Jeet Shah
Software engineer & Columbia University MSCS student
New York City
I am a computer science graduate student working across production software, data systems, and applied machine-learning research. Through August 2026, I work part-time with Elevate Fitness and Voltek AI/Nanoloy. This website is an archive of my projects, research, and writing—and a place to experiment with design.
experience
Elevate Fitness
Dec 2025 - Aug 2026- Build and maintain Movynn, a React Native and Expo fitness application backed by Convex and published on the Apple App Store in India.
- Implemented native Apple Sign-In and secure session recovery, including nonce verification and first-login profile persistence.
- Built multi-ticket booking and Razorpay payment recovery and reconciliation, requiring server-confirmed capture before order fulfillment.
- Added account-deletion and pseudonymization flows, automated app and backend tests, continuous-integration checks, and release documentation.
Voltek AI / Nanoloy
Oct 2024 - Aug 2026- Lead frontend development for Voltek AI, an internal battery-research platform used by 10--100 internal Nanoloy users.
- Redesigned PostgreSQL process storage from process-specific, join-heavy tables to three indexed canonical tables with transactional writes and backfill validation, reducing observed material-query latency from 3-20 seconds to under 500 ms.
- Built the Next.js orchestration layer for a battery-research assistant supporting OpenAI, Anthropic, Gemini, and xAI, with internal-document retrieval, web search, citations, streamed responses, and chat history.
- Co-maintain NDAX and Neware ingestion pipelines, including file reconciliation and defensive parsing for incomplete file sets.
Centre for Interdisciplinary AI, FLAME University
Jan 2024 - May 2025- Configured and administered a multi-user GPU compute server for research and model training, including user management, drivers, storage, and environment isolation.
- Co-developed ConvGRU, conditional-GAN, and ConvLSTM training pipelines and satellite-data ingestion tooling for precipitation nowcasting.
- Supported team kaubega's Weather4Cast 2024 and 2025 submissions through model development, data-ingestion tooling, and GPU infrastructure.
ISRO, Space Applications Centre
May 2023 - Aug 2023- Developed ConvLSTM precipitation-nowcasting models using INSAT-3D satellite data in an air-gapped HPC environment.
research
arXiv preprint. Co-author. Team kaubega placed second in the Weather4Cast 2025 cumulative-rainfall task.
arXiv preprint. Co-author. Team kaubega placed first on the Weather4Cast 2024 core-challenge leaderboard.
Precipitation Nowcasting Using ConvLSTM with INSAT-3D Satellite Data over the Indian Subcontinent
2025FLAME Scholar's Program undergraduate thesis supervised by Kaushik Gopalan. The ConvLSTM forecast reduced six-hour RMSE by 30.7% compared with a Lucas-Kanade optical-flow baseline.