Presentation at CRI’2025 Conference
Presentation of our work on fairness auditing in dermatological AI systems
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Presentation at CGCA-02 Conference 2025
Presentation of our work on ML for sustainable materials optimization at the International CGCA-02
Beautiful cathedral in Clermont-Ferrand :)
I like picture and exploration 
publications
Data Version Control (DVC) for Machine Learning: How to Version Your Data Like Code
Published in Medium, 2024
A practical guide to using Data Version Control (DVC) for managing datasets and ML experiments with the same rigor as code versioning.
MLOps Explained: A Deep Dive into Machine Learning Operations
Published in Medium, 2024
A comprehensive guide to MLOps practices, exploring how to operationalize machine learning models in production environments.
Machine learning and deep learning for the prediction of thermal comfort based on a sustainable porous matrix: A bibliometrix analysis
Published in Energy Efficiency (Under Review), 2025
A comprehensive bibliometrix analysis examining the application of machine learning and deep learning techniques for predicting thermal comfort in sustainable porous materials.
Recommended citation: quot;Machine learning and deep learning for the prediction of thermal comfort based on a sustainable porous matrix: A bibliometrix analysis." Energy Efficiency. Under review.
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Machine learning for the optimization of porosity-hygroscopy correlations of porous geopolymers in indoor thermal comfort: A hybrid feature selection approach
Published in Open Ceramics, 2025
This paper presents a hybrid feature selection approach using machine learning to optimize porosity-hygroscopy correlations in porous geopolymers for improved indoor thermal comfort.
Recommended citation: L. Tiogning-Djiogue, H. Tcheneghon Motcheyo, E. Kamseu, et al. (2025). "Machine learning for the optimization of porosity-hygroscopy correlations of porous geopolymers in indoor thermal comfort: A hybrid feature selection approach." Open Ceramics.
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Still Malignant if You Were a Woman? Auditing Group and Counterfactual Fairness in Dermatological AI
Published in CRI 2025 Conference, 2025
This paper audits group and counterfactual fairness in dermatological AI systems, examining how diagnostic accuracy varies across demographic groups and under counterfactual interventions.
Accélération de l’extraction des motifs graduels fréquents basée sur la réduction dimensionnalité
Published in EGC 2026, 2026
Dans cet étude, nous proposons l’utilisation de l’analyse en composantes principales (ACP) comme méthode de prétraitement afin d’accélérer la génération des motifs graduels.
Publications
Published in , 1900
talks
Still Malignant if You Were a Woman? Auditing Group and Counterfactual Fairness in Dermatological AI
Published:
Presented our research on auditing fairness in dermatological AI systems, focusing on the intersection of group fairness and counterfactual fairness in clinical decision support. This work, co-authored with Lauraine Tiogning-Djiogue, examined how AI diagnostic systems may produce disparate outcomes across demographic groups, particularly regarding skin cancer detection across gender and skin tone.
teaching
Teaching Assistant - Machine Learning and Database Theory
Undergraduate course, National Advanced School of Engineering of Yaoundé, 2023
Teaching Assistant position under the supervision of Dr. Lauraine Tiogning, where I mentored students in core concepts of machine learning and database theory.