Dinuo (Zoey) Zhou

Data & Back-End Development Intern at Grav.id

Developed an end-to-end NLP data pipeline to ingest, normalize, and entity-match 489K+ raw IRS 990 records, transforming them into 80K structured nonprofit profiles to support funding and grant analysis. Delivered analytics dashboards on nonprofit funding patterns and grant competitiveness, enabling product and customer success teams to make data-driven strategic decisions.

Research Apprentice at UW Department of Linguistics

Conducted systematic error analysis of dialectal ASR outputs to identify recurring phonological and lexical bias patterns, informing improved model evaluation criteria. Built a web-based evaluation and QA tool to standardize transcript comparison workflows, reducing review variance across 61 prompt conditions.

Research Apprentice at UW Department of Linguistics

Produced analysis-ready human response datasets by coding and normalizing experimental outputs, enabling structured quantitative analysis across language learning experiments. Applied mixed-effects regression modeling in R to analyze learning trends across 30+ participants and validate experimental hypotheses using tidyverse tools (ggplot2, dplyr, tidyr) for analysis and visualization.