Jim Samuel

Associate Professor of Practice
Executive Director, Master of Public Informatics

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. College of Architecture, an affiliate of the Royal Institute of British Architects


  • Room 493, Civic Square Building
  • (848) 932-2969
  • jim.samuel [at] rutgers.edu
  • Twitter: @jimsamuel
  • LinkedIn
Teaching and Research Interests
  • Artificial Intelligence
  • Data Science
  • Social Media Analytics
  • AI Bias & Ethics  
  • Computer Simulation Modeling

Jim Samuel is an Associate Professor of Practice and Executive Director of the Informatics Program at the Bloustein School. 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, textual analytics, natural language processing and artificially intelligent public opinion informatics. His expertise extends to 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 – City University of New York, and he also has M.Arch and M.B.A (International Finance) degrees.  Dr. Samuel has worked with large multinational financial services corporations, and advises businesses and organizations on data analytics and AI driven value creation strategies. He is passionate about research driven thought leadership in AI, information philosophy, analytics and informatics. 


View all course offerings and related syllabi

Teaching topics include textual analytics, data visualization, natural language processing, generation and understanding, and supervised, unsupervised and reinforcement machine learning.

Selected Publications
  • Samuel, J., Ali, G. G., Rahman, M., Esawi, E., & Samuel, Y. (2020). Covid-19 public sentiment insights and machine learning for tweets classification. Information, 11(6), 314. Download from:  https://doi.org/10.3390/info11060314
  • Samuel, J., Rahman, M. M., Ali, G. M. N., Samuel, Y., Pelaez, A., Chong, P. H. J., & Yakubov, M. (2020). Feeling positive about reopening? New normal scenarios from COVID-19 US reopen sentiment analytics. IEEE Access, 8, 142173-142190. Download from:   https://ieeexplore.ieee.org/iel7/6287639/8948470/09154672.pdf  
  • Garvey, M. D., Samuel, J., & Pelaez, A. (2021). Would you please like my tweet?! an artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation. Decision Support Systems, 144, 113497. 
  • Ali, G.G.M.N.; Rahman, M.M.; Hossain, M.A.; Rahman, M.S.; Paul, K.C.; Thill, J.-C.; Samuel, J. Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare 2021, 9, 1110.  Download from: https://doi.org/10.3390/healthcare9091110 
  • Samuel, J., Brennan-Tonetta, M., Samuel, Y., Subedi, P. and Smith, J., Strategies for Democratization of Supercomputing: Availability, Accessibility and Usability of High Performance Computing for Education and Practice of Big Data Analytics, (Forthcoming) Journal of Big Data – Theory & Practice, Fall 2021. 
  • Conner, C., Samuel, J., Garvey, M., Samuel, Y., and Kretinin, A., (2021) Conceptual Frameworks for Big-Data Visualization: Discussion on Models, Methods and Artificial Intelligence for Graphical Representations of Data. (Forthcoming) Handbook of Research for Big Data: Concepts and Techniques, Apple Academic Press, USA. 
  • J. Samuel, R. Palle, and E. Soares, Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, exploratory supervised & unsupervised learning on vaccine and market topics & sentiment, 2021.  Download preprint from:  https://arxiv.org/abs/2107.02025 

Areas of Expertise: Artificial Intelligence, AI Bias & Ethics, Computer Simulation Modeling, Data Science, Informatics, Information Philosophy, Science & Technology Policy, Social Media Analytics, Statistical Research Methods, Strategic Planning, Urban Design, Visualization