Summary
Mohammad Nouyed is a postdoctoral researcher specializing in AI safety and clinical evaluation, with a decade of experience applying frontier LLMs and multimodal foundation models to real-world healthcare problems. His work at WVU probes failure modes such as chatbot sycophancy, prompt framing effects on diagnostic accuracy, and comparative benchmarks of general-purpose versus domain-specific models for medical imaging, with publications in npj Digital Medicine and IEEE venues. His PhD developed scalable methods for gigapixel histopathology classification addressing class imbalance and model trustworthiness, and earlier biometric quality work was approved for DHS testing—evidence of impact beyond academia. Skilled in Python, PyTorch, Hugging Face, OpenAI/Gemini APIs and pathology toolkits like OpenSlide and QuPath, he seeks research faculty or scientist roles in clinical AI evaluation and safety. A U.S. permanent resident based in Morgantown, he combines deep technical rigor with applied evaluation studies that bridge diagnostics, ethics, and deployment.
10 years of coding experience
16 years of employment as a software developer
Master of Science (M.Sc.), Computer Science, 3.89, Master of Science (M.Sc.), Computer Science, 3.89 at Indpendent University Bangladesh
Doctor of Philosophy - PhD, Computer Science, 3.71, Doctor of Philosophy - PhD, Computer Science, 3.71 at West Virginia University
Bachelor of Science (B.Sc.), Computer Science, Bachelor of Science (B.Sc.), Computer Science at Independent University bangladesh
English