Ahmed Soliman,

AI Data Scientist and Researcher specializing in advanced artificial intelligence methodologies. Expertise encompasses machine learning architectures, generative AI, multimodal systems, and natural language processing with a focus on clinical data analysis and medical informatics. Proficient in developing and optimizing large-scale language models for healthcare domains through advanced fine-tuning methodologies and prompt engineering. Dedicated to advancing responsible AI technologies that bridge computational innovation with clinical practice, delivering evidence-based solutions that transform healthcare delivery and improve patient care quality. Committed to pioneering ethical AI solutions that deliver measurable impact and translate cutting-edge research into practical applications with significant societal and commercial value.

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.