Rajiv Ramnath

Rajiv Ramnath's recent work spans a broad range of applied computer science topics, including machine learning for video and image generation, document understanding and retrieval, federated and graph-based learning, model efficiency and pruning, and security for AI systems such as retrieval-augmented language models. Much of the research applies deep learning and generative models to practical problems like document question-answering, species image generation, sports prediction, and accident forecasting. The work is typically shared as preprints and conference-style papers, often through collaborative, multi-topic projects.

Publication output has grown over the last decade, rising from roughly 6-7 papers per year in 2017-2021 to a peak of 19 in 2024, with continued high activity (10-14 papers) in 2025-2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026