Ebraheem Abdelhafez

• Designed and implemented robust data pipelines using Azure Data Factory, streamlining ETL processes to efficiently process terabytes of
data daily
• Leveraged PySpark on Databricks to optimize large-scale data transformations, boosting processing efficiency by 50%
• Developed advanced unsupervised learning algorithms, to detect root causes for failures that occur during test rides

• Designing and implementing robust data pipelines with Azure Data Factory, optimizing ETL processes
• Leveraging PySpark on Databricks for efficient data processing and transformation
• Developing advanced unsupervised learning algorithms to pinpoint root causes of errors, enhancing data accuracy and reliability
• Explored domain adaptation techniques to bridge the gap between real-world and synthetic domains specifically for semantic segmentation tasks
• Monitored and validated Machine Learning models during test drives to ensure their effectiveness and reliability
• Researched and implemented temporal fusion methodologies for enhancing 3D object detection, utilizing Birds Eye View (BEV) Networks

• Tweaked and fine-tuned models before being deployed to multiple countries to ensure best performance during dry runs
• Integrated temporal fusion component for 3D object detection with BEV Networks, improving detection precision with lower hardware
setup costs

• Parameterize 3D scenes into differentiable hash tables implemented in JAX
• Managed to obtain high-fidelity 3D reconstructions using closed-form and learning-based inference methods (the resolution can reach 800x800x800)
• The implemented parameterizations drop the memory footprint by 40% compared to grids for the same resolution
• Improve the camera pose estimation accuracy whenever the scene is reconstructed with high fidelity (using hash tables)

• Parameterized 3D scenes into JAX-based differentiable hash tables, enabling efficient, high-resolution reconstructions
• Delivered high-fidelity 3D reconstructions (800x800x800) using closed-form and learning-based inference, beating the benchmark of only
(350x350x350) resolution
• Reduced memory usage by 40% with hash table parameterizations, surpassing traditional grid-based methods

• Built ML classifiers to assess lease approval certainty across countries, saving 20% of salespeople time and maintaining projected
finance per month in multiple countries
• Created R-Shiny apps (e.g., Pipeline-Forecasting, Reporting Portal) for HANA Data Lake, boosting data analysis and automating the
reporting processes