Foutse Khomh

Biographie

Foutse Khomh est professeur titulaire de génie logiciel à Polytechnique Montréal, titulaire d'une chaire en IA Canada-CIFAR dans le domaine des systèmes logiciels d'apprentissage automatique fiables, et titulaire d'une chaire de recherche FRQ-IVADO sur l'assurance qualité des logiciels pour les applications d'apprentissage automatique.

Il a obtenu un doctorat en génie logiciel de l'Université de Montréal en 2011, avec une bourse d'excellence. Il a également reçu le prix CS-Can/Info-Can du meilleur jeune chercheur en informatique en 2019. Ses recherches portent sur la maintenance et l'évolution des logiciels, l'ingénierie des systèmes d'apprentissage automatique, l'ingénierie en nuage et l’IA/apprentissage automatique fiable et digne de confiance.

Ses travaux ont été récompensés par quatre prix de l’article le plus important Most Influential Paper en dix ans et six prix du meilleur article ou de l’article exceptionnel (Best/Distinguished Paper). Il a également siégé au comité directeur de plusieurs conférences et rencontres : SANER (comme président), MSR, PROMISE, ICPC (comme président) et ICSME (en tant que vice-président). Il a initié et coorganisé le symposium Software Engineering for Machine Learning Applications (SEMLA) et la série d'ateliers Release Engineering (RELENG).

Il est cofondateur du projet CRSNG CREATE SE4AI : A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems et l'un des chercheurs principaux du projet Dependable Explainable Learning (DEEL). Il est également cofondateur de l'initiative québécoise sur l'IA digne de confiance (Confiance IA Québec). Il fait partie du comité de rédaction de plusieurs revues internationales de génie logiciel (dont IEEE Software, EMSE, JSEP) et est membre senior de l'Institute of Electrical and Electronics Engineers (IEEE).

Étudiants actuels

Nanda Assobjio Brice Yvan

Maîtrise recherche - Polytechnique

Ahmed Haj Yahmed

Maîtrise recherche - Polytechnique

Gabriel Laberge

Doctorat - Polytechnique

forough majidi

Doctorat - Polytechnique

Site web

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Maîtrise recherche - Polytechnique

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorat - Polytechnique

Co-superviseur⋅e :

Arghavan Moradi Dakhel

Postdoctorat - Polytechnique

Maîtrise recherche - Polytechnique

Doctorat - Polytechnique

Ben Braiek Yasmine

Maîtrise recherche - Polytechnique

Publications

Challenges in Machine Learning Application Development: An Industrial Experience Report

Md. Saidur Rahman

Emilio Martínez Rivera

Yann‐Gaël Guéhéneuc

Bernd Lehnert

SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their custom… (voir plus)ers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.

2022-05-01

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) (published)

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (voir plus)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (publié)

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (voir plus)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (published)

Clones in deep learning code: what, where, and why?

Hadhemi Jebnoun

Md Saidur Rahman

Biruk Asmare Muse

2022-04-08

Empirical Software Engineering (publié)

arxiv.org

Software-Engineering Design Patterns for Machine Learning Applications

Hironori Washizaki

Yann‐Gaël Guéhéneuc

Hironori Takeuchi

Naotake Natori

Takuo Doi

Satoshi Okuda

In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (voir plus) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.

2022-03-01

Computer (publié)

Software-Engineering Design Patterns for Machine Learning Applications

Hironori Washizaki