skip to main content
10.1145/3293611.3341565acmconferencesArticle/Chapter ViewAbstractPublication PagespodcConference Proceedingsconference-collections
abstract

2019 Principles of Distributed Computing Doctoral Dissertation Award

Published: 16 July 2019 Publication History

Abstract

The winner of the 2019 Principles of Distributed Computing Doctoral Dissertation Award is Dr. Sepehr Assadi for his dissertation Combinatorial Optimization on Massive Datasets: Streaming, Distributed, and Massively Parallel Computation, written under the supervision of Prof. Sanjeev Khanna at the University of Pennsylvania.
The thesis resolves a number of long-standing problems in the exciting and still relatively new area of sublinear computation. The area of sublinear computation focuses on design of algorithms that use sublinear space, time, or communication to solve global optimization problems on very large datasets. In addition to addressing a wide range of different problems, comprising graph optimization problems (matching, vertex cover, and connectivity), submodular optimization (set cover and maximum coverage), and resource-constrained optimization (combinatorial auctions and learning), these problems are studied in three different models of computation, namely, streaming algorithms, multiparty communication, and massively parallel computation (MPC). The thesis also reveals interesting relations between these different models, including generic algorithmic and analysis techniques that can be applied in all of them.
For many fundamental optimization problems, the thesis gives asymptotically matching algorithmic and intractability results, completely resolving several long-standing problems. This is accomplished by using a broad spectrum of mathematical methods in very detailed and intricate proofs. In addition to a wide variety of classic techniques, ranging from graph theory, combinatorics, probability, linear algebra and calculus, it also makes heavy use of communication complexity and information theory, for example.
Sepehr's dissertation work has been published in a remarkably large number of top-conference papers. It received multiple best paper awards and multiple special issue invitations, as well as two invitations to the Highlights of Algorithms (HALG) conference. Due to its contributions to the field of distributed computing and all the merits mentioned above, the award committee unanimously selected this thesis as the winner of the 2019 Principles of Distributed Computing Doctoral Dissertation Award.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
PODC '19: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing
July 2019
563 pages
ISBN:9781450362177
DOI:10.1145/3293611
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 July 2019

Check for updates

Qualifiers

  • Abstract

Conference

PODC '19
Sponsor:
PODC '19: ACM Symposium on Principles of Distributed Computing
July 29 - August 2, 2019
Toronto ON, Canada

Acceptance Rates

PODC '19 Paper Acceptance Rate 48 of 173 submissions, 28%;
Overall Acceptance Rate 740 of 2,477 submissions, 30%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 105
    Total Downloads
  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media