Professor John D. Lee
John's research focuses on the safety and acceptance of complex human-machine systems by considering how technology mediates attention. This research is grounded in conceptual and computational models of human-technology interaction. Applications include trust in technology, tele-health, advanced driver assistance systems, and driver distraction.
Erin Chiou, PhD candidate
Erin's research focuses on human-automation interaction and the role of cooperation in system resilience. Her broader interests include trust in technology, device design, and the impact of technology in healthcare related work systems.
Patricia Ferrara, PhD candidate
Pat investigates worker interaction with automation in radically changing work systems with particular focus on industrially developing regions. She is creating a model linking technology adoption to organizational change that will guide in learning interventions and she will apply the model to poultry growers in an integrated production system in the north of Mozambique.
Lorelei Grepo, PhD student
Lo’s research focuses on data visualization and interface design of mobile health applications to facilitate more effective information processing, support decision making and improve overall health management among users. She is interested in general applications of cognitive engineering, human computer interaction and statistical analysis techniques to system design.
Ja Young Lee, PhD candidate
Ja Young is interested in investigating how people interleave multiple tasks, and how interface design can help efficient interaction between the system and the user. She is currently using symbolic structures and algorithmic matching processes to develop a computational model of drivers' strategic task switching behaviors.
Rashmi Payyanadan, PhD candidate
Rashmi studies safe driving behavior of older adults when developing Advanced Driver Assistance Systems to support them. The methodology to assess safety implications of ADAS (Advanced Driver Assistance Systems) have not yet been developed, but these support systems need to be designed such that they do not produce negative side effects for older adults. While not much research has been done to understand user acceptance and effects of ADAS technologies on road user behavior; there are concerns on whether these systems will be used by older drivers and in fact improve road safety. As ADAS technologies continue to advance, her research work is to explore opportunities for older drivers to prolong their mobility by assessing and improving their use of ADAS technologies.
Morgan Price, PhD student
Morgan’s interests are in modeling driver's behavioral adaptation to varying levels of automation as well as developing coordinative mechanisms by which highly automated vehicles can convey capability and establish trust with drivers.
Vindhya Venkatraman, PhD candidate
Vindhya’s research interests center on driver-vehicle interaction. In this context, she applies perceptual and behavioral theories to computation models of crash-imminent interaction between safety systems and drivers. She is also interested in driver interaction with automated vehicles, and in particular, collaboration techniques for such interaction. In the past, she has been involved in studies of driver distraction with visual, manual, and cognitive in-vehicle systems.
Joonbum Lee, PhD
JB developed a computational model to evaluate potential distraction for in-vehicle devices. He is interested in how symbolic top-down process coordinates with bottom-up process in visual attention. He has been working on combining drivers' expectation and visual salience on the interface.
Anthony McDonald, PhD
Tony studies applied machine learning with a focus on detecting driver impairment and understanding driving behavior. Recently he has been developing algorithms for detecting and differentiating driver distraction, drowsiness, and alcohol impairment. In his spare time Tony investigates the links between sleep apnea and driving patterns, evaluates the potential of symbolic time-series analysis for identifying driving events, and conducts text mining analyses of large public databases.
Shadeequa D. Miller, PhD
Dee is currently conducting research to identify and understand privacy management and information-sharing behaviors of older adults as well as their informal caregivers, with the aim of guiding the design of information presentation and privacy control on user interfaces for consumer products. She is particularly interested in identifying ways to improve information management and presentation that motivate people to behave in a healthy manner.
Bobbie D. Seppelt, Post-Doc
Bobbie D. Seppelt’s research focused on operator performance and behavior in use of complex automation systems, with the aim of promoting collaborative, productive human-automation interaction as mediated through provided information displays. In the design and evaluation of interfaces, she has applied multiple theoretical approaches including Ecological Interface Design, Cognitive Work Analysis, Representation Aiding, and Multiple Resource Theory. Her expertise includes interface design for driver support systems, evaluation of operator trust and reliance on automation for supervisory control tasks, and application of ecological frameworks to interface design.
Mai Lee Chang, M.S.
Mai Lee's research interest centered on human-automation interaction, specifically trust and reliance.
Mahtab Ghazizadeh, PhD
Mahtab's research focuses on several aspects of human factors engineering, mainly technology acceptance and modeling human-technology interaction. She uses a variety of statistical modeling techniques to model users' attitudes toward technology. Her current project involves modeling older adults' acceptance of Elder Tree, a community-based information and communication technology (ICT) that enhances aging in place.
Shannon C. Roberts, PhD
Shannon's research focuses on giving drivers feedback on their performance in hopes of raising their awareness of the consequences of their actions and inducing a positive behavior change. This can be achieved by giving drivers information of their peers driving behavior, thereby enforcing social norm conformance.