genuine-avocado

India

Description and Rationale

Who I am

I’m Nitesh, an independent data scientist and engineer with a background spanning computer vision, geospatial intelligence, voice AI, RAG/LLM systems, and cloud infrastructure. I graduated from IIT Delhi and have spent the past few years building end-to-end ML pipelines and offline-first data platforms for real-world, field-deployed applications — working with noisy, high-frequency sensor and operational data, and turning it into reliable predictive systems.

Why I’m registering

I have a long-standing personal interest in aviation and flight tracking. I previously built SkyWatcher, a web app that pulls live ADS-B data to show a flight’s speed, altitude, and path on an interactive map — a project born purely out of curiosity about how much can be reconstructed about an aircraft’s behavior from open flight data. Taxi-out time prediction feels like a natural extension of that interest: it’s the kind of messy, real-world operational data problem with genuine downstream impact (fuel burn, emissions, delays) that I enjoy working on. I’m also motivated by the challenge’s open-data and open-science ethos — publishing code publicly and writing up the approach for JOAS mirrors how I already like to work, having co-authored papers on applied ML pipelines before. This felt like a great opportunity to combine my aviation curiosity with my modelling and engineering skills, benchmark myself against a strong community of practitioners, and contribute something useful back to the open aviation research ecosystem.

Details

• Type: Independent
• Country: India
• Number of team members: 1

ID	Forename	Surname	Affiliation
1	Nitesh	Saini	Unknow