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Shlomi Dolev <[log in to unmask]>
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Shlomi Dolev <[log in to unmask]>
Thu, 23 Mar 2023 08:40:24 +0200
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2023 Principles of Distributed Computing Doctoral -- Dissertation Award

Many exceptionally high-quality doctoral dissertations were submitted for
the 2023 Principles of Distributed Computing
Doctoral Dissertation Award. After careful deliberation, the award
committee decided to share the award between:

• Dr. Siddhartha Jayanti for his dissertation “Simple, Fast, Scalable, and
Reliable Multiprocessor Algorithms.”

• Dr. Dean Leitersdorf for his dissertation “Fast Distributed Algorithms
via Sparsity Awareness.”

Dr. Siddhartha Jayanti completed his PhD on November 27th 2022, under the
supervision of Prof. Julian Shun, at MIT. In
his thesis, Dr. Jayanti identifies simplicity, speed, scalability, and
reliability as four core design goals for multiprocessor
algorithms, and designs and analyzes algorithms that meet these goals. The
thesis comprises a vast number of novel
results in the scope of distributed and concurrent synchronization. His
algorithmic contributions include a scalable
algorithm for concurrent union-find, a wait-free linearizable, fast array
data structure that supports standard array
operations in constant time and optimal space, and mutual exclusion (lock)
algorithms with optimal complexity for
real-time and persistent memory systems. Dr. Jayanti also defines a
generalization of the fundamental wake-up problem,
permitting him to prove fundamental new hardness results for many standard
data structures, including queues, stacks,
priority queues, counters, and union-find data structures. Moreover, he
devises a novel simple-to-use technique for
producing machine-verified proofs of the correctness (linearizability and
strong linearizability) of concurrent algorithms,
and successfully applied this method to verify fundamental data multicore
data structures, such as queues, union-find,
and snapshot objects.

Dr. Dean Leiterdorf completed his thesis on May 14th, 2022, under the
supervision of Prof. Keren Censor-Hillel, at
the Technion. In his thesis, Dr. Leitersdorf designs fast distributed
algorithms for sparse matrix multiplication and
demonstates their usefulness by applying them to shortest path and subgraph
existence problems. Applications of
matrix multiplication are found in many fields, including scientific
computing, statistics, machine learning, and quantum
computing, and therefore fast algorithms for matrix multiplication are
critical for these. Dr. Leitersdorf does not
just come up with solutions that can exploit the sparsity of the input
matrices but also the sparsity of the output
matrix, which allows him to come up with a large number of results for
different communication models that partially
significantly improve the state of the art. Among these are constant-round
algorithms for computing graph spanners and
approximate all-pairs-shortest-paths as well as constant-round algorithms
for computing the girth of the input graph up
to an additive 1 in the Congested Clique model. Through reductions between
various models and a number of advanced
techniques, Dr. Leitersdorf extends his results also to the CONGEST model,
hybrid networks, and various other models.
On top of this, he also designs a variety of algorithms that speed up
clique detection in quantum computing settings
and whose runtime breaks lower bounds known for classical distributed

The award is sponsored jointly by the ACM Symposium on Principles of
Distributed Computing (PODC) and the EATCS
Symposium on Distributed Computing (DISC). It is presented annually, with
the presentation taking place alternately
at PODC and DISC. This year it will be presented at PODC, to be held in
Orlando, Florida USA, June 19-23, 2023.
The 2023 Principles of Distributed Computing Doctoral Dissertation Award

• Shlomi Dolev (Chair), BGU
• Rachid Guerraoui, EPFL
• Fabian Kuhn, University of Freiburg
• Woelfel Philipp, University of Calgary
• Christian Scheideler, Paderborn University



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