Omar Shehab

Engineered iterative feature selection for 6000 MOFs, reducing 2200→300 features via ensemble learning (Random Forest, XGBoost) across 4 rounds. Accelerated downstream regressor workflows from 1 month to weeks iteration cycles, enabling faster experimentation. Collaborated in a 4-person subteam within a 15-person lab, coordinating outputs with regressor team.

Designed and implemented distributed computing algorithms to optimize large-scale molecular data processing for datasets containing over 10,000 molecular structures. Built scalable data pipelines using community detection algorithms with a modularity score of 0.94. Developed fault-tolerant systems with robust error handling for molecular simulation workflows, ensuring reliability in critical scientific computations.

Designed and implemented distributed computing algorithms for large-scale molecular data processing, optimizing performance for datasets with over 10,000 molecular structures using parallel processing techniques. Built scalable data pipeline with community detection algorithms achieving a 0.94 modularity score, showing expertise in algorithm optimization and performance tuning. Developed fault-tolerant systems for molecular simulation workflows, implementing robust error handling and recovery mechanisms ensuring reliable processing of critical scientific computations.