Subodha Kumar
Statistics, Operations, and Data Science
Paul R. Anderson Distinguished Chair Professor
  • Website Visit
  • Titles & RolesDirector, Center for Data Analytics

Biography

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science and the Founding Director of the Center for Business Analytics and Disruptive Technologies at Temple University’s Fox School of Business. He has a secondary appointment in Information Systems. He also serves as the Concentration Director for Ph.D. Program in Operations and Supply Chain Management.

Prof. Kumar has been awarded a Changjiang Scholars Chair Professorship by the China’s Ministry of Education. He is also a Visiting Professor at the Indian School of Business (ISB), and he previously served on the faculty of University of Washington and Texas A&M University.

Prof. Kumar has received numerous other research and teaching awards and has published more than 185 papers in reputed journals and refereed conferences. In 2019, he was elected to become a Production and Operations Management Society (POMS) Fellow. He was ranked #1 worldwide for publishing in Information Systems Research. In addition, he has authored books, book chapters, Harvard Business School cases, and Ivey Business School cases. He is routinely cited in different media outlets including NBC, CBS, Fox, Business Week, and New York Post.

He is the Deputy Editor of Production and Operations Management Journal and the Founding Executive Editor of Management and Business Review (MBR), among other editorial boards. He was the conference chair for POMS 2018 and DSI 2018, and he has co-chaired several other conferences.

More details are available at: https://sites.temple.edu/subodha/

Research Interests

Artificial intelligence, Machine learning, Blockchain, Fintech, Supply chain analytics, Healthcare analytics, Social media analytics, Retail analytics, Digital marketing, Cybersecurity, Econometric and Analytical modeling, Software management, Project management, Scheduling, Combinatorial optimization, Data mining

Knowledge Hub

Media Mentions