My research focuses on advancing natural language processing (NLP) through the development of efficient machine learning techniques, particularly for optimizing large language models (LLMs) in real-world applications. I am passionate about designing methods that reduce computational costs while maintaining high performance, enabling the deployment of sophisticated NLP systems on resource-constrained devices.

I am also particularly interested in knowledge distillation and memory-efficient model compression as strategies for improving the usability and practicality of LLMs. My research investigates innovative techniques to distill and adapt models without sacrificing generalization capabilities, striking a balance between computational efficiency and robustness. Through these efforts, I seek to bridge the gap between cutting-edge NLP innovations and their real-world deployment, empowering researchers and practitioners to create models that are both powerful and resource-conscious.

Feel free to reach out for collaboration! 😃