Dr. Yanling Chang, assistant professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, has been elected a Scialog Fellow for the Research Corporation for Science Advancement (RCSA).
Created in 2010 by RCSA, the Scialog (short for science and dialog) format brings together communities of early-career scientists from multiple disciplines and institutions across the U.S. and Canada. Participation in Scialog is by invitation and is inclusive of researchers from groups underrepresented in science.
RCSA will bring together a multidisciplinary group, including Chang, of early-career researchers to address the global threat to human health from animal-borne infectious diseases as part of its new Scialog initiative, Mitigating Zoonotic Threats.
Chang’s research focuses on dynamic decision-making under imperfect information with applications in military operations, supply chain risk management, the gig economy and human-machine interaction.
Recently, her paper “Misinformation and Disinformation in Modern Warfare” appeared in Operations Research, the flagship journal in her field. This work developed a game-theoretical model to analyze the implication of distorted information in the 2016-2017 Battle of Mosul.
She also applied her methodology to understand the root cause and dynamics of mental fatigue in extended cognitive demanding tasks (e.g., health care professionals). Her article “The Effects of Mental Fatigue on Effort Allocation: Modeling and Estimation” has been accepted by Psychological Review, a top journal in theoretical psychology.
Her other works on self-scheduling businesses and critical infrastructure protections have also been published in the leading journal, IISE Transactions.
In the fall of 2021, Chang received a National Science Foundation award for her dynamic discrete choice analysis work. Often, a person driving on a busy highway also engages in secondary tasks that demand cognitive effort (e.g., attending to a business-related phone call, listening to a podcast, etc.). This research aims to provide a novel estimation method to build a predictive model of a person’s driving behavior, to monitor the driver's activities and potentially to intervene or assist in improved driving performance and safety.