I studied Computer Science with a minor in Physics at the University of Michigan from 2016 to 2020. Relevant coursework included:

• Machine Learning
• Operating Systems
• Web Systems
• Computer Vision
• Data Structures and Algorithms
• Computer Architecture

I interned at LinkedIn in the summer of 2019 as an iOS engineer in the Growth org, working on experiments for LinkedIn's "Kudos" feature.

After graduating, I joined LinkedIn as a full-time backend engineer in July 2020. My early work focused on building backend systems for various content surfaces: editorial content curation and delivery for LinkedIn's pilot "Discover" tab, an analytics pipeline for our Editors to evaluate content virality, and notification lifecycle logic. My more recent work focuses on building distributed near-realtime recommender systems for the feed by tying together Flink ingestion pipelines, LLM embedding inference, GPU-accelerated vector retrieval, and caching layers that make all of it affordable at scale.