Natick, Massachusetts, United States
AI Assisted Coding
• Worked on a transformer based architecture for code prediction that is fast, accurate, runs fully offline, and meets the RAIL guidelines for speed
• Reduced memory footprint by 53% and sped up the model inference by 17% using network projection techniques
• Increased model accuracy by 12.3% by incorporating improved data preprocessing techniques using Abstract Syntax Trees (AST) and Byte Pair Encoding (BPE)
• Data Analysis for tracking user productivity metrics like acceptance rate, correctness, etc.
Conferences, Workshops, Talks
• NeurIPS 2023 - Women in Machine Learning: [Poster] Farm to Plate AI
• Google DevFest Boston, December 2023: [Talk] Code Generation and Code Completion using Generative AI
• Women in Data Science, November 2023
• Grace Hopper Celebration 2023: [Workshop] Farm to Plate AI: Enhance Freshness and Reduce Waste using Computer Vision and Robotics
• NeurIPS 2021 - Women in Machine Learning: [Poster] Getting Started with Model Cards
• NeurIPS 2021: [Social] Reducing Maternal Mortality using AI