Abstract: Reaction networks are a powerful tool for modeling the behavior of a wide variety of real-world systems, including population dynamics and chemical processes, as well as algorithms for sampling combinatorial objects. While many such systems have well-understood equilibrium states, the long-standing conjecture that these states will always be achieved remains open.

This talk presents the class of simplicial reaction networks, which includes a wide variety of natural combinatorial examples. I will show how simplicial structures can be used to understand and control the equilibrium behavior of the network as a whole, and discuss related progress towards the Global Attractor Conjecture. Finally, I will present additional work exploring combinatorial approaches to the Inverse Eigenvalue Problem on graphs, including the randomized Zero Forcing algorithm and a lower bound for the Minimum Rank problem.

It’s been quite a summer for outreach! I recently participated in the Museum of Mathematics’ Mathematics Outreach Seminar and Training program, which was a great opportunity to workshop outreach talks alongside a vibrant crew of early-career mathematicians — and push our comfort zones with eclectic public speaking exercises. Check us out in the New York Times!

Topping off the summer of teaching activities, I also earned Berkeley’s Certificate in Teaching and Learning in Higher Education, the culmination of many semesters of classroom experience. The certificate program involved coursework, several semesters of teaching, classroom observations and assessments, and design of an original course syllabus and teaching portfolio. 

Looking forward to starting the next chapter of outreach with some exciting public talks to be announced soon!

I’ll be at the JMM ILAS special session to speak about my recent work on the Computational Hardness of the Minimum Rank Problem on Graphs.

My coauthor Sam will also be presenting our joint work on Zero Forcing with Random Sets.

New on the arXiv:  Zero Forcing with Random Sets. 

Given a graph G and a real number 0 ≤ p ≤ 1, we define the random set Bp(G) ⊂ V(G) by including each vertex independently and with probability p. We investigate the probability that the random set Bp(G) is a zero forcing set of G. In particular, we prove that for large n, this probability for trees is upper bounded by the corresponding probability for a path graph. Given a minimum degree condition, we also prove a conjecture of Boyer et. al. regarding the number of zero forcing sets of a given size that a graph can have.

The Berkeley EECS Department was kind enough to award me with the EECS Outstanding TA award this year! I’m very grateful to the colleagues who nominated me, and to all the wonderful students who made teaching discrete math and probability a delightful experience.

I was fortunate to receive the D.E. Shaw Zenith Fellowship award this year. Despite the name, here’s hoping the best is yet to come.

I decided to try out making blog posts based on the notes I take when I read papers. The first one is about a paper I’m presenting in my Neural Network Mathematics reading group on 4/5: Adversarial Examples from Computational Constraints by Bubeck, Price, and Razenshteyn.

This semester, I participated in Berkeley’s 2-day PhD workshop on Communications for Engineering Leaders! We were challenged to design 5-minute lightning research talks, accessible to a broad audience of engineers and scientists.

Last year, I was accepted to the 2020-21 AMS research community on the Inverse Eigenvalue Problem. Since then, we’ve had an intensive learning workshop, and now a special session at the 2021 Joint Mathematics Meetings, leading up to our (virtual) collaboration this summer.

Check out our session at the 2021 JMM site for more information.

I also attended ITCS for the first time, and I highly recommend the short talk format. This is definitely a great conference for Theory students to attend and absorb new ideas!

I started off the new year by participating in a thought-provoking new mini course from Harvard GSAS: Disrupting the New Jim Code: Applying Principles of Anti-Racism to Algorithmic Fairness, Accountability, and Ethics.

We talked about many different lenses on ethical development of algorithms and machine learning, and read from literature spanning social science to law to computer science. 

I highly recommend this course to researchers and practicioners alike, and to anyone in a position to work towards anti-racist, ethical decisionmaking.