Reza Azad
End-to-end Deep Learning Engineer at LOXO
Berlin Metropolitan Area Germany
Summary
👤
Senior🎓
Top SchoolReza Azad is an end-to-end deep learning engineer and researcher with 9+ years of experience bridging academic research and industrial ML product development, currently based in Berlin. He has a strong publication record in top computer vision and medical imaging venues (ICCV, ECCV, MICCAI, WACV) and has contributed to open-source medical imaging tooling such as ivadomed and BCDU-Net implementations. His work spans convolutional and attention-based architectures, few-shot learning, and missing-modality compensation, with hands-on expertise in Python, CUDA, and high-performance model engineering. Reza has collaborated with leading labs worldwide (MILA, RWTH, Stanford) and translated novel methods into practical pipelines for medical image segmentation and video understanding. Notably, he ranked 2nd in a 700+ participant SegPC 2021 challenge, reflecting both research rigor and competitive engineering.
9 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Engineering, Excellent, Doctor of Philosophy - PhD, Computer Engineering, Excellent at RWTH Aachen UniversityonlineMaster's degree, Artificial Intelligence, A+, Master's degree, Artificial Intelligence, A+ at Sharif University of Technology
English, Turkish, Persian
Github Skills (16)
keras10
lstm10
convolutional-neural-networks10
deep-learning10
medical-image-segmentation10
medical-image-processing9
segmentation9
image-segmentation9
dataprep8
machine-learning8
computer-vision8
data-preprocessing8
attention-mechanism8
python8
medical8
Programming languages (2)
Github contributions (5)
rezazad68/BCDU-Net
Jun 2019 - Feb 2022
BCDU-Net : Medical Image Segmentation
Role in this project:
ML Engineer
ML EngineerContributions:165 commits, 1 PR, 62 pushes in 2 years 8 months
Contributions summary:Reza primarily contributed to the implementation and modification of deep learning models for medical image segmentation, specifically focusing on skin lesion and retinal vessel segmentation. Their work involved developing and integrating custom layers, attention mechanisms, and convolutional neural network architectures like U-Net and LSTM-based models using Keras. Furthermore, the user worked on data preparation, including patch extraction, dataset normalization, and evaluation of model performance, demonstrating a comprehensive understanding of the machine learning workflow.
medical-applicationsemantic-segmentationmedical-imageimage-segmentationmedical-image-segmentation
rezazad68/FRCU-Net
Aug 2021 - Nov 2021
Contributions:18 commits, 11 pushes, 1 branch in 3 months
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