Jim Samuel

Jim Samuel, Ph.D.

Associate Professor of Practice | Executive Director, Master of Public Informatics


Office: 493, Civic Square Building
Email: jim.samuel [at] rutgers.edu
Phone: (848) 932-2969


Ph.D., Baruch College (AACSB), City University of New York; M.B.A., Thunderbird School of Global Management, Arizona State University; M.Arch, Sir J.J. CoA

Jim Samuel, Ph.D.

Associate Professor of Practice | Executive Director, Master of Public Informatics

Dr. Jim Samuel is an Associate Professor of Practice and Executive Director of the Informatics Program at Bloustein. He is an information and artificial intelligence (AI) scientist, with significant industry experience in finance, technology, entrepreneurship and data analytics. Dr. Samuel’s primary research covers human intelligence and artificial intelligences interaction and information philosophy.

Dr. Samuel’s applied research focuses on the optimal use of big data and smart data driven AI applications, text analytics, natural language processing, public informatics and socioeconomic implications of AI, applied machine learning, social media analytics, AI education and AI bias.

Dr. Samuel completed his Ph.D. from the Zicklin School of Business, Baruch College – CUNY, and he also has M.Arch and M.B.A (International Finance) degrees along with AI/NLP training from Stanford University. His research has been published in top academic journals and he has served as Editor in Chief and in multiple editorial roles. Dr. Samuel has worked with large multinational financial services corporations, and advises businesses and organizations on analytics and AI driven value creation strategies. He is passionate about research driven thought leadership in AI, information philosophy, data science and informatics.

Teaching and Research Interests
  • Artificial intelligence, generative AI
  • Natural language processing, polysemy and meanings
  • Data science, business & social media analytics, public informatics
  • AI-human interaction, AI bias, AI ethics and policy
  • Information philosophy, equivocality, overload and complexity.

Teaching topics include Artificial Intelligence, natural language processing, generation and understanding for Public Informatics, data science, and supervised, unsupervised and reinforcement machine learning.

Specific current courses include:
  • 34:816:645 Artificial Intelligence: Practice, Principles & Strategy (Online live synchronous, currently open to all graduate students @ Rutgers University).
  • 34:816:510 Studio in Public Informatics – NLP and text analytics projects for clients
  • 34:816:656 Intro to Python for Data Visualization (Summer only, 1 credit, online, open to all @ Rutgers University)