I'm Aymane Lakouiss, a PhD candidate in artificial intelligence applied to image analysis and a Research Engineer specializing in deep learning and computer vision. I trained at ENSEIRB-MMATMECA in signal processing, image processing, and AI, and hold a Master’s in Systems Engineering for Signal and Image Processing. My focus is on developing AI-powered image analysis methods for cosmetic applications and imaging science. My work spans CNN-based segmentation and classification, data-driven analysis, and translating results into actionable insights for cosmetic treatment assessment. I enjoy solving complex problems, collaborating across disciplines, and applying rigorous mathematics and engineering to real-world vision challenges.…

Aymane Lakouiss

I'm Aymane Lakouiss, a PhD candidate in artificial intelligence applied to image analysis and a Research Engineer specializing in deep learning and computer vision. I trained at ENSEIRB-MMATMECA in signal processing, image processing, and AI, and hold a Master’s in Systems Engineering for Signal and Image Processing. My focus is on developing AI-powered image analysis methods for cosmetic applications and imaging science. My work spans CNN-based segmentation and classification, data-driven analysis, and translating results into actionable insights for cosmetic treatment assessment. I enjoy solving complex problems, collaborating across disciplines, and applying rigorous mathematics and engineering to real-world vision challenges.…

Available to hire

I’m Aymane Lakouiss, a PhD candidate in artificial intelligence applied to image analysis and a Research Engineer specializing in deep learning and computer vision. I trained at ENSEIRB-MMATMECA in signal processing, image processing, and AI, and hold a Master’s in Systems Engineering for Signal and Image Processing. My focus is on developing AI-powered image analysis methods for cosmetic applications and imaging science.

My work spans CNN-based segmentation and classification, data-driven analysis, and translating results into actionable insights for cosmetic treatment assessment. I enjoy solving complex problems, collaborating across disciplines, and applying rigorous mathematics and engineering to real-world vision challenges.

See more

Language

French
Fluent
English
Advanced
Arabic
Fluent
German
Beginner

Work Experience

PhD Researcher, Deep Learning & Mathematics at Institut Denis Poisson
December 1, 2025 - December 1, 2028
Development of AI-based cosmetic image analysis methods to automate evaluation of facial youth-related characteristics. Design and adaptation of deep learning models (segmentation and classification) based on CNN architectures to improve accuracy and robustness. Exploitation of analysis results to assess effectiveness of cosmetic treatments and study correlations between youth indicators and treatment types.
Internship: Deep Learning & Computer Vision Engineer at Orange Innovation
March 1, 2025 - August 1, 2025
Improvement of a deep learning model for automatic extraction of walls, doors, and windows from architectural floor plan images. Optimization of model architecture, hyperparameters, and data augmentation techniques to enhance accuracy. Development of post-processing methods to refine vectorization, including point realignment and segment merging.
Internship: Machine Learning & Computer Vision Engineer at HANDDDLE SAS
March 1, 2024 - August 1, 2024
Design and development of an AI and computer vision solution for industrial data processing. Implementation of approaches to extract relevant information from production videos, combining mathematical methods and artificial intelligence to analyze and structure the data.
Project Internship (PFA) at CORDOUAN TECH
June 1, 2023 - September 1, 2023
Development of FPGA-based embedded software for digital signal data acquisition and processing, including output generation and formatting.
PhD Researcher: Deep Learning & Mathematics at Institut Denis Poisson
December 1, 2025 - December 1, 2028
Development of artificial intelligence–based cosmetic image analysis methods to automate the evaluation of facial youth-related characteristics. Design and adaptation of deep learning models (segmentation and classification) based on CNN architectures to improve the accuracy and robustness of the analyses. Exploitation of analysis results to assess the effectiveness of cosmetic treatments and to study correlations between youth indicators and treatment types.

Education

PhD at University of Orléans
January 1, 2025 - January 1, 2028
Engineering Degree at ENSEIRB-MATMECA
January 1, 2021 - January 1, 2025
Master’s Degree (ISC) at University of Bordeaux
January 1, 2023 - January 1, 2024
CPGE MP* at LYDEX
January 1, 2019 - January 1, 2021
PhD Thesis at University of Orléans
January 1, 2025 - January 1, 2028
Engineering Degree at ENSEIRB-MATMECA
January 1, 2021 - January 1, 2025
Master’s Degree (ISC) at University of Bordeaux
January 1, 2023 - January 1, 2024
CPGE MP* at LYDEX
January 1, 2019 - January 1, 2021

Qualifications

Add your qualifications or awards here.

Industry Experience

Computers & Electronics, Software & Internet, Education, Media & Entertainment, Professional Services
    paper Deep learning for cosmetic images: effect of cosmetic products on skin

    ⋄ Development of artificial intelligence–based cosmetic image analysis
    methods to automate the evaluation of facial youth-related characteristics.
    ⋄ Design and adaptation of deep learning models (segmentation and classification) based on CNN architectures to improve the accuracy and robustness
    of the analyses.
    ⋄ Exploitation of analysis results to assess the effectiveness of cosmetic
    treatments and to study correlations between youth indicators and treatment
    types.

    paper Deep learning for box internet position

    ⋄ Improvement of a deep learning model for the automatic extraction of
    walls, doors, and windows from architectural floor plan images.
    ⋄ Optimization of model architecture, hyperparameters, and data augmentation techniques to enhance accuracy.
    ⋄ Development of post-processing methods to refine vectorization, including
    point realignment and segment merging.