Joint work with Miklos Racz and Cyrus Rashtchian.

Given a set of presents and a set of children who each desire some subset of presents: how should Santa distribute presents among the kids in order to maximize the minimum happiness of any child? We design an approximation algorithm which not only gives a better constant factor approximation than the best known, but which also compares to an LP smaller than those in all previous work. Joint work with Thomas Rothvoss and Yihao Zhang.