Gabin (Maxime) N.
Doctoral Research Assistant and Teaching Assistant
Experience
Apr 2024 - Sep 2025
1 year 6 monthsMunich, Germany
Doctoral Research Assistant and Teaching Assistant
Ludwig Maximilian University of Munich
- Contributed to developing an ML-based anomaly detection framework that balances accuracy, interpretability, and fairness, enabling explainable AI solutions in industrial power management
- Analyzed the convergence behavior of deep learning optimizers, establishing optimal parameter initialization strategies and both decreasing and adaptive learning rate schedules for stochastic gradient descent
- Contributed in improving the state of the art in results on the mathematical foundations of deep learning, offering guidance to efficiently train deep neural networks
- Leveraged high-performance computing sources to train deep neural networks and validated theoretical findings
- Conducted tutorial classes on mathematical foundations of machine learning, deep learning and optimization, supporting the training of emerging data scientists
- Monitored and synchronized project changes with Git, maintaining local and GitHub repositories
Sep 2019 - Mar 2024
4 years 7 monthsAachen, Germany
Doctoral Research Assistant and Teaching Assistant
RWTH Aachen University
- Conducted research on gradient based methods for learning deep neural networks under both constant and decreasing learning rates
- Established the global convergence of gradient descent algorithm for learning linear neural networks, contributing to understanding of optimization techniques in deep learning and supporting effective model development
- Validated theoretical results by training deep neural networks on synthetic data
- Conducted tutorial classes on continuous optimization, mathematics of data science and higher mathematics for electrical engineering
Jun 2018 - May 2019
1 yearLimbe, Cameroon
Machine Learning Intern
Group One Holding Company
- Applied machine learning techniques to detect anomalies in fuel consumption data, optimize consumption, and limit fuel pilferage in telecommunication base stations
- Trained ML models, including neural networks, with the potential to secure up to 84617 liters of fuel
- Deployed predictive machine learning models into a web application which facilitate visualization and monitoring of fuel consumption at each base station monthly
- Maintained consistent and secure code history by synchronizing local and remote repositories with Git
Summary
Researcher in the field of applied mathematics, with over 6 years of experience in machine learning (ML), deep learning, data science and optimization. Proficient in analyzing and optimizing advanced algorithms for deep neural networks, contributing to efficient model training. Experience in translating complex theoretical insights into practical decision-making, with successful ML applications in fuel consumption prediction and anomaly detection in telecom base stations, and enhancing fairness and performance in explainable AI for power generation plants.
Languages
French
NativeEnglish
AdvancedGerman
IntermediateEducation
Sep 2019 - Sep 2025
Ludwig Maximilian University of Munich and RWTH Aachen University
PhD in Applied Mathematics, Optimization for Training Deep Neural Networks · Applied Mathematics · Aachen, Germany
Aug 2017 - Feb 2019
African Institute for Mathematical Sciences
MSc in Industrial Mathematics, Machine Learning · Industrial Mathematics · Limbe, Cameroon
Oct 2016 - Nov 2018
University of Yaounde I
MSc in Applied Mathematics, Dynamics systems and modeling · Applied Mathematics · Yaoundé, Cameroon
...and 2 more
Certifications & licenses
Introduction To Container Technology & Application To AI
Model Parallelism: Building And Deploying Large Neural Networks
Generative AI With Diffusion Models
Generative AI: Elevate Your Data Science Career
Generative AI With Large Language Models
Machine Learning Engineering For Production (MLOps) Specialization
Structuring Machine Learning Projects
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