About Me

I’m a PhD student in Artificial Intelligence, deeply passionate about AI. My research focuses on semantic SLAM, developing a new SLAM approach optimized for embedded systems. I’m always looking for ways to push the boundaries of AI in robotics.

AI & Research

I take on many AI projects to improve my skills in machine learning and computer vision.

Ants Enthusiast

Ant colonies fascinate me—their organization and problem-solving abilities even inspire AI algorithms!

Tea Lover

Exploring different teas and cultures is my way to relax and think (about coding...).

Publications

  1. Optimizing Vision Transformers for Edge Deployment: Hybrid Token Reduction for Efficient Semantic Segmentation

    [EEAI] European Conference on EDGE AI Technologies and Applications

    Mathilde Proust, Martyna Poreba, Calvin Galagain, Michal Szczepanski, Karim Haroun

  2. Challenges and Performance of SLAM Algorithms on Resource-Constrained Devices

    [EEAI] European Conference on EDGE AI Technologies and Applications

    Calvin Galagain, Martyna Poreba, François Goulette

Education

  1. PhD student – CEA & ENSTA Paris

    Since 10/2023

    PhD candidate in artificial intelligence and robotics, working on SLAM algorithms in dynamic environments. My research focuses on improving the management of moving objects in SLAM systems by using semantic segmentation to filter these objects. The goal is to develop solutions optimized for embedded systems with low computational power, ensuring real-time execution.

  2. M2 MVA (Mathématiques, Vision, Apprentissage)

    10/2022 - 07/2023

    The MVA master's program is one of the most renowned artificial intelligence programs in France. I worked on numerous topics related to both the theory and applications of deep learning, including computer vision, natural language processing (NLP), and machine learning.

  3. ESIEE Paris

    09/2020 - 07/2023

    ESIEE Paris is an engineering school where I specialized in artificial intelligence, data science, and electronics. I developed expertise in machine learning, deep learning, and statistical modeling while also gaining solid knowledge in embedded systems.

  4. Saint Stanislas

    09/2018 - 07/2020

    This was an intensive preparatory program for France’s engineering schools. I studied advanced mathematics and physics in great detail, which allowed me to develop strong problem-solving and analytical skills.

  5. Baccalauréat S

    2018

    Maths & Physics specialization.

Teaching

  1. IA307: Programming with GPU for Deep Learning

    2025 - Télécom Paris

    This course provides an overview of GPU programming techniques for deep learning, covering multi-threading, GPU architectures, the backpropagation algorithm, and practical implementation.

  2. IN102: Introduction to C Programming

    2024 - ENSTA Paris

    Course covering the fundamentals of C programming, including memory management, pointers, data structures, and algorithmic problem-solving.

  3. IA101: Artificial Intelligence and Data

    2024 - ENSTA Paris

    Introduction to artificial intelligence concepts, covering supervised and unsupervised learning, fundamental ML models, and data-driven decision-making.

  4. NPM3D: 3D Point Clouds

    2024 & 2025 - M2 MVA

    Course on 3D point cloud processing, including acquisition methods, segmentation, registration, and deep learning-based approaches for 3D scene understanding.

Experience

  1. Exwayz – AI Research Intern

    04/2023 - 10/2023

    Developed a real-time object detection and tracking system using 3D LiDAR data. Worked on SLAM algorithms and SLAM-GPS fusion while managing data anisotropy to improve segmentation efficiency without loss of accuracy. Implemented multi-object tracking with dynamic bounding box estimation.

  2. ZRD – AI Research Intern

    05/2022 - 09/2022

    Research internship at the Zentrum für Recht und Digitalisierung in Saarbrücken, focusing on legal implications of AI. Developed a system for automatic real-time video analysis to detect sensitive content in collaboration with the Prosecutor’s Office. Explored adversarial learning techniques to assess vulnerabilities in deep neural networks.

  3. IRCGN – Research Project

    11/2021 - 09/2022

    Conducted research on Deepfake detection and face recognition for forensic applications. Worked on facial resemblance estimation between suspects and police video footage, implementing deep learning models for landmark detection, data augmentation, and deepfake decoding from scratch. Gained critical insight into the research process and peer evaluation methodologies.

Feb, 2024

Myrmeciinae

This project is a sports application that tracks strength training performance while also serving as a social network.

in coming ...
Feb, 2024

Formicinae

This project involves rebuilding a SLAM system from scratch in C++ for localization and mapping.

in coming ...
Feb, 2024

Dolichoderinae

This project focuses on visualizing and generating a state-of-the-art review based on research domains.

in coming ...
Feb, 2024

Aneuretinae

This project aims to minimize the total cost of purchasing Pokemon cards by optimizing both card prices and shipping costs across multiple vendors.

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