Soroush Korivand

Southern Methodist University, Mississippi State University

Papers

Total Citations

H-Index

About

Soroush Korivand is a rising researcher at the intersection of human factors engineering and artificial intelligence, whose work is shaping the future of Industry 4.0 and 5.0. His primary research areas focus on optimizing human-robot teaming (HRT) performance, with a particular emphasis on adaptive task load management and physiological data analysis. Korivand’s major contributions include pioneering the use of Q-learning and reinforcement learning algorithms to dynamically adjust task loads in manufacturing environments, thereby mitigating the negative effects of both high stress and low engagement on human performance. His most cited work, “Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis” (2024), has garnered 9 citations and introduces a novel framework that integrates eye movement analysis for real-time performance prediction. Additionally, his exploration of teleoperated communication robots for law enforcement, published in 2024, extends his expertise into public safety, aiming to enhance the safety of first responders during high-risk interactions. With a growing citation record and a clear trajectory toward practical, human-centered automation, Korivand is establishing himself as a key voice in the development of intelligent, adaptive human-robot systems.