Salman Ahmed Rahman

M.S. in Natural Language Processing

Research Interests

I am currently working as a Research Associate at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), where my research focuses on developing natural language processing techniques for multilingual propaganda detection and fact-checking. My broader research interests span the fields of natural language processing, machine learning, and computational social science. My work aims to develop culturally-aware and socially responsible AI systems that can effectively counter misinformation, detect harmful online behaviors, and foster healthy online communities. Specific areas of interest include:

• Cross-lingual and multi-lingual approaches to propaganda detection and fact-checking
• Integrating cultural knowledge and social context into NLP models
• Developing robust and equitable methods for identifying and mitigating online toxicity
• Leveraging computational methods to understand and address the spread of misinformation across different platforms and communities
• Designing NLP tools and resources for low-resource languages and dialects

Education

• M.S. in Natural Language Processing, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
• Bachelor of Technology, Information Technology, Vellore Institute of Technology (VIT), India

I will begin my Ph.D. in Computer Science at UCLA, where I plan to continue my research on developing socially aware and responsible language technologies.

Research Experience

Mohamed bin Zayed University of Artificial Intelligence

• Master's Thesis: "Cross-lingual Propaganda Detection in Social Media"
  • Developed a novel approach for detecting propaganda techniques in multilingual social media content using transfer learning and cross-lingual embeddings
  • Achieved state-of-the-art performance on benchmark datasets in Arabic, English, and Hindi
• Research Associate, MBZUAI
  • Collaborated on a project aimed at automatically generating layman's explanations for scientific research papers using large language models and reinforcement learning
  • Contributed to the development of a neighborhood-based framework for resource-efficient content flagging in online communities