Gabin (Maxime) Nguegnang
Freelance Mathematics Expert for AI Model Training
Experience
Freelance Mathematics Expert for AI Model Training
Outlier AI and Mindrift AI
- Trained AI models to address specialized real-world problems
- Designed research oriented prompts grounded in applied mathematics, and developed rubrics criteria that consistently improve model reasoning and output quality
- Assessed the performance, accuracy, and reliability of advanced AI models
- Collaborated closely with cross-functional development teams to ensure AI models meet industry standards and provided actionable insights
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 stochastic gradient descent in deep learning, establishing optimal conditions for parameter initialization and both decreasing and adaptive learning rate schedules
- Improved the state of the art results on the mathematical foundations of deep learning, by bridging the gap between theory and practice to enable more efficient training strategies
- 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
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
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 facilitates 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
Industries Experience
See where this freelancer has spent most of their professional time. Longer bars indicate deeper hands-on experience, while shorter ones reflect targeted or project-based work.
Experienced in Education (6 years), Energy (2.5 years), Telecommunication (1 year), and Information Technology (0.5 years).
Business Areas Experience
The graph below provides a cumulative view of the freelancer's experience across multiple business areas, calculated from completed and active engagements. It highlights the areas where the freelancer has most frequently contributed to planning, execution, and delivery of business outcomes.
Experienced in Research and Development (6.5 years), Product Development (2 years), Business Intelligence (1 year), Information Technology (1 year), and Quality Assurance (0.5 years).
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 gradient based methods 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.
Skills
- Programming Languages: Python (Proficient), Matlab (Proficient)
- Experiments: Cuda, High Performance Computing (Hpc), Parallel Programming
- Data Science & Machine Learning: Pytorch, Scikit-learn, Containers (Docker, Enroot), Ml Workflows, Generative Ai, Llms, Hugging Face Libraries, Project Scoping And Design, Pandas, Transformers
- Cloud Technologies: Amazon Web Services, Google Cloud Vertex Ai
- Collaborative Mindset And Cross-functional Teamwork
- Problem-solving And Adaptability To New Technologies
- Conceptual And Analytical Thinking
- High Sense Of Responsibility And Integrity
- Mentoring And Knowledge Transfer
- Effective Scientific And Technical Communication
Languages
Education
Ludwig Maximilian University of Munich and RWTH Aachen University
PhD, Optimization for Training Deep Neural Networks · Applied Mathematics · Aachen, Germany
African Institute for Mathematical Sciences (AIMS)
Co-operative MSc, Machine Learning · Industrial Mathematics · Limbe, Cameroon
University of Yaounde I
MSc, Dynamics systems and modeling · Applied Mathematics · Yaoundé, Cameroon
Certifications & licenses
Model Parallelism: Building And Deploying Large Neural Networks
Generative AI With Diffusion Models
Generative AI With Large Language Models
Machine Learning Engineering For Production (MLOps) Specialization
Structuring Machine Learning Projects
Profile
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