Mohamed T. Younes
Papers
1
Total Citations
2
H-Index
1
About
Mohamed T. Younes is a forward-looking researcher at the intersection of artificial intelligence and human resource technology, with a primary focus on robotic process automation (RPA) and large language models (LLMs). His most cited work, "MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking" (2025), introduces a groundbreaking Applicant Tracking System (ATS) that leverages a multi-layer LLM-driven RPA framework to revolutionize recruitment. By automating resume screening and candidate shortlisting, Younes directly addresses critical bottlenecks in traditional hiring processes, offering a scalable solution that saves time and resources while enhancing accuracy. Although his citation count is still emerging—with 2 citations for this key paper—his work signals a significant shift toward intelligent automation in HR tech. Younes’s contributions stand out for their practical, industry-ready approach, bridging cutting-edge AI with real-world workforce challenges. As a researcher, he exemplifies how LLMs can be harnessed beyond text generation, embedding them into operational frameworks that transform organizational efficiency. For students and researchers exploring AI-driven process optimization, Younes’s MLAR framework offers a compelling case study in applied innovation.
Research Focus
Key Achievements
Top Papers
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