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. CoA.
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.
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 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)
- Samuel, J., Kashyap, R., Samuel, Y., & Pelaez, A. (2022). Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations. International journal of information management, 65, 102505. Download from: https://www.sciencedirect.com/science/article/pii/S0268401222000366
- Samuel, J. (2023) The Critical Need for Transparency and Regulation amidst the Rise of Powerful Artificial Intelligence Models. Access here: https://scholars.org/contribution/critical-need-transparency-and-regulation
- Samuel, J., Brennan-Tonetta, M., Pfeiffer, M. H., Andrews, C., Hale, M., Chidipothu, N., … & Aslam, Z. (2023). Garden State open data index for public informatics: an integrated view of New Jersey’s open information ecosystem. Access here: https://bloustein.rutgers.edu/opendata/
- Samuel, J. (2021). A call for proactive policies for informatics and artificial intelligence technologies. Scholars Strategy Network. Access here: https://scholars.org/contribution/call-proactive-policies-informatics-and
- Rahman, M. S., Paul, K. C., Rahman, M. M., Samuel, J., Thill, J. C., Hossain, M. A., & Ali, G. M. N. (2023). Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach. Sustainable Cities and Society, 95, 104570. Download from: https://www.sciencedirect.com/science/article/pii/S2210670723001816
- 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. https://www.sciencedirect.com/science/article/abs/pii/S0167923621000075
- Jain, P. H., Kumar, V., Samuel, J., Singh, S., Mannepalli, A., & Anderson, R. (2023). Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods. Information, 14(6), 305. Download from: https://doi.org/10.3390/info14060305
- 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. Healthcare2021, 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, Journal of Big Data – Theory & Practice, Fall 2022. Download from: https://jbdtp.org/index.php/JBDTP/article/view/16
- 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. Handbook of Research for Big Data: Concepts and Techniques, Apple Academic Press, USA.
- Rahman, M. M., Ali, G. M. N., Li, X. J., Samuel, J., Paul, K. C., Chong, P. H., & Yakubov, M. (2021). Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon, 7(2). Download from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867397/
- 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, 2022. Download preprint from: https://arxiv.org/abs/2107.02025