• Developed a visualization of the embedding space of biological foundation models, leveraging React and TypeScript for the frontend, and Python and FastAPI for data integration from the datalake. Resulted
in faster hit to lead times for scientists running experiments.
• Improved features to display partnership-specific data while ensuring appropriate access control with
feature flags.
• Leveraged LLMs to create streamlined work flows that accelerate drug discovery using internally generated proprietary datasets as well as partner data.
• Created a feedback mechanism for LLM responses, enabling the user to provide ratings (thumbs
up/down) and text comments. Built using GraphQL and React, the solution improved the tracking
and quality of AI responses.
