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yqtian-se merged 2 commits into from
Jun 5, 2026
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Update SIGSOFT liaison contacts #140

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Update SIGSOFT liaison contacts
yqtian-se Jun 5, 2026
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fix a grammar issue
yqtian-se Jun 5, 2026
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29 changes: 12 additions & 17 deletions content/execcontact.md
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Expand Up @@ -85,30 +85,25 @@ title = "SIGSOFT Executive Committee"
University of Hawaii, USA
kazman@hawaii.edu

#### Liaisons for Diversity, Equity, and Inclusion: Jo Atlee
#### Liaisons for Diversity, Equity, and Inclusion: Federica Sarro

University of Waterloo
Canada
jmatlee@uwaterloo.ca
University College London, United Kingdom
f.sarro@ucl.ac.uk

#### Liaisons for Diversity, Equity, and Inclusion: Kelly Blincoe
#### Liaisons for Diversity, Equity, and Inclusion: Foutse Khomh

University of Auckland
New Zealand
k.blincoe@auckland.ac.nz
Polytechnique Montréal, Canada
foutse.khomh@polymtl.ca

#### Liaisons for Diversity, Equity, and Inclusion: Alexander Serebrenik
#### Liaisons for Diversity, Equity, and Inclusion: Birgit Penzenstadler

Eindhoven University of Technology
Netherlands
a.serebrenik@tue.nl
Chalmers, Gothenburg University and Lappeenranta University
birgitp@chalmers.se

#### Research Highlight Chair: Martin Robillard
#### Research Highlights Chair: Silvia Abrahão

McGill University
Montreal, Quebec
Canada
https://www.cs.mcgill.ca/~martin/
Universitat Politècnica de València
sabrahao@dsic.upv.es

#### Social Media Chair: Judith Michael

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4 changes: 2 additions & 2 deletions content/researchhighlight.md
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Expand Up @@ -22,7 +22,7 @@ _By submitting a nomination statement, nominators authorize the SIGSOFT Research
SIGSOFT Research Highlights Committee
-------------------------------------

* [Martin Robillard](https://www.cs.mcgill.ca/~martin/), McGill University (Chair)
* [Silvia Abrahão](mailto:sabrahao@dsic.upv.es), Universitat Politècnica de València (Chair)
* [Nicole Novielli](http://collab.di.uniba.it/nicole/), University of Bari
* [Michael Pradel](https://software-lab.org/people/Michael_Pradel.html), University of Stuttgart
* [Saurabh Sinha](https://researcher.watson.ibm.com/researcher/view.php?person=us-sinhas), IBM Research
Expand Down Expand Up @@ -78,4 +78,4 @@ P. Zhang, J. Wang, J. Sun, G. Dong, X. Wang, X. Wang, J.S. Dong, and T. Dai

**Venue:** ICSE 2020

**Nomination Statement:** Deep neural networks (DNNs) have demonstrated their effectiveness in multiple important application contexts, from face recognition, to medical diagnosis, fraud detection, and others. Especially when DNNs work with human-related characteristics, it is of paramount importance to ensure that they show fair behavior. However, because of societal bias often occurring in the training data, the resulting DNNs may introduce discrimination unintentionally. To address this problem, the paper proposes a scalable approach for generating individual discriminatory instances of DNNs. By generating several instances, it is possible to retrain a DNN to reduce discrimination. The approach is evaluated by comparing it with other two from the state of the art. The evaluation is performed on three significant datasets and shows a more effective search space exploration as well as the possibility to generate a larger number of individual discriminatory instances using significant less time. This paper provides a contribution that is cross-cutting two disciplines, software engineering and machine learning, and paves the way toward improving the quality of DNNs and their usability in societal contexts.
**Nomination Statement:** Deep neural networks (DNNs) have demonstrated their effectiveness in multiple important application contexts, from face recognition, to medical diagnosis, fraud detection, and others. Especially when DNNs work with human-related characteristics, it is of paramount importance to ensure that they show fair behavior. However, because of societal bias often occurring in the training data, the resulting DNNs may introduce discrimination unintentionally. To address this problem, the paper proposes a scalable approach for generating individual discriminatory instances of DNNs. By generating several instances, it is possible to retrain a DNN to reduce discrimination. The approach is evaluated by comparing it with other two from the state of the art. The evaluation is performed on three significant datasets and shows a more effective search space exploration as well as the possibility to generate a larger number of individual discriminatory instances using significantly less time. This paper provides a contribution that is cross-cutting two disciplines, software engineering and machine learning, and paves the way toward improving the quality of DNNs and their usability in societal contexts.
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