Naveen Kumar

2006 Ph.D. in Industrial Engineering, University of Washington, Seattle, WA
2003 Master of Science, Industrial Engineering, University of Washington, Seattle, WA
2002 Master of Science, Industrial Engineering, University of Tennessee, Knoxville, TN
1997 Bachelor of Engineering, Punjab Engineering College, India

Applications of Artificial Intelligence, Machine-Learning, and Econometrics on Digital Platforms, Information Systems, Healthcare

2018 – Present: Assistant Professor – MIS, School of Business, University of Washington Bothell
2015 – 2018: Assistant Professor, Department of Business Information and Technology (BIT), Fogelman College of Business and Economics, University of Memphis, Memphis, TN
2012 – 2015: Adjunct Faculty, Master of Healthcare Administration, Pacific University, Forest Grove, OR

2006 – 2015: Researcher/Analyst – Machine Learning and Big Data Analytics, Intel Corporation, USA

Paruchuri, S., Pollock, T. and Kumar, N. 2019. “On the tip of the brain: Understanding when negative reputational events can have positive reputation spillovers, and for how long,” forthcoming in Strategic Management Journal (SMJ).
Kumar, N., Qiu, L. and Kumar, S. 2018. “Exit, Voice, and Response in Digital Platforms: An Empirical Investigation of Online Management Response Strategies,” Information Systems Research, 29(4), 849-870.
- Media Coverage: “Is Online Management Responsiveness Good for Business?” Fox School News, October 16, 2017.
- Best Paper Nomination, Conference on Information Systems and Technology (CIST), 2017.
Kumar, N., Venugopal, D., Qiu, L. and Kumar, S. 2018. “Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning,” Journal of Management Information Systems, 35(1), 350-380.
Kumar, N., Mastrangelo, C. and Montgomery, D. 2011. “Hierarchical Modeling using Generalized Linear Models,” Quality and Reliability Engineering International, 27(6): 835-42.
Kumar, N., Kennedy, K., Gildersleeve, K., Abelson, R., Mastrangelo, C.M. and Montgomery, D.C. 2006. “A Review of Yield Modeling Techniques in Semiconductor Manufacturing,” International Journal of Production Research, 44(23): 5019-36.

Kumar, N., Qiu, L. and Kumar, S. “A Hashtag is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags,” In revision for resubmission at Management Science.
Kumar, N., Venugopal, D., Qiu, L. and Kumar, S. “Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models,” Submitted to Journal of Management Information Systems.
Rezaee, Z. and Kumar, N. “Integration of Big Data Education into the Business Curriculum” (Work-In-Progress)

Kumar, N. “Using Data Analytics Techniques to Evaluate Performance,” Book Chapter in Applying Quality Management in Healthcare: A Systems Approach, by Spath, Patrice L.,‎ Kelly, Diane L., 4th Edition, Health Administration Press. 2017, Pages 167-202.
Mastrangelo, C., Kumar, N. and Forrest, D. “Hierarchical Modeling for Monitoring Defects,” Book Chapter in Frontiers in Statistical Quality Control by Lenz, Hans-Joachim; Wilrich, Peter-Theodor; Schmid, Wolfgang (Eds.), 1st Edition. January 2010, Pages 225-36.
Mastrangelo, C. and Kumar, N. “Feedback Control,” Book Chapter in Encyclopedia of Statistics in Quality and Reliability. John Wiley and Sons, Ltd. March 2008.

Microsoft Azure Research Award ($20,000.00 Credit) (Year 2017-2018)
Securing Online Review Platforms: An Anomaly Detection Framework using Advanced Machine-Learning. Funded by the Cluster to Advance Cyber Security & Testing (CAST), FedEx Institute of Technology (FIT), University of Memphis. ($12,000 per year) (Years 2015-2016, 2017-2018, 2018-2019)
Applications of Data Science Principles in Software Testing. Funded by the FedEx Corporation ($12,000 per year) (Years 2015-2016, 2017-2018)

nkchawla@uw.edu
(425) 352-5236
UW2-321