Fenil Modi

At Hubbl, I defined the backend and data architecture, including 8+ core services and the PostgreSQL schema for community, event, and speaker records. I also consolidated four regional user-group datasets into one relational store.

I built Node-based background workers for scheduled mail synchronization and scraping, and owned the registration pipeline for a 120-capacity national analytics event with the India AI + Tableau User Group and Queen’s University Belfast.

On ORCAS at Silver Oak University, I built a Python pipeline to validate checksums and resolve epoch drift across a ∼10,000-object TLE catalogue. I used Monte Carlo sampling to quantify collision-probability uncertainty and cross-validated SGP4 against reference ephemerides.

I was first author and presenter of an IEEE-published paper on probabilistic space debris conjunction assessment. My research combined covariance intersection, machine-learning conjunction screening, and 3σ covariance ellipse overlap in the B-plane; I’m seeking research positions in space situational awareness and large-scale scientific data processing.