Worked on personalization in the context of Federated Learning at UCLA. During internships at AWS AI, mainly worked on efficient LLM training, designed meta/learned optimizers to train LLMs, and worked on theoretical and practical implications of quantized training of LLMs.
Education
PhD student at UCLA ECE, advised by Prof. Suhas Diggavi.
Background
Final year PhD student at UCLA ECE, advised by Prof. Suhas Diggavi. Broadly interested in large scale machine learning and challenges revolving around it, including privacy-preserving machine learning, efficient training of large language models, optimization techniques for large language models, and personalization.