Muhammad Mahbubur Rahman

Muhammad M. Rahman, Ph.D., is a tenure-track assistant professor and a computer scientist. Prior to joining the George Washington University and Children’s National Hospital, Dr. Rahman was an AI research fellow at National Institutes of Health. Before that, he was a postdoctoral fellow in the Center for Language and Speech Processing (CLSP) research lab at Johns Hopkins University.

Dr. Rahman’s primary research interests are artificial intelligence (AI), natural language processing (NLP), machine learning, data analytics and child and adolescent mental health. He has a deep passion for investigating and developing new machine learning, natural language processing and AI techniques to understand child health using big data. Dr. Rahman has explored AI, NLP and machine learning techniques on large unstructured document understanding. His research automatically sectionizes large and complex PDF documents and annotates each section with a semantic and human-understandable label. He has also pursued AI, NLP, and machine learning approaches in the development of novel, real-world intervention technologies for addiction, substance use and mental health. His research also investigates knowledge extractions, concept identifications, entity linking and content summarization from unstructured text.

Dr. Rahman intends to continue his research in the fields of NLP, machine learning, deep learning and data science, and desires to explore practical solutions in various application domains, including mental health, clinical psychology, and child well-being, where large volumes of data need to be processed from different sources, such as social media and national surveys. He is also deeply interested in designing new analytical approaches that help translational research scientists including clinical psychologists and behavioral therapists. The approaches could include extracting information about patients from medical records, predicting important trends regarding mental health of a patient from social interactions and geoinformation, and developing models for identifying mental health disorders.