Elias Benbourenane

I develop tools for biomedical research using React, FastAPI, and Next.js, including a comprehensive platform to explore drug-disease relationships across 13M+ PaTH Network patient records. Recently built an LLM-powered application that parses biomedical knowledge graphs to generate visual and text summaries of drug mechanisms, and prototyped an ML tool for UPMC hospital to identify dangerous patient care transitions.

A major focus has been modernizing legacy systems and optimizing performance. I refactored a decade-old SQL analytics codebase with modular design principles, achieving a ~30x runtime reduction. Additionally, I've scripted multi-node SLURM pipelines for HPC data analysis that reduced quarterly workflow compute times by over 90%.

On the infrastructure side, I architected and deployed a secure, HIPAA-compliant Linux/Docker environment for AI compute workloads that now serves 200+ users. I've managed over 10 production servers across AWS and on-premises environments, often leading architecture decisions and serving as the primary developer across 9+ concurrent projects.

During my internship, I led a comprehensive full-stack migration to a Django and React architecture, introducing new features while significantly improving code maintainability. I also developed an analytics dashboard using Django and Chart.js that helps the organization analyze user engagement patterns and make data-driven outreach decisions.

Beyond development work, I implemented robust security measures including a user authentication and authorization system with OAuth 2.0 integration. I also established CI/CD pipelines using GitHub Actions that fully automated the build, test, and deployment processes on AWS. The quality of my work led to a promotion to a consulting role post-internship, where I continue to advise on technical direction and key engineering decisions across multiple projects.

I worked on high-performance systems for physics research, focusing on database optimization and custom tooling development. My primary achievement was refactoring SQL tables containing billions of rows of sensor data, which reduced the overall database size by over 40% while significantly improving indexing performance for faster queries.

I also extended a custom Grafana fork with new Go and React modules specifically designed for querying high-frequency physics time series data. To support the team's development workflow, I built comprehensive CI/CD pipelines using AWS, Docker, and GitHub Actions that fully automated daily builds and deployments. Additionally, I engineered automated Go-based SQL data validation pipelines that eliminated approximately 6 hours of manual physicist review work each week.

I operated the university's help desk phone line, providing comprehensive technical support for software, hardware, and general IT issues to students, faculty, and staff. Averaging over 15 calls per day, I assisted users with varying levels of digital literacy, successfully resolving complex technical problems while maintaining clear, patient communication. My focus on user experience and problem-solving resulted in a 100% satisfaction rating throughout my tenure.