Messi Nguelé: Efficient Complex System Analysis on Parallel Architectures with Ethical ML

Register for online participation: https://events.teams.microsoft.com/event/44da0e6b-a826-4719-8699-eaed44465b34@92454335-564e-4ccf-b0b0-24445b8c03f7

Complex systems are systems composed of many components in interactions and usually generates a large amount. Many complex systems analysis applications are using machine learning algorithms [1,2]. Once trained, the models built by machine learning algorithms can be used to solve many problems related to regression, classification, or clustering. For better inference, models should be trained on large amounts of data; but the greater the amount of data for training, the higher the execution time of the algorithms. The advent of parallel architectures (multi/many-core, GPU) makes it possible to reduce this execution time. However, using them efficiently require to re-think traditional sequential machine learning algorithms into parallel ones. As multi-core are more and more common, we want to fully exploit them to make complex systems applications to be faster. In this project, we want to parallelize machine learning algorithms that are intrinsically (by definition) sequential. Our goal is to take advantage of parallel architectures that are available even to common user, in order to produce the most efficient machine learning models with the shortest training time. As results, we expect to have: - Parallel model for machine learning algorithms; - A DSLs for machine learning algorithms parallelization; -Algorithms or heuristics that make complex system applications faster. Currently, we have preliminary results obtained with multi-core in the context of AI4D Africa project. This project is its extension. It will be done by integrating ethical considerations and seeing if it alters results in terms of execution time or performance metric.


About the speaker: Dr Messi Nguelé Thomas defended his PhD in Computer Science in 2018 in cotutelle between University Grenoble Alpes and University of Yaoundé I. He is now a Senior Lecturer in Computer Science at the Faculty of Sciences of the University of Yaoundé I. He is Head of the Department of Computer Engineering at ESTLC of the University of Ebolowa. He is the Head of the Computer Science and Applications Laboratory at Ambam. He mainly teaches programming courses (C, Python) and distributed learning courses on parallel architectures. His research work mainly focuses on the parallelization of machine learning algorithms.

He is a recipient of the Africom Award Mobily for Staff (July 2022) and a recipient of the ACTS AI4D Africa funding (August 2022 – August 2024). He was a visiting researcher at University of Grenoble (September 2023 and February 2025). He is affiliated with the Idasco research team (Data Sciences and Complex Systems), in its HiPerDas (High Performance Data Science) branch. He is the President of the ARITeD research association. He is the Education Officer of the Cameroon Computer Science Society and Treasurer of the Cameroon Artificial Intelligence Society.

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