Dr. Erika Gallegos is an Associate Professor in the Systems Engineering Department at Colorado State University.
Her research is centered on integrating humans with complex systems to enhance safety, performance, and sustainability in the design and evaluation of new and existing infrastructure. Dr. Gallegos' work focuses on modeling human behavior and cognitive workload over time to evaluate the interactions between humans and machines, with an emphasis on developing appropriate trust, maintaining situational awareness, and enhancing decision-making. She applies advanced machine learning and artificial intelligence techniques - including deep learning, predictive analytics, computer vision, and AI-driven decision support - to analyze and predict human behavior and system performance.
Her research is primarily applied to the transportation domain. Current focuses include integrating smart technology (e.g., connected vehicles, connected infrastructure, autonomous vehicles) into the transportation system while facilitating safety for all users (e.g., automated, non-automated, motorized, non-motorized), as well as enhancing vehicle electrification through equitable and sustainable end-user solutions.
Her education and research backgrounds are in civil engineering, industrial and systems engineering, transportation engineering, human factors, and data analytics. She received her B.S. from Oregon State University and her M.S. and Ph.D. from the University of Washington.
Dr. Erika Gallegos is an Associate Professor in the Systems Engineering Department at Colorado State University.
Her research is centered on integrating humans with complex systems to enhance safety, performance, and sustainability in the design and evaluation of new and existing infrastructure. Dr. Gallegos' work focuses on modeling human behavior and cognitive workload over time to evaluate the interactions between humans and machines, with an emphasis on developing appropriate trust, maintaining situational awareness, and enhancing decision-making. She applies advanced machine learning and artificial intelligence techniques - including deep learning, predictive analytics, computer vision, and AI-driven decision support - to analyze and predict human behavior and system performance.
Her research is primarily applied to the transportation domain. Current focuses include integrating smart technology (e.g., connected vehicles, connected infrastructure, autonomous vehicles) into the transportation system while facilitating safety for all users (e.g., automated, non-automated, motorized, non-motorized), as well as enhancing vehicle electrification through equitable and sustainable end-user solutions.
Her education and research backgrounds are in civil engineering, industrial and systems engineering, transportation engineering, human factors, and data analytics. She received her B.S. from Oregon State University and her M.S. and Ph.D. from the University of Washington.