SoCalDM is a friendly, day-long research conference designed for discrete mathematicians in Southern California. For more details please visit the conference website:

https://sites.google.com/view/socaldm2025/home

Here is the schedule for the event: 

9:00-9:30 Welcome coffee

9:30-10:00 Jonathan Davidson (Cal State LA), A Combinatorial Design Approach to a Multicolor Bipartite Ramsey Problem

10:10-10:40 Lenny Fukshansky (Claremont McKenna College), On a new absolute version of Siegel’s lemma

10:40-11:00 Coffee Break

11:00-11:30 Mason Shurman (UCI), Covering Random Digraphs with Hamilton Cycles

11:40-12:10 Claire Levaillant (USC), Solutions to the Diophantine equation $\sum_{i=1}^n\frac{1}{x_i}=1$ in integers of the form p^a*q^b with p and q two distinct primes. 

12:10-2:00 Lunch

2:00-2:30 Sehun Jeong (Claremont Graduate University), Integral quadratic forms and lattice angles

2:40-3:10 Justin Troyka (Cal State LA), Growth rates of permutations with given descent or peak set

3:20-3:50 Yizhe Zhu (USC), CLTs for linear spectral statistics of inhomogeneous random graphs

There is an infinite pool of balls and an infinite number of boxes. Let's randomly drop n balls into the boxes and study the number of occupied boxes. Classical limit theorems, the law of large numbers and the central limit theorem, are non-trivial, but known. Can we now prove the law of the iterated logarithm? I will show how to do this using tools that have much wider applicability. The talk is based on joint work with Dariusz Buraczewski (Wroclaw, Poland) and Alexander Iksanov (Kyiv, Ukraine).

TBA

Abstract: In this talk, we present our results for the 1-bit phase retrieval problem: the recovery of a possibly sparse signal $x$ from the observations $y=sign(|Ax|-1)$. This problem is closely related to 1-bit compressed sensing where we observe $y=sign(Ax)$, and phase retrieval where we observe $y=|Ax|$. We show that the major findings in 1-bit compressed sensing theory, including hyperplane tessellation, optimal rate and a very recent efficient and optimal algorithm, can be developed in this phase retrieval setting. This is a joint work with Ming Yuan: https://arxiv.org/abs/2405.04733.