Ahmed Soliman,

Expertise in Machine Learning, Generative AI and Natural Language Processing (NLP), with a focus on developing Agentic AI systems and advancing Large Language Model (LLM) technologies and Multimodal applications. Proven ability to optimize and fine-tune models for real-world applications, including code generation, Electronic Health Record (EHR) analysis for suicide risk identification, and healthcare innovation. Proficient in profiling LLMs and developing multimodal applications. Experienced in prompt engineering, LangChain, and information retrieval methodologies such as Retrieval-Augmented Generation (RAG). Dedicated to advancing AI research, driving innovation, and fostering knowledge sharing in the field of Artificial Intelligence.

My research advances Machine Learning (ML), Generative AI, and Natural Language Processing (NLP), focusing on Agentic AI systems, LLM optimization, and multimodal applications. I develop and fine-tune AI models for real-world challenges, including code generation, EHR-based suicide risk identification, and healthcare innovation. Leveraging techniques like prompt engineering, LangChain, and Retrieval-Augmented Generation (RAG), I aim to bridge research and practical implementation. My work drives innovation in AI, emphasizing scalable solutions and knowledge sharing to address complex societal and industrial problems.

• Conversional AI
• Deep Learning
• Generative AI
• Information Retrieval
• Machine learning and applications
• Multimodal
• Natural Language Processing (NLP)
• Prompt Engineering
• data science