Conducted thorough dataset checks and experiment logging to enhance the trustworthiness of research outcomes.

Engaged in failure-mode analysis to identify and mitigate potential issues in machine learning systems.

Contributed to the acceptance of a full paper at ES 2026, focusing on reliable AI planning methodologies.

Developed agentic workflows to convert unstructured data into structured formats, improving data usability.

Implemented caching, batching, and async worker queues, achieving a 38% reduction in inference costs.

Ensured system reliability with circuit breakers, maintaining performance metrics for interactive workloads.