Skip Navigation

Naveen Kumar

Skip Side Navigation

Naveen Kumar

Associate Professor of Management Information Systems

Naveen Kumar

Department: Management Information Systems
Office: Adams Hall Room 3242
Phone: (405) 325-3645
Interests: Large Language Models and Artificial Intelligence

Naveen Kumar is an Associate Professor in the Management Information Systems (MIS) division at the Price College of Business, University of Oklahoma, Norman, and has years of experience in information systems and data science. Dr. Kumar holds a Ph.D. from the University of Washington, Seattle (2006). Before joining academia in 2015, Dr. Kumar worked for over nine years as a data science researcher at Intel Corporation, one of the nation’s leading high-tech companies. His research interests include large language models and artificial intelligence techniques focusing on developing and applying analytics methods for contemporary big data applications.

Dr. Kumar has taught many executive, undergraduate, and graduate level courses in data science. He offers learners a variety of real-world experiences, which he shares in the classroom through storytelling and practical application exercises. He employs an application-oriented learning approach by relating theoretical concepts to real world industry-based scenarios, rather than delivering a didactic lecture covering the course material. He has helped his students in seeking internship or full-time positions in data science. Dr. Kumar finds it personally gratifying to hear from students after they have graduated.

Dr. Kumar has been involved in different types of service activities within university systems as well as in the academic community. He was invited to be a Track Chair of prestigious conferences such as the Production and Operations Management Society (POMS) annual conference (2018) and Decision Science Institute (DSI) annual conference (2018 and 2019). He has been a referee to the leading Information Systems journals. He has been in leadership roles in data science for more than a decade. He has successfully led and delivered data science programs and projects to various institutions. Dr. Kumar has worked with organizations, such as FedEx Corporation, the Airforce Institute of Technology, Memphis Neighborhood Preservation Inc., and others to conduct data science research projects. 

Dr. Kumar is a recipient of Microsoft Azure Research Award (Year 2017-2018). Also, he has secured funding for his research work in developing anomaly detection framework for online review platforms using advanced machine-tearning by the by the Cluster to Advance Cyber Security & Testing (CAST), FedEx Institute of Technology (FIT) (Years 2015-2016 and 2017-2018) and applications of data science principles in software testing, funded by the FedEx Corporation (Years 2015-2016 and 2017-2018).


  • Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2019). Detecting anomalous online reviewers: an unsupervised approach using mixture models. Journal of Management Information Systems, 36(4), 1313-1346.
  • Paruchuri, S., Pollock, T. G., & Kumar, N. (2019). On the tip of the brain: Understanding when negative reputational events can have positive reputation spillovers, and for how long. Strategic Management Journal, 40(12), 1965-1983.
  • Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2018). Detecting review manipulation on online platforms with hierarchical supervised learning. Journal of Management Information Systems, 35(1), 350-380.
  • Kumar, N., Qiu, L., & Kumar, S. (2018). Exit, voice, and response on digital platforms: An empirical investigation of online management response strategies. Information Systems Research, 29(4), 849-870.
  • Bhuyan, S. S., Kim, H., Isehunwa, O. O., Kumar, N., Bhatt, J., Wyant, D. K., ... & Dasgupta, D. (2017). Privacy and security issues in mobile health: Current research and future directions. Health Policy and Technology, 6(2), 188-191.
  • Sharma, S., Kumar, N., Kumar, R., & Krishna, C. R. (2020). The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach. Communications of the Association for Information Systems, 46(1), 26.
  • Kumar, N., Qiu, L., and Kumar, S. “A Hashtag is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags,” Under review (2nd round).
  • Kumar, N., Kennedy, K., Gildersleeve, K., Abelson, R., Mastrangelo, C. M., & Montgomery, D. C. (2006). A review of yield modelling techniques for semiconductor manufacturing. International Journal of Production Research, 44(23), 5019-5036.