MS Computer Science @ Georgia Tech · Former ML Engineer @ Adobe · Building compute-efficient AI systems for video understanding and document intelligence.
I'm a grad student at Georgia Tech specialising in ML and Computer Vision. Previously I spent 4 years at Adobe shipping real-time document intelligence and computer vision models that now serve millions of users worldwide. I care deeply about compute efficiency — building systems that are not just accurate, but deployable and fast.
Compute-optimal multimodal retrieval resolving the ingest-vs-query cost bottleneck. Applies adaptive candidate pruning and uncertainty-aware tool selection to maximise information gain per GPU dollar.
Fine-grained optimisation system using task-type classifiers to dynamically select among five context retrieval strategies — from baseline RAG to agentic search — on the Video-MME benchmark.
Adapts Swin ViT models to self-supervised 2-channel pavement image registration with a 1×1 input adapter layer, enabling road-degradation monitoring for infrastructure agencies.
Training-free, explainable framework using empirical bias profiling and inverse propensity weighting to mitigate position bias in LLM re-ranking across real-world datasets.
Open to research collaborations, interesting problems, and good conversations.