
Hadley Black
 PhD Student, Computer Science
 University of California, Los Angeles

 Lab: Engineering VI  479
 email: hablack[at]cs[dot]ucla[dot]edu

 My CV

Short Bio I am a second year PhD student in Computer Science at UCLA where my adviser is Raghu Meka. I received my MS in Computer Science from UCSC in 2018 where I was advised by C. Seshadhri. Prior to that I received my BA in Computer Science and BA in Pure Mathematics from UCSC in 2016.

Research Interests I am broadly interested in Theoretical Computer Science, Combinatorics, and Probability Theory. Especially randomized algorithms, property testing, and boolean functions.


Publications
 • Domain Reduction for Monotonicity Testing: A o(d) Tester for Boolean Functions in dDimensions
 Hadley Black, Deeparnab Chakrabarty, and C. Seshadhri
Symposium on Discrete Algorithms (SODA) 2020.
 abstract
ECCC:TR18187
arXiv:1811.01427
We describe a $\tilde{O}(d^{5/6})$query monotonicity tester for Boolean functions $f:[n]^d \to \{0,1\}$ on the $n$hypergrid. This is the first $o(d)$ monotonicity tester with query complexity independent of $n$. Motivated by this independence of $n$, we initiate the study of monotonicity testing of measurable Boolean functions $f:\mathbb{R}^d \to \{0,1\}$ over the continuous domain, where the distance is measured with respect to a product distribution over $\mathbb{R}^d$. We give a $\tilde{O}(d^{5/6})$query monotonicity tester for such functions.
Our main technical result is a domain reduction theorem for monotonicity. For any function $f:[n]^d \to \{0,1\}$, let $\epsilon_f$ be its distance to monotonicity. Consider the restriction $\hat{f}$ of the function on a random $[k]^d$ subhypergrid of the original domain. We show that for $k = \text{poly}(d/\epsilon)$, the expected distance of the restriction is $\mathbb{E}[\epsilon_{\hat{f}}] = \Omega(\epsilon_f)$. Previously, such a result was only known for $d=1$ (BermanRaskhodnikovaYaroslavtsev, STOC 2014). Our result for testing Boolean functions over $[n]^d$ then follows by applying the $d^{5/6}\cdot \text{poly}(1/\epsilon,\log n, \log d)$query hypergrid tester of BlackChakrabartySeshadhri (SODA 2018).
To obtain the result for testing Boolean functions over $\mathbb{R}^d$, we use standard measure theoretic tools to reduce monotonicity testing of a measurable function $f$ to monotonicity testing of a discretized version of $f$ over a hypergrid domain $[N]^d$ for large, but finite, $N$ (that may depend on $f$). The independence of $N$ in the hypergrid tester is crucial to getting the final tester over $\mathbb{R}^d$.
 • A o(d) polylog n Monotonicity Tester for Boolean Functions over the Hypergrid [n]^{d}
 Hadley Black, Deeparnab Chakrabarty, and C. Seshadhri
Symposium on Discrete Algorithms (SODA) 2018.
 abstract
ECCC:TR17159
arXiv:1710.10545
We study monotonicity testing of Boolean functions over the hypergrid [n]^d and design a nonadaptive tester with 1sided error whose query complexity is O(d^{5/6}) \poly(\log n,1/\eps). Previous to our work, the best known testers had query complexity linear in d but independent of n. We improve upon these testers as long as n = 2^{d^{o(1)}}.
To obtain our results, we work with what we call "the augmented hypergrid", which adds extra edges to the hypergrid. Our main technical contribution is a Margulisstyle isoperimetric result for the augmented hypergrid, and our tester, like previous testers for the hypercube domain, performs directed random walks on this structure.
Theses
 Identity Testing for Depth3 Circuits with Small Fanin
(Undergraduate Thesis)  2016 >
pdf
Other stuff I like...
 Music. Guitar, electronic composition. Here's some music I've made.
 Rock climbing.
 Chess.
 Coffee.
 Teaching.
