New York, New York, United States
As an Engineer III at BlackRock, Rachel enhances Aladdin’s AI Chat by integrating various financial tools and agents, strengthening orchestration frameworks, and advancing evaluation capabilities. Rachel was a full-stack software engineer at ServiceNow’s Conversational AI team prior to joining BlackRock, where she contributed to RAG LLM reranking, hybrid search, and AI admin search config dashboard. She received her B.S. in Computer Science from Columbia University and published research on optimizing GPU energy efficiency for LLM inference.
AI Service, AI Platform, AI Chat Orchestration
Conversational AI team Projects Contributed To: RAG, LLM Re-Ranking, AI Search Admin Console, and various Enterprise-Level Search Functionalities. GHC'24
Sustainable computing @ARCADE Lab x IBM, supervised by Martha Kim I worked as a research assistant at the ARCADE lab and co-authored the paper: Characterizing Training Performance and Energy for Foundation Models and Image Classifiers on Multi-Instance GPUs. It's a comprehensive study on the performance-energy trade-offs across different GPU configurations, quantifying potential energy savings during the inference step in the machine learning lifecycle using NVIDIA Multi-Instance GPU (MIG) in comparison to the traditional GPU model, such as the A100.
CSEE 3827 Fundamentals of Computer Systems (prof. Martha Kim)
AI Search