Developing a ranking system framework for MediKanren, a biomedical reasoning knowledge graph-based system that answers medical queries, primarily used for identifying treatments for rare diseases.

Using Large Language Models (LLMs) to enhance the ranking algorithm, improving output accuracy

Employing Racket and Python to efficiently query multiple knowledge graphs, evaluating the results based on confidence levels and the novelty of the information.

Creating and implementing automation tools and frameworks for efficient database migration using SQL and Python, ensuring seamless and secure data transitions.

Contributing to the optimization of investment strategies by leveraging innovative AI-driven solutions, resulting in improved accuracy and efficiency of investment assessments.