I am a seasoned Applied Machine Learning Research Scientist/data scientist/Engineer leader with 16+ years of experience. My expertise is building statistical/machine learning models(Bayesian and Frequentist modeling techniques) to help businesses realize their data journey. Exploring and figuring out possibilities of generative AI (LLMs) one commit at a time.

• Currently, in stealth. Experimenting with building safe, personalized assistants for Intelligent Process Automation using LLMs.

h2oai:

• Led research and developed service to improve ways to evaluate LLM models with Automated QA generation (task-specific synthetic test generation using auto prompt optimization) and alignment optimization (LLM robustness).
• Led efforts on LLM2SQL assistant for QnA on structured tabular data using SQL generation, https://github.com/h2oai/sql-sidekick
• Fun project with LLMs for image generation, https://github.com/h2oai/wave-image-styling-playground
• Responsible for research, concept, and production of Model-Analyzer, a unified interactive framework for simulation (What-If scenarios) and adversarial robustness, to continuously explore and evaluate predictive model behavior and limitations. https://docs.h2o.ai/wave-apps/h2o-model-analyzer/get-started/what-is-h2o-model-analyzer
• Responsible for building (including driving cross-organizational product and business strategies) AutoInsights under the guidance of Dr. Leland Wilkinson (Chief Scientist). AutoInsights is an automated self-service AI/ML system designed to discover hidden insights (auto EDA) and publish them as interactive, engaging, and actionable insights using natural language narratives. https://docs.h2o.ai/wave-apps/h2o-autoinsights/get-started/what-is-h2o-autoinsights
• Responsible for driving ML innovation/product and business strategies to improve the model interpretation ideas in h2o Driverless AI MLI.