I am a final-year PhD candidate at Carnegie Mellon University’s Computer Science Theory Group.

I am broadly interested in algorithms, with a focus on algorithms under uncertainty. This includes (among others) online, dynamic, streaming and distributed algorithms.

I am fortunate to be advised by Bernhard Haeupler. Prior to coming to CMU, I completed an MSc at the Technion, where I was fortunate to be advised by
Nir Ailon,
Seffi Naor and
Hadas Shachnai.

GHC 7001, Carnegie Mellon University

We present a new dynamic matching sparsification scheme. From this scheme we derive a framework for dynamically rounding fractional matchings against *adaptive adversaries*. Plugging in known dynamic fractional matching algorithms into our framework, we obtain numerous randomized dynamic matching algorithms which work against adaptive adversaries (the first such algorithms, as all previous randomized algorithms for this problem assumed an *oblivious* adversary). In particular, for any constant ɛ>0, our framework yields (2+ɛ)-approximate algorithms with constant update time or polylog worst-case update time, as well as (2-ɛ)-approximate algorithms in bipartite graphs with arbitrarily-small polynomial update time, with all these algorithms' guarantees holding against adaptive adversaries. All these results achieve *polynomially* better update time to approximation tradeoffs than previously known to be achievable against adaptive adversaries.

We study network coding gaps for the problem of makespan minimization of multiple unicasts.
In this problem distinct packets at different nodes in a network need to be delivered to a destination
specific to each packet, as fast as possible. The network coding gap specifies how much coding
packets together in a network can help compared to the more natural approach of routing.

While makespan minimization using routing has been intensely studied for the multiple unicasts
problem, no bounds on network coding gaps for this problem are known. We develop new techniques
which allow us to upper bound the network coding gap for the makespan of k unicasts, proving
this gap is at most polylogarithmic in k. Complementing this result, we show there exist instances
of k unicasts for which this coding gap is polylogarithmic in k. Our results also hold for average
completion time, and more generally any ell_p norm of completion times.

The online matching problem was introduced by Karp, Vazirani and Vazirani nearly three decades ago. In that seminal work, they studied this problem in bipartite graphs with vertices arriving only on one side, and presented optimal deterministic and randomized algorithms for this setting. In comparison, more general arrival models, such as edge arrivals and general vertex arrivals, have proven more challenging, and positive results are known only for various relaxations of the problem. In particular, even the basic question of whether randomization allows one to beat the trivially-optimal deterministic competitive ratio of 1/2 for either of these models was open. In this paper, we resolve this question for both these natural arrival models, and show the following.

- For edge arrivals, randomization does not help | no randomized algorithm is better than 1/2 competitive.
- For general vertex arrivals, randomization helps | there exists a randomized (1/2+ Ω(1))-competitive online matching algorithm.

Vizing’s celebrated theorem asserts that any graph of maximum degree admits an edge
coloring using at most Δ+1 colors. In contrast, Bar-Noy, Motwani and Naor showed over a
quarter century ago that the trivial greedy algorithm, which uses 2Δ-1 colors, is optimal among
online algorithms. Their lower bound has a caveat, however: it only applies to low-degree
graphs, with Δ = O(log n), and they conjectured the existence of online algorithms using
(1+o(1))·Δ colors for Δ = ω(log n). Progress towards resolving this conjecture was only made
under stochastic arrivals (Aggarwal et al., FOCS’03 and Bahmani et al., SODA’10).

We resolve the above conjecture for adversarial vertex arrivals in bipartite graphs, for which
we present a (1+o(1))·Δ-edge-coloring algorithm for Δ = ω(log n) known a priori. Surprisingly,
if Δ is not known ahead of time, we show that no online (e/(e-1)-Ω(1))·Δ-edge-coloring algorithm exists.
We then provide an optimal, (e/(e-1)+o(1))·Δ-edge-coloring algorithm for unknown Δ = ω(log n).
Key to our results, and of possible independent interest, is a novel fractional relaxation for edge
coloring, for which we present optimal fractional online algorithms and a near-lossless online
rounding scheme, yielding our optimal randomized algorithms.

We study the minimum-cost metric perfect matching problem under online i.i.d arrivals. We
are given a xed metric with a server at each of the points, and then requests arrive online,
each drawn independently from a known probability distribution over the points. Each request
has to be matched to a free server, with cost equal to the distance. The goal is to minimize the
expected total cost of the matching.

Such stochastic arrival models have been widely studied for the maximization variants of
the online matching problem; however, the only known result for the minimization problem is a
tight O(log n)-competitiveness for the random-order arrival model. This is in contrast with the
adversarial model, where an optimal competitive ratio of O(log n) has long been conjectured
and remains a tantalizing open question.

In this paper, we show improved results in the i.i.d arrival model. We show how the i.i.d
model can be used to give substantially better algorithms: our main result is an O((log log log n)2)-
competitive algorithm in this model. Along the way we give a 9-competitive algorithm for the
line and tree metrics. Both results imply a strict separation between the i.i.d model and the
adversarial and random order models, both for general metrics and these much-studied metrics.

In a recent breakthrough, Paz and Schwartzman (SODA'17) presented a single-pass (2+ɛ)-approximation algorithm for the maximum weight matching problem in the semi-streaming model. Their algorithm uses O(n log^2 n) bits of space, for any constant ɛ>0.

We present two simplified and more intuitive analyses, for essentially the same algorithm, which also improve the space complexity to the optimal bound of O(n log n) bits --- this is optimal as the output matching requires \Omega(n log n) bits. Our analyses rely on a simple use of the primal dual method and a simple accounting method.

J2TEAC 2018 **Special Issue on EC 15 **.
Supersedes the EC 15 paper below.

Motivated by Internet targeted advertising, we address several ad allocation problems. Prior work
has established these problems admit no randomized online algorithm better than (1-1/e)-competitive
([Karp et al. 1990; Mehta et al. 2007]), yet simple heuristics have been observed to perform much
better in practice. We explain this phenomenon by studying a generalization of the bounded-degree
inputs considered by [Buchbinder et al. 2007), graphs which we call (k,d)-bounded. In such graphs
the maximal degree on the online side is at most d and the minimal degree on the offline side is at
least k. We prove that for such graphs, these problems' natural greedy algorithms attain competitive
ratio 1-(d-1)/(k+d-1), tending to one as d/k tends to zero. We prove this bound is tight for these
algorithms.

Next, we develop deterministic primal-dual algorithms for the above problems achieving competitive
ratio 1-(1-1/d)k>1-1/e^{k/d}, or exponentially better loss as a function of k/d, and strictly better
than 1-1/e whenever k ≥ d. We complement our lower bounds with matching upper bounds for the vertex-
weighted problem. Finally, we use our deterministic algorithms to prove by dual-fitting that simple
randomized algorithms achieve the same bounds in expectation. Our algorithms and analysis differ
from previous ad allocation algorithms, which largely scale bids based on the spent fraction of
their bidder's budget, whereas we scale bids according to the number of times the bidder could have
spent as much as her current bid. Our algorithms differ from previous online primal-dual algorithms,
as they do not maintain dual feasibility, but only primal-to-dual ratio, and only attain dual
feasibility upon termination. We believe our techniques could find applications to other well-
behaved online packing problems.

Distributed graph algorithms that separately optimize for either the number of
rounds used or the total number of messages sent have been studied extensively. However,
algorithms simultaneously efficient with respect to both measures have been elusive.
For example, only very recently was it shown that for Minimum Spanning Tree
(MST), an optimal message and round complexity is achievable (up to polylog terms)
by a single algorithm in the CONGEST model of communication.

In this paper we provide algorithms that are simultaneously round- and message-optimal
for a number of well-studied distributed optimization problems. Our main
result is such a distributed algorithm for the fundamental primitive of computing simple
functions over each part of a graph partition. From this algorithm we derive round- and
message-optimal algorithms for multiple problems, including MST, Approximate Min-Cut
and Approximate Single Source Shortest Paths, among others. On general graphs
all of our algorithms achieve worst-case optimal ˜O(D+sqrt{n}) round complexity and ˜O(m)
message complexity. Furthermore, our algorithms require an optimal ˜O(D) rounds and
˜O(n) messages on planar, genus-bounded, treewidth-bounded and pathwidth-bounded
graphs.

We study the classic Bin Packing problem in
a fully-dynamic setting, where new items can arrive and old items may depart.
We want algorithms with low asymptotic competitive
ratio *while repacking items sparingly* between updates. Formally, each item
i has a *movement cost* c_{i}≥0, and we want to use
α·OPT bins and incur a movement cost
γ·c_{i}, either in the worst case, or in an amortized sense, for
α, γ as small as possible. We call γ the *recourse*
of the algorithm.
This is motivated by cloud storage applications,
where fully-dynamic Bin Packing models the problem of data
backup to minimize the number of disks used, as well as communication
incurred in moving file backups between disks.
Since the set of files changes over time, we could
recompute a solution periodically
from scratch, but this would give a high number of disk rewrites, incurring
a high energy cost and possible wear and tear of the disks. In this work, we
present optimal
tradeoffs between number of bins used and number of items repacked, as well as
natural extensions of the latter measure.

C7ICALP 2018

We present a simple randomized reduction from fully-dynamic integral matching algorithms to fully-dynamic “approximately-maximal” fractional matching algorithms. Applying this reduction to the recent fractional matching algorithm of Bhattacharya, Henzinger, and Nanongkai (SODA 2017), we obtain a novel result for the integral problem. Specifically, our main result is a randomized fully-dynamic (2+ɛ)-approximate integral matching algorithm with small polylog worst-case update time. For the (2+ɛ)-approximation regime only a fractional fullydynamic (2+ɛ)-matching algorithm with worst-case polylog update time was previously known, due to Bhattacharya et al. (SODA 2017). Our algorithm is the first algorithm that maintains approximate matchings with worst-case update time better than polynomial, for any constant approximation ratio. As a consequence, we also obtain the first constant-approximate worst-case polylogarithmic update time maximum weight matching algorithm.

In this paper we study the classic online matching problem,
introduced in the seminal work of Karp, Vazirani
and Vazirani (STOC 1990), in regular graphs. For such
graphs, an optimal deterministic algorithm as well as
efficient algorithms under stochastic input assumptions
were known. In this work, we present a novel randomized
algorithm with competitive ratio tending to one
on this family of graphs, under *adversarial* arrival order.
Our main contribution is a novel algorithm which
achieves competitive ratio 1-O(sqrt(log d)/ sqrt(d))
in expectation on d-regular graphs. In contrast, we show that
all previously-studied online algorithms have competitive
ratio strictly bounded away from one. Moreover, we
show the convergence rate of our algorithm’s competitive
ratio to one is nearly tight, as no algorithm achieves
competitive ratio better than 1-O(1/sqrt(d)). Finally, we
show that our algorithm yields a similar competitive ratio
with high probability, as well as guaranteeing each
vertex a probability of being matched tending to *one*.

We present a faster distributed broadcasting primitive for
the classical radio network model.

The most basic distributed radio network broadcasting primitive - called Decay - dates back to
PODC'87 result of Bar-Yehuda, Goldreich, and Itai. In any radio network with some informed source
nodes, running Decay for O(d log n + log^2 n) rounds informs all nodes at most d hops away from a
source with high probability. Since 1987 this primitive has been the most important building block
for implementing many other functionalities in radio networks. The only improvements to this
decades-old algorithm are slight variations due to [Czumaj, Rytter; FOCS'03] and [Kowalski and Pelc,
PODC'03] which achieve the same functionality in O(d log(n/d) + log^2 n) rounds. To obtain a speedup
from this, d and thus also the network diameter need to be near linear, i.e., larger than n^(1-eps).

Our new distributed primitive spreads messages for d hops in
O(d (log n log log n)/(log d) + log^{O(1)} n) rounds with high probability.
This improves over Decay for any super-polylogarithmic d = log^{omega(1)} n and achieves
near-optimal O(d log log n) running time for d = n^eps.
This also makes progress on an open question of Peleg.

Motivated by Internet targeted advertising, we address several ad allocation problems. Prior work
has established these problems admit no randomized online algorithm better than (1-1/e)-competitive
([Karp et al. 1990; Mehta et al. 2007]), yet simple heuristics have been observed to perform much
better in practice. We explain this phenomenon by studying a generalization of the bounded-degree
inputs considered by [Buchbinder et al. 2007), graphs which we call (k,d)-bounded. In such graphs
the maximal degree on the online side is at most d and the minimal degree on the offline side is at
least k. We prove that for such graphs, these problems' natural greedy algorithms attain competitive
ratio 1-(d-1)/(k+d-1), tending to one as d/k tends to zero. We prove this bound is tight for these
algorithms.

Next, we develop deterministic primal-dual algorithms for the above problems achieving competitive
ratio 1-(1-1/d)k>1-1/e^{k/d}, or exponentially better loss as a function of k/d, and strictly better
than 1-1/e whenever k ≥ d. We complement our lower bounds with matching upper bounds for the vertex-
weighted problem. Finally, we use our deterministic algorithms to prove by dual-fitting that simple
randomized algorithms achieve the same bounds in expectation. Our algorithms and analysis differ
from previous ad allocation algorithms, which largely scale bids based on the spent fraction of
their bidder's budget, whereas we scale bids according to the number of times the bidder could have
spent as much as her current bid. Our algorithms differ from previous online primal-dual algorithms,
as they do not maintain dual feasibility, but only primal-to-dual ratio, and only attain dual
feasibility upon termination. We believe our techniques could find applications to other well-
behaved online packing problems.

The majority of Web email is known to be generated by machines even when one excludes spam. Many
machine-generated email messages such as invoices or travel itineraries are critical to users.
Recent research studies establish that causality relations between certain types of machine-
generated email messages exist and can be mined. These relations exhibit a link between a given
message to a past message that gave rise to its creation. For example, a shipment notification
message can often be linked to a past online purchase message. Instead of studying how an incoming
message can be linked to the past, we propose here to focus on predicting future email arrival as
implied by causality relations. Such a prediction method has several potential applications, ranging
from improved ad targeting in up sell scenarios to reducing false positives in spam detection.

We introduce a novel approach for predicting which types of machine-generated email messages,
represented by so-called "email templates", a user should receive in future time windows. Our
prediction approach relies on (1) statistically inferring causality relations between email
templates, (2) building a generative model that explains the inbox of each user using those
causality relations, and (3) combining those results to predict which email templates are likely to
appear in future time frames. We present preliminary experimental results and some data insights
obtained by analyzing several million inboxes of Yahoo Mail users, who voluntarily opted-in for such
research.

In many computational and economic models of multi-agent interaction, each participant repeatedly "best-responds" to the others' actions. Game theory research on the prominent "best-response dynamics" model typically relies on the premise that the interaction between agents is somehow synchronized. However, in many real-life settings, e.g., internet protocols and large-scale markets, the interaction between participants is asynchronous. We tackle the following important questions: (1) When are best-response dynamics guaranteed to converge to an equilibrium even under asynchrony? (2) What is the (computational and communication) complexity of verifying guaranteed convergence? We show that, in general, verifying guaranteed convergence is intractable. In fact, our main negative result establishes that this task is undecidable. We exhibit, in contrast, positive results for several environments of interest, including complete, computationally-tractable, characterizations of convergent systems. We discuss the algorithmic implications of our results, which extend beyond best-response dynamics to applications such as asynchronous Boolean circuits.

J1 Discrete Mathematics & Theoretical Computer Science 2011

Given a graph G=(V,E) and a weight function w:E →R, a coloring of vertices of G, induced by w, is defined by χw(v) = ∑_{e∋v} w(e) for all v∈V. In this paper, we show that determining whether a particular graph has a weighting of the edges from {1,2} that induces a proper vertex coloring is NP-complete.

"Education's purpose is to replace an empty mind with an open one."

- Malcolm Forbes.

I have had the pleasure of teaching various courses, both at the Technion and at CMU.

__At Carnegie Mellon:__

**Graduate Algorithms** (15-750):

Spring 2019

**Probability and Computing** (15-359/659):

Spring 2015

__At the Technion:__

**Data Structures 1** (234218):

/ôrˈTHōəpē

1. The correct or accepted pronunciation of words.

2. The study of correct or accepted pronunciation.

``How do you pronounce 'Wajc'?", you ask?

**Answer: The second syllable of the words e-vites and invites**.

Using the International Phonetic Alphabet, Wajc would be spelled [vajts].

Still unsure? Check out this recording of Kira Radinsky:

Here's a subset of a Polish transcription table to explain how those letters are supposed to represent those sounds.

Ww | v | as in "vat" |

Aa | o | as in "hot" |

Jj | y | as in "yes" |

Cc | ts | as in "bits" |

And before you ask: no, I don't speak Polish. Here's a relevant meme by Krzysztof Onak: