Phd Position F/m Workflow Provenance and Its
Il y a 6 mois
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Autre diplôme apprécié **:Master's degree
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria Centre at Rennes University is one of Inria's eight centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.
**Contexte et atouts du poste**:
**Supervisory **Team**:
- Silvina Caino-Lores, PhD (Inria, France)
- Alexandru Costan, PhD, HDR (INSA Rennes, France)
- Renan Souza, PhD (Oak Ridge National Laboratory, USA)
- Rafael Ferreira da Silva, PhD (Oak Ridge National Laboratory, USA)
**Location and Mobility**:
The thesis will be hosted by the KerData team at the Inria research center of Rennes. Rennes is the capital city of Britanny, in the western part of France. It is easy to reach thanks to the high-speed train line to Paris. Rennes is a dynamic, lively city and a major center for higher education and research: 25% of its population are students.
This thesis will include collaborations with international partners from the USA, thus research visits to and from the collaborator's teams are expected.
- KerData is a human-sized team currently comprising 5 permanent researchers, 2 contract researchers, 1 engineer and 6 PhD students. You will work in a caring environment, offering a good work-life balance.
**Mission confiée**:
**Context and Overview**:
Recent works suggest that workflow provenance (i.e., the documentation and tracking of all processes within AI development) might hold the key to supporting FAIR and Responsible AI [SAL+22,KNHJ+23]. Workflow provenance refers to capturing detailed information about all activities, processes, and transformations applied to data and code during AI development and operations. It includes information about data sources, data preprocessing, model selection, hyperparameter tuning, and evaluation metrics, among others. Capturing this provenance could provide a holistic view of the AI workflow, making it transparent and reproducible. However, a challenging aspect of working
with AI workflows is that today there are no comprehensive formalisms able to capture the complexity and relationships in workflow and model provenance data [BBFM23]. Furthermore, multiple technical challenges arise when attempting to capture, store and manage the full provenance of AI workflows [MCSAGBS21, SS23], and it is still not well understood what information is valuable and how it can be leveraged in the support of AI transparency and explainability [JRO+20].
**Research Objectives**:
- Aim C, that develops a methodology to elucidate the connections between the formalized provenance metadata and model behavior, assessing how these elements influence model performance and interpretability. The methodology aims to evaluate the transparency and explainability in practical open-source AI models, including foundation models, in order to find links between the provenance metadata and their architectural and behavioral traits. The anticipated result of the endeavor in Aim C is a methodological framework leveraging provenance matadata taxonomies, the causal model between the studied models and their behavior, and associated statistical findings that support transparency and explainability in practice.
**Principales activités**:
**Envisioned Approach**:
To explore what AI workflow provenance metadata can tell us about AI transparency and explainability, we will build upon previous work and active research of the members in the supervisory team in the USA and France.
For Aim A, the research methodology centers on analysing the AI model life-cycle, enabling technologies and infrastructures, using our previous work on taxonomies for neural network metadata [RCLJT22] as a starting point. We described the structure of a neural network with an architectural taxonomy capturing the number, type (e.g., convolution or pooling), shape, and order of layers; and the hyperparameters associated with each layer (e.g., kernel, stride, and padding for convolutional or pooling layers). The architectural taxonomy is independent of the data, thus allowing for comparison across datasets. We also defined a behavioral taxonomy throughout training covering the training parameters (e.g., learning rate and batch size); the criterion used for gain or loss; the method used for training and the measurement used for fitness; and the type of learning curve including, for example, designations of late-learners and never-learners. Following a similar approach, we aim to formalize similar taxonomies for other machine learning methods. This work wi
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