I dive in first and figure out the tools as I go. My work is in computer vision and on-device ML, but the curiosity goes much wider than that. I like understanding how things work at the level below where most people stop, and building things that actually matter to me.
Professional work, personal tools, and infrastructure. All built by figuring things out along the way.
Professional
Professional · 2022 – Present
Real-Time Golf Shot Analysis Platform
Turning a phone into a precision instrument that competes with hardware costing thousands of dollars.
Professional golf monitors use radar arrays and dedicated sensors and cost upwards of $5,000. The goal was to match that capability using just an iPhone camera, computing ball speed, launch angle, spin, and carry distance in real time on-device.
There was no existing work to build on. No models to fine-tune, no datasets to train from, no papers to reference. As part of a two-person team, every component had to be designed from scratch: the neural networks, the physics engine, the camera calibration pipeline, the evaluation framework. Every design decision came from experimentation and measurement.
Getting it accurate enough to compete with dedicated hardware was a hard problem. It got solved.
<5%
Error vs. $5K+ monitors
<5ms
Inference on iPhone
6
Pipeline stages
5
Environments
Technical contributions
Ball detection pipeline across 5 distinct shooting environments with region-of-interest optimisation
Custom change detection network for precise club impact classification from consecutive frames
Ball flight segmentation and centroid tracking through the full flight path after impact
Full camera calibration pipeline: pattern detection, per-device intrinsic profiles, lens distortion correction
Ball speed and launch angle computed from first principles using camera geometry and 3D reconstruction
Physics simulation engine for carry distance: aerodynamic forces, spin decay, terrain bounce across surface types
Multiple distance estimation methods benchmarked across thousands of shots to determine the production approach
All models deployed on-device for real-time inference, no server round-trip
Data infrastructure: cloud storage, automated dataset generation, multi-path evaluation framework
Model IP protection via encryption for distribution
Built a real-time golf shot analysis platform from scratch as part of a two-person team. No prior models, datasets, or benchmarks existed
Designed and implemented a 6-stage on-device inference pipeline: ball detection, impact classification, flight segmentation, 3D speed and angle computation, physics simulation, and CoreML deployment
Achieved sub-5% error against professional-grade launch monitors (Foresight GC2/GC3) at sub-5ms inference on iPhone
Built the full camera calibration system, per-device distortion profiles, and 5 independent distance estimation methods benchmarked across thousands of shots
Designed data infrastructure on AWS (S3 + DynamoDB), automated dataset generation pipelines, and a 5-path evaluation framework
🎓 Education
September 2021 – December 2022
MSc in Data Science · 2:1
University of Exeter, UK
Postgraduate study in machine learning, statistical modelling, and data engineering. Graduated with a 2:1.
💼 Work
March 2020 – July 2021
AI Engineer
Accubits Technologies
Built face mask detection system using ResNet50, 25K samples per class, 96% accuracy, with automated data collection and augmentation pipelines
Developed food image classification pipeline with automated labelling, improving model accuracy by 20% over baseline
Deployed ML inference services on AWS using Docker, Lambda, and EC2 for scalable, production-grade serving
🎓 Education
2015 – July 2019
B.Tech in Computer Science
APJ Abdul Kalam Technological University, Kerala
Undergraduate degree in computer science and engineering.
Built because I wanted something that actually fit how I track money, without my data sitting on someone else's server. Multi-account, investment tracking, Plotly dashboards, 7 colour themes. Runs on my own hardware.
This site started as a homelab documentation project and grew into a full portfolio. Interactive infrastructure diagram, machine catalogue, live uptime status — all in a single HTML file I own and run myself.
Five machines, a private WireGuard mesh, and 30+ self-hosted services running at home.
Part practical, part curiosity. Understanding how things work by running them yourself.
💡
What's a homelab?
A homelab is personal infrastructure you run at home. Instead of relying on cloud services like Google Drive or Netflix, you run your own versions on hardware you own and control. For me it started as a storage experiment and became something much bigger: a way to understand how the internet actually works from the inside.
Most of my life I've been a pattern finder. Give me something new and I'll have a mental map of it before I've read the manual.
Professionally I work in computer vision and on-device ML. For the past few years that's meant being part of a small team building a real-time golf shot analysis system with no prior art to reference. Every component had to be figured out from scratch. It was a hard problem and it got solved.
Outside of work, the same instinct takes over. What started as a small NAS slowly turned into a full self-hosted network. I run it for data privacy and control, but the deeper reason is wanting to understand how the internet actually works at a granular level. How systems talk to each other, where things break, what's happening under the hood.
When in doubt, dig deeper. What does the physics actually look like? How does the network actually decide? That curiosity is what drives everything.
Currently
Senior ML Engineer at AsmiovX, based in Kerala, India. Building computer vision systems for sports and exploring whatever interesting problem comes up next.