ProfileSix years of building, from perception for driverless cars at university to load testing at cloud scale. Here is the long version.
I am a software engineer at GitHub, part of Microsoft, working on GitHub Pages. Until recently I spent four years on Azure App Testing (opens in a new tab) in the CoreAI division, where most of my time went on the service side of things: making large test runs start quickly, run reliably, report honestly, and fail in ways that tell you something useful.
Before that I read Computer Science at BITS Pilani, where I spent three years in Sally Robotics, a student-run driverless vehicle group that met outside class hours. I worked on the perception side, and that work became two papers: one on scene parsing for roads without lane markings, and one on restoring images shot through haze at night.
Away from work I like problems that end with a machine at home doing something it was not really meant to do, usually over a network connection that should not have worked.
Working on GitHub Pages.
Backend for Azure App Testing in the CoreAI division.
Joined the Azure App Testing team full time after interning with it, and was promoted to Software Engineer 2 in 2024.
A student-run driverless vehicle group on campus, built around whatever we could get done outside class hours. I worked on perception: semantic segmentation and object detection on Indian road scenes.
Interned with the Azure App Testing team in the CoreAI division.
Experimental features using Cisco IOx Docker on Catalyst series wireless LAN controllers.
Automated the analysis of client data using Python, NumPy, Pandas and MLxtend.
Built a predictive text typing system for email clients, along the lines of keyboard auto-suggestion, using PyTorch, Hugging Face and NLTK.
A GNOME Shell extension that puts Cloudflare 1.1.1.1 WARP in the top panel, so switching the tunnel on and off no longer means dropping to warp-cli. Published on the GNOME Extensions store.
A Double-DQN reinforcement learning agent trained and evaluated on the Arcade Learning Environment, built with PyTorch, Gym[atari] and NumPy.
A modified DeepLab V3+ architecture for semantic segmentation of Indian road scenes. Placed in the top 16 of the AutoNUE deep learning challenge.
Object detection on a custom dataset of Indian speed breakers, comparing YOLO V3 against TridentNet.
A computer vision approach to nighttime dehaze-enhancement, restoring scenes degraded by both haze and low light in a single end-to-end model.
Scene parsing for autonomous navigation where lane markings, kerbs and road boundaries cannot be relied on, as is typical of Indian roads.