Ahmed Elnaggar
Principal Research Scientist at Cambridge University Press
Greater Munich Metropolitan Area Germany
Summary
🤩
Rockstar🎓
Top SchoolAhmed Elnaggar is a Principal Research Scientist based in Greater Munich with 10+ years building and deploying large-scale AI systems across industry and academia. At Johnson & Johnson he translates cutting-edge LLM and multimodal research into trustworthy, uncertainty-aware agents for drug discovery and long-horizon decision making. A Google Developer Expert in AI and Cloud, he combines hands-on engineering—contributions to Hugging Face Transformers and the ProtTrans protein language models—with scientific leadership and mentorship. He’s equally active in developer education and community events, organizing meetups and Google DevFest workshops to democratize ML knowledge. Notably, his open-source work improved T5 components, TPU sharding, and protein embedding visualizations, reflecting a rare blend of deep model expertise and production-focused optimization. Outside core research he mentors emerging talent and champions AI applications that advance healthcare, science, and accessibility.
10 years of coding experience
11 years of employment as a software developer
Full Stack Web Development Certification Computer Software Engineering, Full Stack Web Development Certification Computer Software Engineering at Free Code CampBachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at University of the District of ColumbiaDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Technical University of MunichBachelor of Science (BSc) Computer Science, Bachelor of Science (BSc) Computer Science at Modern Academy in MaadiMaster of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at Arab Academy for Science, Technology and Maritime Transport
English, German, Arabic
Github Skills (14)
pytorch10
transformer10
machine-learning10
nlp10
language-model10
xnet10
tensorflow10
deep-learning10
python10
bert10
flax9
jax9
parallelization9
tpu9
Programming languages (7)
Github contributions (5)
agemagician/ProtTrans
May 2020 - Aug 2021
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 200 commits, 117 pushes in 1 year 3 months
Contributions summary:Ahmed's commits demonstrate the addition of Bert feature extraction and Albert, and XLNet model embedding notebooks, indicating a focus on feature engineering for protein sequences. The user further added a Bert and XLNet attention head view with the libraries that are used in hugging-face for a better visualization. This suggests the implementation of machine learning models for biological data analysis.
huggingface/transformers
Aug 2020 - Sep 2022
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Role in this project:ML Engineer
ML EngineerContributions:25 reviews, 10 commits, 17 PRs in 2 years
Contributions summary:Ahmed's contributions primarily focused on implementing and refining T5 model components within the Hugging Face Transformers library. They added and modified code related to the T5 encoder, including feature extraction capabilities and model parallelism. The user also addressed issues related to TPU sharding and configuration, and fixed iterations for the decoder, demonstrating a focus on improving the model's functionality and performance. Furthermore, the user addressed gradient checkpointing in the Flax LongT5 model and fixed an issue for Switch Transformers.
pythonbertspeech-recognitionstate-of-the-artflax
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