Acm Doctoral Dissertation Award Winners


ACM established the Doctoral Dissertation Award program to recognize and encourage superior research and writing by doctoral candidates in computer science and engineering. The award is presented each June at the ACM Awards Banquet and is accompanied by a prize of $20,000 plus travel expenses to the banquet.  As of January 1, 2014 all winning dissertations are published exclusively in print and electronic formats as part of the ACM Books Series, which includes distribution through the ACM Digital Library.  Honorable Mention(s) may also be awarded, with a prize of $10,000 shared among recipients.  Financial sponsorship of the award is provided by Google.


Only a Ph.D. student’s advisor may nominate a dissertation.  For each year’s award cycle, a nominated dissertation must have been successfully defended (not deposit date) by the department between October 2017 through (and including) September 2018.  The dissertation submitted should be a finalized version; if a student or advisor thinks a dissertation will be more competitive after revision, the dissertation defense should be postponed if necessary.  Nominations are welcomed from any country, but only English language versions will be accepted.  Only one nomination may be submitted per institution.  If an institution granted more than 10 PhD’s in that year, two dissertations may be nominated.

Next Deadline

October 31, 2018  - End of Day, Anywhere on Earth (AoE), UTC -12

Selection Criteria

Dissertations will be reviewed for technical depth and significance of the research contribution, potential impact on theory and practice, and quality of presentation.


Nominations for the ACM Doctoral Dissertation Award should be submitted using the online nomination form.  Make sure to select the Doctoral Dissertation Award in the dropdown box at the top.  Submitted materials should explain the contribution in terms understandable to a non-specialist.   Each nomination involves several components:

  • Name, address, phone number, and email address of the nominator (the student’s thesis advisor) 
  • Name, address, and email address of the candidate (person being nominated).  Affiliation should be the name of the school.
  • Suggested citation if the candidate is selected.  This should be a concise statement (maximum of 25 words) describing the key technical or professional accomplishment for which the candidate merits this award.  Note that the final wording for awardees will be at the discretion of the Award Committee.
  • Nomination statement (200-300 words in length) addressing why the candidate should receive this award.  This should address the significance of the dissertation, not simply repeat the information in the abstract.
  • Copy of the dissertation, together with a copyright transfer form filled out by the candidate and assigning exclusive publication rights to ACM (select either the copyright or license form).  Copyright will revert to the author if it is not selected for publication.  The copyright form can be attached as the last page of the dissertation in your pdf upload.
  • Endorsement letters.  The nomination must include an endorsement letter from the department head.  In addition, at least 3, and not more than 5, supporting letters should be included from experts in the field who can provide additional insights or evidence of the dissertation’s impact.  (The nominator/advisor may not write a letter of support.)  Each letter should include the name, address, and telephone number of the endorser.  The nominator should collect the letters and bundle them for submission.  The endorsement letter and supporting letters can be combined in one file in your pdf upload.

For questions on the above, please contact us at, or Rosemary McGuinness, ACM Awards Committee Liaison.  ACM's conflict-of-interest guidelines apply to all award nominations.

Julian Shun has won the 2015 Doctoral Dissertation Award presented by ACM for providing evidence that, with appropriate programming techniques, frameworks and algorithms, shared-memory programs can be simple, fast and scalable. In his dissertation Shared-Memory Parallelism Can Be Simple, Fast, and Scalable, he proposes new techniques for writing scalable parallel programs that run efficiently both in theory and in practice.

While parallelism is essential to achieving high performance in computing, writing efficient and scalable programs can be very difficult. Shun’s three-pronged approach to writing parallel programs that he outlines in his thesis includes: 1) proposing tools and techniques for deterministic parallel programming; 2) the introduction of Ligra, the first high-level shared-memory framework for parallel graph traversal algorithms; and 3) presenting new algorithms for a variety of important problems on graphs and strings that are both efficient in theory and practice.

Shun is a post-doctoral researcher at the University of California, Berkeley, where he was awarded a Miller Research Fellowship. He earned his Ph.D. at Carnegie Mellon University, which nominated him for the ACM Doctoral Dissertation Award. He earned a B.A. in Computer Science from the University of California, Berkeley, where he was ranked first in the 2008 graduating class of computer science students. During the 2013-2014 academic year, he was the recipient of a Facebook Graduate Fellowship.

He will receive the Doctoral Dissertation Award and its $20,000 prize at the annual ACM Awards Banquet on June 11 in San Francisco. Financial sponsorship of the award is provided by Google Inc.

Honorable Mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. They will share a $10,000 prize, with financial sponsorship provided by Google Inc.

In Sidford’s dissertation, Iterative Methods, Combinatorial Optimization, and Linear Programming Beyond the Universal Barrier, he considers the fundamental problems in continuous and combinatorial optimization that occur pervasively in practice, and shows how to improve upon the best-known theoretical running times for solving these problems across a broad range of parameters. Sidford uses and improves techniques from diverse disciplines including spectral graph theory, numerical analysis, data structures, and convex optimization to provide the first theoretical improvements in decades for multiple classic problems ranging from linear programming to linear system solving to maximum flow. Sidford is presently a postdoctoral researcher at Microsoft New England. He received a Ph.D. in Computer Science from the Massachusetts Institute of Technology, which nominated him for this award.

Mirarab’s dissertation, Novel Scalable Approaches for Multiple Sequence Alignment and Phylogenomic Reconstruction, addresses the growing need to analyze large-scale biological sequence data efficiently and accurately. To address this challenge, Mirarab introduces several methods: PASTA, a scalable and accurate algorithm that can align data sets up to one million sequences; statistical binning, a novel technique for reducing noise in estimation of evolutionary trees for individual parts of the genome; and ASTRAL, a new summary method that can run on 1,000 species in one day and has outstanding accuracy. These methods were essential in analyzing very large genomic datasets of birds and plants. Mirarab is currently an Assistant Professor of Electrical and Computer Engineering at the University of California, San Diego. He obtained a Ph.D. in Computer Science from the University of Texas at Austin, which nominated him for this award.

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