Experience

I work on retrieval models for Microsoft's Audience Network — the system that matches ads to relevant content across the web. My main focus is intent-based retrieval: building encoder models that understand what a user is actually looking for and surface ads that genuinely match.

The next-generation retrieval model I led achieved +14.32 absolute precision points (P@100) over the production baseline using 30× less training data. It went into A/B testing in NA and delivered +0.61% CTR and +0.51% revenue lift. The evaluation methodology was adopted as standard across US and India MSAN teams.

I also designed a unified evaluation framework across three teams (IDC, STCA, US MSAN) and built a high-impact product ads index using ML-driven retrieval that replaced the legacy rule-based system, delivering a 2.77% revenue lift.

Built an image quality scorer — a fine-tuned ResNet-50 (F1: 90%) that now evaluates nearly every food photo on the platform. Also designed an automated ad creation system using generative models, and built Photo Cake — a real-time image overlay feature that drove 3,000+ orders on Mother's Day.

Led semantic text similarity at scale — scaled to 1M+ texts with F1 of 85%, exploring prompt engineering, fine-tuning, and novel loss functions. Wrote 20+ pages of data preparation guidelines and supervised the annotation team.