Ali Adeli, Assistant Professor, DEPARTMENT OF BUSINESS INFORMATION AND TECHNOLOGY

Ali Adeli

Assistant Professor, DEPARTMENT OF BUSINESS INFORMATION AND TECHNOLOGY

Phone
901.678.3729
Email
amadeli@memphis.edu
Fax
901.678.3729
Office
321 FAB
Office Hours
By Appointment

About

Ali is completing his Ph.D. in Information and Decision Sciences from the Carlson School of Management at the University of Minnesota. His research investigates the impact of emerging digital technologies on human behavior, organizations, and society, with a focus on electronic markets. The interdisciplinary nature of his research draws upon methods from machine learning, agent-based simulation, artificial intelligence, behavioral experiments, and econometrics. His research has been presented at major Information System conferences such as Institute for Operations Research and the Management Sciences (INFORMS) annual meeting, Conference on Information Systems & Technology (CIST), Workshop on Information Technologies and Systems (WITS), and Hawaii International Conference on System Sciences (HICSS).

Education

Ph.D. in Information and Decision Sciences, University of Minnesota
M.Sc. in Information Technology, University of Technology Malaysia
B.Sc. in Electrical Engineering, Semnan University

Teaching Interests

Data Analytics, Databases and Data Modeling, Data Mining, Programming, System Analysis and Design, Artificial Intelligence applications

Research Interests

Electronic markets, Intelligent Agents, Agent-based modeling and simulation, Machine Learning, Artificial Intelligence, Modeling human behavior

Selected Research

Toward understanding the dynamics of bidder behavior in continuous combinatorial auctions: agent-based simulation approach, with G. Adomavicius and A. Gupta, HICSS 2017, Computer Society Press.

Replicating human behavior in complex trading environments: an agent-based simulation approach to bidder modeling in continuous combinatorial auctions, with G. Adomavicius and A. Gupta (in progress).

Humans vs. Intelligent Agents: agent-enabled experiments to understand user behavior in complex market environments (in progress).