I’m travelling to some cool events in the upcoming weeks. 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 the incredible program 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 won the McKibben and Merner Endowed Fellowship in Mathematics for my performance on my preliminary exams and first year courses!

Though it’s a far from perfect system, I’m relieved to say that I passed my preliminary 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 is put through a deletion channel, which deletes each bit with some constant probability, and noisy traces are output. This arises in DNA storage and sensor networks. The question catching the attention of many people: how many noisy traces are needed to reconstruct the original string with high probability? Currently, both the average case and worst case instances of this problem are dead stuck- the best methods have been pushed as far as they can go and no one has presented any other promising ways forward.

We are studying variants of this question on structures other than strings. This has several interesting applications in its own right, but might also give insight to a new method that can make progress in the string case.

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? Our goal is to 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. Ongoing work with Thomas Rothvoss and Yihao Zhang.