جستجوی استاد راهنمای دانشگاهی

Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.

• Interaction with Automation
• Interaction Design Research
• Human Robot Interaction
• +۲ مورد دیگر

Terry C. Lansdown is an Associate Professor in the School of Social Sciences and Department of Psychology at Heriot-Watt University. His expertise lies in Human Factors, particularly focusing on driver safety, workload management, and attentional distraction in transportation contexts. Prior to his academic role, he worked as a senior researcher at the Transport Research Laboratory, contributing to numerous EU-funded and government-sponsored projects. His research integrates ergonomics principles with real-world applications, addressing issues like automated driving systems, social distractions in vehicles, and occupational health in construction industries. Key research interests include: Driver behavior and road safety Human factors in automated systems Workload and attentional demands in complex tasks Health and safety in small-to-medium enterprises (SMEs) He has led studies on pandemic response strategies, such as the CHARIS project analyzing behavioral adherence during the COVID-19 crisis. Lansdown’s work frequently bridges psychological theory with practical interventions, including text messaging campaigns to improve construction worker safety and experimental designs to evaluate automation failure responses. Collaborations span academic and industrial sectors, with publications addressing topics ranging from UV exposure in outdoor workers to the cognitive impacts of in-vehicle distractions. His contributions align with UN Sustainable Development Goals related to health (SDG 3) and sustainable cities (SDG 11).

• Human Factors
• Transport Safety
• Driver Behavior
• +۷ مورد دیگر

Jeff Moher is an Associate Professor of Psychology and Co-Chair of the Department of Psychology at Connecticut College, where he has been teaching since 2017. He also serves as the Data, Information, and Society Pathway Co-Coordinator, demonstrating his leadership within the institution. His educational background includes a Ph.D. and M.A. from Johns Hopkins University and a B.S. from the University of Michigan. This strong academic foundation has prepared him for his research and teaching career in cognitive psychology. Moher's research focuses on cognitive psychology and cognitive neuroscience, particularly visual attention and cognition in action. His work investigates why distractions occur, when they are likely to arise, and what mechanisms humans can harness to avoid them. He has found that humans employ various cognitive mechanisms to minimize distractions based on explicit knowledge, task goals, object properties, and recent experience, though he has also discovered surprising limitations in attentional selection. His research is particularly relevant given that over 3,000 people are killed annually in the United States from distracted driving. His recent publications show a consistent trajectory examining attentional mechanisms, distraction, and visual processing across different contexts. His work frequently employs sophisticated methodologies to measure both cognitive and motor responses to distractions, revealing how seemingly minor distractions can accumulate to induce serious performance costs. Moher's research is currently funded by grants from the National Science Foundation and the National Institutes of Health, indicating the significance and quality of his work. He likely mentors numerous students through the department's research groups, contributing to their development as researchers. He directs the CAMELab (Center for Attention, Movement, and Embodied Learning), which utilizes multiple methodologies including eye-tracking, electroencephalography, three-dimensional reach-tracking, and psychophysics to approach questions about attention and distraction. Current projects in his lab explore why salient distractors cause people to give up quickly during visual search, the brain mechanisms involved in learning to ignore distractions, how internal distractions impact physical interactions with the world, and how hand movement paths reveal information about attentional processes.

• Cognitive psychology
• Cognitive neuroscience
• Visual attention
• +۱ مورد دیگر

Niels Henze is a Professor at the Chair of Media Informatics within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg, where he has been serving since May 2018. His research centers on human-computer interaction, with a strong focus on predictive models in interactive systems, mobile interaction, augmented and virtual reality, and attention-aware computing. His research interests include: Human-Computer Interaction (HCI) Predictive modeling for runtime adaptation Mobile and wearable interaction Augmented and Virtual Reality (AR/VR) Attention-aware and context-sensitive systems Sociocognitive aspects of interactive technologies The analysis of his recent publications reveals a consistent focus on leveraging user behavior and contextual cues to build intelligent, adaptive interfaces. His work integrates machine learning with interaction design, emphasizing real-time model updates, implicit feedback, and cognitive load awareness to improve usability and user experience across mobile and immersive platforms. Scientific awards: No awards mentioned in the provided text. Niels Henze leads research in adaptive interactive systems and advises students in the field of media informatics. He has previously held a junior professorship at the University of Stuttgart and completed his doctorate at the University of Oldenburg under Susanne Boll. He is actively involved in advancing the theoretical and practical foundations of predictive and attention-aware computing. Grants and funding sources are not specified in the text. He is associated with the Institute for Information and Media, Language and Culture (I:IMSK) at the University of Regensburg, contributing to a multidisciplinary environment that bridges informatics with cultural and linguistic studies.

• Human-Computer Interaction
• Predictive Models in Interactive Systems
• Mobile Human-Computer Interaction
• +۵ مورد دیگر

Wang Jianmin serves as Professor and Doctoral Supervisor at Tongji University's School of Art and Media, concurrently holding the position of Vice Dean since 2014. With a computer science PhD from Sun Yat-sen University, he bridges engineering and media arts through pioneering research in intelligent communication systems and digital media interfaces. His work focuses on human-centered design for emerging technologies, particularly in automotive and virtual environments. His academic foundation includes: PhD in Engineering (Computer Software and Theory), Sun Yat-sen University (2003) Master's in Computational Mathematics, Sun Yat-sen University (1999) Bachelor's in Computational Mathematics, Nankai University (1996) Professor Wang's research centers on intelligent communication systems and digital media art, with significant contributions to automotive human-machine interfaces (HMI), virtual reality applications, and user experience methodologies. His investigations into driver-robot transparency, augmented reality navigation, and mixed-reality educational platforms demonstrate interdisciplinary innovation connecting computer science, cognitive psychology, and design theory. Current projects explore AI-driven media systems for urban environments and safety-critical interaction frameworks. Analysis of his recent publications reveals a cohesive research trajectory focusing on automotive HMI (40% of output), human-robot interaction (30%), and mixed reality applications (30%). His work consistently emphasizes experimental validation through driving simulators and user studies, yielding practical design guidelines for industry implementation. The interdisciplinary nature spans computer science, cognitive ergonomics, and media studies, with increasing emphasis on AI integration in communication systems. His scientific recognition includes national and provincial awards for innovation in human-computer interaction and educational technology: 2019 China Industry-University-Research Innovation Award for automotive HMI systems 2020 China User Experience Alliance Excellence Award 2012 Guangdong Dingying Science and Technology Award Multiple national/provincial science progress awards (2001-2009) 2020 Tongji University Teaching Achievement Award for curriculum development As an educator, Professor Wang mentors graduate students in national design competitions including the 'Core Cup' Future Automotive HMI Challenge and International User Experience Innovation Competition. His research program is supported by substantial funding from diverse sources: National Grants: National Natural Science Foundation projects on driver behavior modeling and cognitive testing Ministry of Education: 15+产学合作 projects for virtual simulation labs and curriculum development Shanghai Municipal: Publicity Department funding for smart city media research Industry Partnerships: Huawei (intelligent vehicle HMI), SAIC Motor (AR-HUD design), and automotive electronics firms He directs Tongji's Media Experiment and Practice Teaching Center and the All-Media Research Institute, leading teams developing virtual simulation platforms for emergency news reporting, intelligent vehicle interaction testing systems, and mixed reality educational tools. Current initiatives focus on AI-enhanced media art for urban applications and next-generation HMI frameworks for autonomous mobility solutions.

• Journalism and Communication
• Intelligent Communication
• Digital Media Art
• +۶ مورد دیگر

Qiaoning Carol Zhang serves as Assistant Professor of Human Systems Engineering within The Polytechnic School at Arizona State University's Ira A. Fulton Schools of Engineering. Her research investigates the critical intersection of human perception, social contexts, and emerging technologies including artificial intelligence, robotics, and automated vehicles, with emphasis on creating intuitive, user-friendly, and inclusive systems. Her academic foundation includes: Ph.D. in Information, University of Michigan (2023) M.S. in Industrial and Operations Engineering, University of Michigan (2018) B.S. in Industrial Engineering, Hunan University (2016) Dr. Zhang's research program centers on understanding how individual differences and social dynamics shape technology interactions. Key focus areas include Human-AI Collaboration , Human-Robot Interaction , Human Factors in Automated Vehicles , and User Experience Design . Her work employs interdisciplinary methodologies to ensure technology adapts to diverse user needs across complex socio-technical environments, particularly in transportation and healthcare robotics. Analysis of her 15 most recent publications (2021-2025) reveals dominant themes in trust dynamics within automated vehicles, with significant attention to explainable AI interfaces. Research consistently examines how voice characteristics (gender, similarity), explanation modalities, and individual differences (age, personality) impact cognitive and affective trust. Recent work extends to healthcare robotics for elderly populations using Kano model analysis to identify critical user requirements. No scientific awards are documented in the provided materials. Dr. Zhang actively recruits Ph.D. candidates and undergraduate/master's researchers with backgrounds in human-computer interaction, data science, and interdisciplinary fields (design, computer science, cognitive science). She emphasizes opportunities in transportation technology, healthcare robotics, AI, and UX research/design, requiring applicants to submit CVs, research statements, and representative work samples. While specific grants aren't detailed, her research scope indicates substantial funding in human factors and emerging technology domains. Her research team focuses on developing empathetic technology through projects examining trust calibration in automated vehicles and healthcare robot design for older adults. Current initiatives include voice interface optimization for diverse user groups and Kano model applications in home healthcare robotics, aiming to bridge technical capabilities with human-centered design principles.

• Human-AI Collaboration
• Human-Robot Interaction
• Human Factors in Automated Vehicles
• +۴ مورد دیگر

Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.

• Affective Computing
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Associate Professor Jason Thompson holds an Associate Professor position in the Department of Psychiatry at the University of Melbourne. He is affiliated with the Faculty of Medicine, Dentistry and Health Sciences and previously served as Co-Director of the Transport, Health and Urban Systems (THUS) Research Laboratory at the Melbourne School of Design. He earned a PhD in Medicine (2015) from Deakin University, a Master's in Clinical Psychology, and a Bachelor of Science with Honours. His research focuses on computational social science applied to injury rehabilitation, compensation systems, and healthcare design. He has attracted over $5M in research funding and published over 100 articles. Key areas include agent-based modeling, systems dynamics, and policy analysis for public health challenges such as pandemic response and urban mobility. Thompson currently leads the NHMRC Centre of Excellence in Compensable Injury after Road Crashes. Grants: ARC Future Fellowship (2022), DECRA (2017) Awards: Best Paper Award (Computational Social Science Society of the Americas, 2017) Labs: THUS Research Lab (until 2024) His work bridges epidemiological modeling (e.g., influencing Victoria's 2020 pandemic exit strategy) and complex systems analysis for injury prevention and health system design.

• Computational Social Science
• Public Health
• Injury Prevention
• +۴ مورد دیگر

Dr. Otto Lappi is a Research Fellow at the University of Helsinki's Department of Digital Humanities, Faculty of Arts. His work bridges cognitive science and human performance in complex dynamic tasks, particularly focusing on predictive processing in visuomotor control. PhD in Cognitive Science Title of Docent at University of Helsinki Academy Research Fellow (2020-2025) His research explores: Cognitive mechanisms in high-speed sports and driving Predictive gaze strategies and steering control Flow states in skill acquisition VR and eye-tracking applications in archaeomusicology Neural substrates of domain expertise Recent publications analyze: Flow-state dynamics in visuomotor learning Predictive gaze behavior in automated driving Computational models of attention in distracted driving Chunking mechanisms in high-speed sports performance Scientific contributions include: Academy Research Fellow (Research Council of Finland) Editorial work on high-performance cognition Organizing cognitive science workshops As doctoral supervisor, he has guided: Samuel Tuhkanen (Predictive Gaze and Steering) Jesse Kuokkanen (Mechanistic Computationalism) Ida Maasalo (Driver Behavior) Markus Mattsson (Psychometrics)

• Cognitive Science
• Human Performance
• Gaze Control
• +۳ مورد دیگر

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

• Trustworthy Machine Learning
• Transportation Safety
• Transportation Equity
• +۴ مورد دیگر

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +۳ مورد دیگر

Niklas Grabbe is a postdoctoral researcher at the Chair of Ergonomics, Technical University of Munich (TUM), where he leads the research group on "Automated Driving and Mobility Systems" and is establishing a new working group focused on "Modelling complex socio-technical systems" with emphasis on Resilience Engineering and the Functional Resonance Analysis Method (FRAM). His research centers on human factors in automated and teleoperated driving, mobility systems, and behavior modeling. He applies systems engineering principles to analyze safety and usability, particularly using FRAM to study performance variability and resilience in socio-technical systems. His work addresses critical challenges in urban automated driving, teleoperation, and multi-driver interactions through rigorous quantitative modeling and field studies. Analysis of Grabbe's publications reveals a dominant trend in applying FRAM to driving scenarios, with recurring themes of safety enhancement, human-automation interaction, and usability evaluation. His interdisciplinary work bridges transportation psychology, safety engineering, and human-computer interaction, consistently emphasizing systems thinking to solve complex problems in automated vehicle technology. Grabbe contributes to academic instruction through courses like "Modelling Complex Sociotechnical Systems" (Winter 2025/26). He operates within TUM's Chair of Ergonomics infrastructure, utilizing specialized labs including driving simulators and ergonomic mockups to support experimental research in human factors and automated systems.

• Human Factors Engineering
• Automated Driving
• Teleoperated Driving
• +۵ مورد دیگر

Bala Balasingam is a Professor at the University of Windsor's Faculty of Engineering, specializing in interdisciplinary research at the intersection of electrical engineering, human factors, and autonomous systems. His work focuses on battery management systems, cognitive load assessment, and the human aspects of automated driving. Collaborating with the Faculty of Human Kinetics, he addresses challenges related to driver distraction and vigilance in autonomous vehicles. Research interests include battery parameter estimation, real-time state-of-charge tracking, and thermal management of lithium-ion batteries. His team employs advanced methods such as electrochemical impedance spectroscopy (EIS), Kalman filtering, and machine learning for battery diagnostics. In human factors, his studies utilize eye-tracking and biometric sensors to evaluate cognitive workload during complex tasks like vehicle takeovers in automated systems. Key contributions include novel approaches to battery internal resistance estimation, optimized charging algorithms, and validation frameworks for battery management systems. His recent work explores the integration of portable devices in automated driving systems and the impact of cognitive load on driver performance. Collaborations span engineering and kinesiology domains, emphasizing practical applications in transportation and energy sectors. Labs/Teams: Leads interdisciplinary projects combining electrical engineering and human kinetics expertise, with a focus on real-world applications in automotive and industrial automation.

• Battery Management Systems
• Autonomous Vehicles
• Human Factors Engineering
• +۴ مورد دیگر

Eric Jessup is a Research Professor and Director of the Freight Policy Transportation Institute at Washington State University's School of Economic Sciences. He holds a Ph.D. in Agricultural Economics from WSU (1998), an M.S. and B.S. in Agricultural Economics from the University of Kentucky (1992, 1988). His career includes roles at Informa Economics, Albert-Ludwig-University (Germany), and American Express. His research focuses on transportation modeling, freight systems, applied econometrics, and geo-spatial analysis. Notable awards include the 2008 Transportation Research Forum Best Paper Award and recognition as an Eno Fellow (1994). Research interests emphasize optimizing freight logistics, transportation policy, and infrastructure investment. Recent work addresses pandemic impacts on global trade, truck parking safety, and rail rate dynamics. He has directed major freight policy initiatives, including the Freight Policy Transportation Institute and the PacTrans Workforce Development Institute. Over 80 publications span journals like Transportation Research Forum and Journal of the Transportation Research Forum , with a focus on GIS applications, supply chain resilience, and safety critical events. Education: Ph.D. Agricultural Economics, WSU, 1998 M.S. Agricultural Economics, University of Kentucky, 1992 B.S. Agricultural Economics, University of Kentucky, 1988 Awards: 2008 TRF Best Paper Award 2006 Best Paper Award (Italy) 1996 Northwest UTC Student of the Year 1994 Eno Transportation Fellowship Labs/Teams: Leads the Freight Policy Transportation Institute and collaborates with the Transportation Research Group at WSU. Engaged with national initiatives like PacTrans and the Idaho Statewide Freight Data Project. Grants and advising are central to his work, though specific student advisees are not listed. His research bridges academia and industry, addressing real-world challenges in freight logistics, environmental policy, and transportation safety.

• Transportation Modeling and Economics
• Freight Systems Modeling
• Applied Econometrics
• +۳ مورد دیگر

Carryl Baldwin is the Carl and Rozina Cassat Distinguished Professor of Aging & Regional Institute on Aging and Director of the Wichita Auditory Research Group (WARG) at Wichita State University's Fairmount College of Liberal Arts and Sciences. Her work spans neuroergonomics, aging research, auditory cognition, and human-vehicle interface design, with funding from NHTSA, NIH, NASA, and private industry partners. Education : PhD in Human Factors Psychology (1997), MA in Human Factors Psychology (1994), BA in Psychology/Asian Studies (1987) Dr. Baldwin's research focuses on human interaction with automation , particularly in transportation contexts. She investigates driver attention, mind wandering, cognitive workload, and multimodal alarm systems using physiological metrics like EEG. Her lab explores aging-related cognitive changes and develops technologies to enhance safety and performance in semi-autonomous systems. The 15 most recent publications reveal trends in neuroergonomics , aging populations , and automated vehicle safety . Key themes include attention management in automation, auditory alarm optimization, and sustainability applications of human factors, with methodologies spanning EEG analysis, eye-tracking, and multimodal interface evaluation. Dr. Baldwin's lab has secured external funding from the National Highway Traffic Safety Administration, NIH-NIA, NASA centers, and defense contractors like Northrop Grumman. Current research includes driver vigilance in Level 2/3 vehicles, pandemic-related social isolation in older adults, and FAA-funded training technology evaluation.

• Neuroergonomics
• Aging Studies
• Human Factors Psychology
• +۵ مورد دیگر