I am a seasoned Applied Machine Learning Research Scientist/data scientist/Engineer leader. My expertise is in building statistical/machine learning models(Bayesian and Frequentist modeling techniques) to help businesses realize their data journey. Currently work for h2o.ai as the Lead Machine Learning Scientist/Engineer, exploring and figuring out possibilities of generative AI (LLMs) one commit at a time.
- Also, exploring and helping improve ways to evaluate LLM models
- I'm currently leading 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 productionize 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.