[agents] AAMAS2022 : Call for 2021 -Victor Lesser- Distinguished Dissertation Award

brian ravenet brian.ravenet at limsi.fr
Tue Oct 26 09:51:18 EDT 2021


IFAAMAS, the International Foundation for Autonomous Agents and Multiagent Systems (http://www.ifaamas.org)  is pleased to announce the call for 2021 Victor Lesser Distinguished
Dissertation Award. Nominations are invited for the award which is sponsored by IFAAMAS and will be presented at AAMAS-2022 (https://aamas2022-conference.auckland.ac.nz).
  
Eligible doctoral dissertations are those defended between January 1, 2021 and November 1, 2021 in the area of Autonomous Agents or Multiagent Systems.
  
This award includes a certificate and a 1500 EUR payment.
  
The selection of the dissertation will be based on the originality, significance, and impact of the work. Evidence of such impact include publications at highly selective conferences and journals in
the field, with due importance given to the AAMAS conference series and JAAMAS. Research output that resulted primarily from the student's initiative will be considered more favourably. The
selection committee will be the final arbiter in the decision process. The selection committee might decide to consult external assessors and reserves the right to not award the prize if the
nominations do not meet the expected quality level.
  
The dissertation must be nominated by the thesis supervisor and must be supported by the following documents, all should be delivered via the link below before or on November 25,  2021:


A link to a PDF file of the dissertation. If the dissertation is not written in English, the nomination must include an accessible link to a substantial manuscript in English, with the nominee as the first author, published in a peer-reviewed journal or conference.


A PDF that contains a list of publications that have arisen from the dissertation, with links to the published papers.


A recommendation from the dissertation supervisor, on departmental letterhead, nominating the dissertation for the IFAAMAS-20 Victor Lesser Distinguished Dissertation Award. The recommendation should explain the contribution of the dissertation to the field of autonomous agents and multiagent systems,  argue the merit and possible future impact of the work,  and highlight, where relevant, how the work resulted from the initiative of the student.  Finally, this document should certify the eligibility of the PhD by asserting that the PhD was defended successfully between January 1, 2021 and November 1, 2021.


The names, email addresses, and affiliations of at least one and at most three referees, familiar with the research of the candidate and experts in the pertinent research area, who will directly email their recommendations for the candidate to the chair of the selection committee (Kate Larson (kate.larson at uwaterloo.ca), University of Waterloo). A reference letter should be no more than 500 words in length and should be on official letterhead, signed and emailed as a  PDF file.
  
NOTE: IT IS THE RESPONSIBILITY OF THE DISSERTATION SUPERVISOR TO CONTACT THE REFEREES AND ENSURE THAT LETTERS (max 500 words, signed, and on letterhead) ARE SUBMITTED BY THE DEADLINE.
  
Though the nomination is to be submitted by the nominee's dissertation supervisor, it is required that the nominee has consented that the dissertation be considered for this award and, if selected for the award, commits to attend the AAMAS-2022 conference, where he/she will receive the award and will give a presentation in a special session of the conference on the work contained in the dissertation. The cost of attending the conference is not covered by the award.
  
Submission link: https://forms.gle/sxpxRoGWNBUbMMYx7
  
Submission deadline: Nov 25, 2021
  
For questions, please contact the chair of the selection committee, Kate Larson, University of Waterloo (kate.larson at uwaterloo.ca).

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://lists.cs.umbc.edu/pipermail/agents/attachments/20211026/61e20cc7/attachment-0001.html>


More information about the agents mailing list