Yash Shah

I build computer-vision and machine-learning systems — from published research to the factory floor.

Climb · ALT 10,000 FT

About

Software engineer at NetApp building computer-vision and machine-learning systems, from published research to the factory floor.

Apparently likes planes enough to build an entire portfolio around them.

Role: SDE 2 at NetApp
Base: Bengaluru, India
Education: Integrated M.Tech, Computer Science and Engineering with Business Analytics, Vellore Institute of Technology · 2020–2025 · CGPA 9.52 / 10

Systems display

Cruise · ALT 60,000 FT

EFMIX2022EfficientNet-B0 · squeeze-and-excitation

A feature-fusion CNN for gastrointestinal disease classification, published in Diagnostics.

ResultThe method surpassed prior benchmarks on the task and was published in Diagnostics (MDPI), 2022, volume 12, article 2316.

URBST2023U-Net · semantic segmentation

Segments roads and buildings from satellite imagery for urban planning.

ResultUsed to identify road networks from satellite images and to generate house layouts for urban-planning workflows.

VIDGP2023speech-to-text · LLMs

Turns a long video into a short summary with follow-up Q&A.

DCATH2023metadata analysis · Fourier analysis

Detects and localises document forgeries at 97% accuracy.

ResultDetects and highlights manipulations with 97% accuracy. The project won first place at Smart India Hackathon 2023.

Flight log · ALT 40,000 FT

CurrentBLR
SDE 2, NetAppBengaluru, India
May 2023ICN
Machine Learning Intern, Defect ScannerSeoul, South Korea
- Stood up a live video-streaming server on Microsoft Azure for global access, cutting streaming latency to under one second.
- Flagged product defects from a live conveyor-belt feed on the factory floor, replacing roughly 12 hours of manual monitoring a day.
- Led an incremental-learning model that detects and registers object classes new to the system on production lines.
- Built an action-recognition model to reduce assembly-line defects.
May 2023 – July 2023BOM
Project Intern, Bhabha Atomic Research CentreMumbai, India
- Led an automated vehicle detection, tracking and logging system.
- Applied ML-Ops to move level-0 systems to level-1: automated testing and monitoring plus a CI runner on a development machine, saving two hours of manual testing per run.
- Introduced an SSIM-based performance monitor to catch failure cases of computer-vision models on video use-cases.
- Contributed to two research articles documenting the algorithms from this work.
October 2022 – February 2023LYS
Solar Project Consultant, Naldeo GroupLyon, France
- Led the team forecasting congestion in solar grids.
- Converted daily readings into hourly distributions, then predicted real-time grid congestion with predictive modelling to enable efficient load setup.