Tarik A. Rashid
Papers
2
Total Citations
5
H-Index
2
About
Tarik A. Rashid is a researcher at the forefront of artificial intelligence, with a focus on enhancing the efficiency and adaptability of large language models (LLMs) and advancing computer vision techniques. His work addresses critical challenges in multi-modal reasoning and object recognition. A major contribution is the development of a framework that integrates LLaMA-Adapter with Meta-Reasoning Prompting (MRP), enabling rapid, parameter-efficient adaptation of LLMs to diverse tasks and modalities while overcoming rigid reasoning strategies—a breakthrough for scalable AI systems. This 2025 paper has already garnered 3 citations, signaling its emerging impact. Earlier, Rashid contributed to computer vision with his 2015 work on "Kernel Visual Keyword Description for Object and Place Recognition," which laid groundwork for robust visual classification. His research bridges the gap between efficient model tuning and advanced reasoning, offering practical solutions for real-world AI deployment. With a growing citation record and a focus on cutting-edge LLM adaptation, Rashid is a rising voice in making AI more flexible and computationally accessible.
Research Focus
Key Achievements
Top Papers
- 1
- 2Kernel Visual Keyword Description for Object and Place Recognition2 citations · 2015