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Optimize the size and performance of AI models for low capacity devices

a. Project Description

Machine Learning and Artificial Intelligence in general slowly become an integral part in the majority of software systems and applications. In combination with the advancements in pervasive and ubiquitous computing, it is now implied that AI models need to be deployable on any device. However, it is not clear yet what the implications are of low capacity devices, such as mobile phones, on the deployment, performance and accuracy of AI models. In this project, we aim to firstly explore these implications and quantify them, in order to guide the optimization of AI models in terms of resource consumption and constrained by requirements on their performance and accuracy. Next, we will integrate these considerations in MLOps pipelines to automate the optimization and make it an integral part of the model’s lifecycle. The expected outcome of the project is the development of models to simultaneously capture the aspect of the AI models as software artifacts (performance and resource consumption) and as AI artifacts (accuracy). Based on these models, we will also develop tools for the optimal and adaptive deployment of AI models on low capacity devices.

b. Tasks and responsibilities

The hired student will work towards the review of MLOps technologies and tools with a focus on the deployment of AI models on IoT and other low capacity devices, as well as a review of the state-of-practice on adaptive AI model deployment and operations. The student will develop the theoretical foundation as well as practical experience on the optimization of MLOps pipelines. The student will aim to publish in top-tier journals, including IEEE Transactions on Software Engineering, Elsevier Journal of Systems and Software, and conferences, such as ICPE, SEAMS, ACSOS, ICSE, and others. The student will also be responsible for supervising and mentoring MSc and BSc students working on the project. The position is open for Winter, Summer or Fall 2024.

c. Required Skills

The student will be asked to demonstrate adequate understanding or expertise in the following topics through relevant courses (on undergraduate or graduate level) or through relevant publications in international conferences or journals. The student should consider applying if they have the expert-level skills and at least 50% of the good-level skills.

d. Application process

Upon contacting the professor to inquire for the position, the student is also asked to submit the following documents: