Chad Jipiti

• •Leading research on next-generation multimodal large language models, resulting in state-of-the-art performance across multiple benchmarks.
• •Developed novel attention mechanism architectures, reducing training time by 35% while improving model performance by 12%.
• •Mentored junior researchers and published 5 papers in top-tier conferences including NeurIPS, ICML, and ACL.
• •Collaborated with ethics team to implement responsible AI guidelines and reduce bias in language models.

• •Contributed to the development of GPT-3, focusing on optimization techniques and training infrastructure for large-scale language models.
• •Implemented performance optimizations that reduced inference latency by 40% and improved training throughput by 25%.
• •Developed methods for evaluating model outputs and fine-tuning systems that significantly improved response quality and safety.

• •Worked on the original BERT team, contributing to architecture design and pre-training methodology for transformer-based models.
• •Developed techniques for knowledge extraction and commonsense reasoning that improved model performance on multiple NLP benchmarks.
• •Published research on attention mechanisms that has been cited over 1,500 times.

Dissertation: "Attention Mechanisms for Neural Network Architectures" Advisor: Dr. Andrew Ng

Specialized in artificial intelligence and natural language processing. Research focus on neural network architectures for language understanding.

Graduated summa cum laude. Honors thesis on statistical methods for machine translation. Minor in Mathematics.