School of Business Faculty

Naveen Kumar

EDUCATION

  • 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

RESEARCH INTERESTS

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

ACADEMIC EXPERIENCE

  • 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

INDUSTRY EXPERIENCE

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

TEACHING EXPERIENCE

  • Machine-Learning for Business

  • Programming for Business

  • Statistics for Business

  • Digital Platform Analytics

  • Enterprise Architecture​

SELECTED RESEARCH PUBLICATION

WORKING PAPERS

BOOK CHAPTERS

  • 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.

GRANTS

  • 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)

Contact Information

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