Enhanced and integrated the Cases at Risk model into Microsoft’s DfM workflows, driving $20M in annualized cost savings by proactively identifying and addressing high-risk support tickets.
Led the development of a high-quality golden dataset and optimized vector database architecture for Copilot for Azure, significantly improving retrieval accuracy and response quality for enterprise customers.
Architected and deployed a comprehensive experimentation platform supporting causal, Bayesian, and frequentist evaluation methodologies across 10+ product teams, driving over $100M in impact through improved decision-making and optimization.
Conducted a comprehensive measurement study of Support Copilot’s impact on agent productivity and customer satisfaction, establishing key metrics and success criteria for AI-powered support tools.
Contributed TabTransformer architecture to the AutoGluon open-source project, enhancing its capabilities for tabular data processing and improving model performance on structured datasets.