Mahmoud Aslan
Deep Learning Engineer at Agile Robots SE
Munich, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolMahmoud Aslan is a Deep Learning Engineer based in Munich with 11 years of software experience and over three years focused on building containerized ML microservices and end-to-end pipelines using PyTorch, MLflow, Spark and FastAPI/Django. He bridges research and production—contributing to a 4k-line research codebase at Eötvös Loránd University, running 500+ experiments on adversarial robustness and sparse coding, and shipping deployable microservices for NLP and vision tasks. Mahmoud combines rigorous optimization work (FISTA, dictionary learning, adversarial training, weight pruning) with practical MLOps skills including Singularity/Docker, CI/CD thinking and Kubernetes/Seldon Core exploration. He also led data-collection efforts for underrepresented Arabic NLP, producing a 10k-article dataset and an 88% accuracy classifier deployed as a RESTful service. An active collaborator, he has corrected and improved training code in well-known deep learning teaching repos, reflecting attention to reproducibility and model correctness.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Computer Science for Autonomous Systems, 4.93/5.0 (Excellent with honors), Master of Science - MS, Computer Science for Autonomous Systems, 4.93/5.0 (Excellent with honors) at Eötvös Loránd UniversityBachelor's degree, Informatics Engineering, Software Engineering specialisation, 81.437% (Very Good), Bachelor's degree, Informatics Engineering, Software Engineering specialisation, 81.437% (Very Good) at Al-Baath University
Arabic, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
neural-network10
keras10
machine-learning10
deep-learning10
tensorflow10
python9
convolutional-neural-networks7
Programming languages (6)
Github contributions (5)
jeffheaton/t81_558_deep_learning
Mar 2018 - Mar 2018
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 2 PRs, 1 issue in 2 days
Contributions summary:Mahmoud primarily corrected parameters used in the `model.fit()` function within several Jupyter Notebook files. These corrections ensured the training process used the appropriate training data (x_train, y_train) and validation data (x_test, y_test) when training deep learning models. The user's changes are focused on improving the accuracy and correctness of the model training process across multiple notebooks relating to various deep learning topics. The corrected parameters impact multiple notebooks, directly affecting the training process.
deep-learningst-louisneural-networksmachine-learninggan
mhmoodlan/mhmoodlan.github.io
Jul 2015 - Jan 2019
Contributions:2 PRs, 34 pushes, 2 branches in 3 years 7 months
uploadingjavapythonmy-projects
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