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.
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Erin Chiou, PhD student
Erin is interested in developing theory-based models in human-technology interaction, using mixed methods and interdisciplinary research to explore sociotechnical issues in health care and device design.
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.
Mahtab Ghazizadeh, PhD candidate
Mahtab's research focuses on several aspects of human factors engineering, mainly automation acceptance and modeling human-automation interaction. She uses a variety of statistical modeling techniques to model users' attitudes toward automation. Her current project involves modeling commercial drivers' acceptance of an in-vehicle feedback and monitoring system.
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Joonbum Lee, PhD candidate
JB is currently developing 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.
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Anthony McDonald, PhD candidate
Tony studies data reduction and machine learning techniques and their applications to detecting driver fatigue in both simulator and naturalistic driving scenarios. He is also interested in using text mining techniques to understand large public databases, and human performance modeling.
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Shadeequa D. Miller, PhD candidate
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.
Rashmi Payyanadan, PhD student
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.
Shannon C. Roberts, PhD candidate
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.
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Vindhya Venkatraman, PhD student
Vindhya's research centers on information exchange and facilitation between human users and systems using human-computer collaboration models, such as mixed initiative interaction. She is interested in developing interaction models and applying these to in-vehicle information systems.
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Alumni
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. Her current research investigates the influence of automation transparency and capability on unmanned vehicle operators’ trust and reliance.