# Computer Science and Discrete Mathematics (CSDM)

## Fooling intersections of low-weight halfspaces

A weight-$t$ halfspace is a Boolean function $f(x)=\mathrm{sign}(w_1 x_1 + \cdots + w_n x_n - \theta)$ where each $w_i$ is an integer in $\{-t,\dots,t\}.$ We give an explicit pseudorandom generator that $\delta$-fools any intersection of $k$ weight-$t$ halfspaces with seed length poly$(\log n, \log k,t,1/\delta)$. In particular, our result gives an explicit PRG that fools any intersection of any quasipoly$(n)$ number of halfspaces of any polylog$(n)$ weight to any $1/$polylog$(n)$ accuracy using seed length polylog$(n).$

## On the strength of comparison queries

Joint work with Daniel Kane (UCSD) and Shachar Lovett (UCSD)

We construct near optimal linear decision trees for a variety of decision problems in combinatorics and discrete geometry.

For example, for any constant $k$, we construct linear decision trees that solve the $k$-SUM problem on $n$ elements using $O(n \log^2 n)$ linear queries. This settles a problem studied by [Meyer auf der Heide ’84, Meiser ‘93, Erickson ‘95, Ailon and Chazelle ‘05, Gronlund and Pettie '14, Gold and Sharir ’15, Cardinal et al '15, Ezra and Sharir ’16] and others.

## A nearly optimal lower bound on the approximate degree of AC$^0$

The approximate degree of a Boolean function $f$ is the least degree of a real polynomial that approximates $f$ pointwise to error at most $1/3$. For any constant $\delta > 0$, we exhibit an AC$^0$ function of approximate degree $\Omega(n^{1-\delta})$. This improves over the best previous lower bound of $\Omega(n^{2/3})$ due to Aaronson and Shi, and nearly matches the trivial upper bound of $n$ that holds for any function.

## Structural aspects of the null-cone problem in invariant theory

## Barriers for rank methods in arithmetic complexity

Arithmetic complexity is considered (for many good reasons) simpler to understand than Boolean complexity. And indeed, we seem to have significantly more lower bound techniques and results in arithmetic complexity than in Boolean complexity. Despite many successes and rapid progress, however, foundational challenges, like proving super-polynomial lower bounds on circuit or formula size for explicit polynomials, or super-linear lower bounds on explicit 3-dimensional tensors, remain elusive.

## Elementary open problems in Algebra (with consequences in computational complexity)

## Crossing the logarithmic barrier for dynamic boolean data structure lower bounds

This paper proves the first super-logarithmic lower bounds on the cell-probe complexity of dynamic boolean (a.k.a. decision) data structure problems, a long-standing milestone in data structure lower bounds.

## Lifting theorems in communication complexity and applications

Communication complexity studies the minimal amount of information two parties need to exchange to compute a joint function of their inputs. Results, especially lower bounds in this basic model found applications in many other areas of computer science.