Find Academic Supervisors

David NoyceView profile

Professor

• Transportation Safety
• Traffic Operations
• Vehicle-to-Infrastructure Communication
• +5 more

David Noyce is a Professor and Executive Director of the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison's Department of Civil & Environmental Engineering. He also serves as Associate Director of the Safety Research Using Simulation (SaferSIM) Center, a University Transportation Center. His work focuses on transportation safety, traffic operations, and automated/connected vehicle systems. Key roles include leading over $35M in research since 2003 through the TOPS Lab and advancing technologies like V2V/V2I communication, winter maintenance strategies, and work zone safety. Education: PhD (Texas A&M, 1999), MS (Civil Engineering, UW-Madison, 1995), MBA (UW-Whitewater, 1994), BS (UW-Madison, 1984). Research interests span traffic control devices, driver behavior analysis, crash reconstruction, and multi-modal transportation systems. He pioneered the national implementation of flashing yellow arrow signals and developed the Wisconsin Connected and Automated Transportation Consortium. Current projects include C-V2X localization frameworks, automated vehicle safety testing, and winter maintenance efficiency. Recognized with 20+ awards, including ITE and ASCE Fellowships and the Dr. Arthur F. Hawnn Professorship. His labs offer full-scale driving simulators and field-testing capabilities for real-world validation of innovations.

• Transportation Safety
• Traffic Engineering
• Bicycle and Pedestrian Safety
• +4 more

Brendan Russo serves as an Associate Professor in the Department of Civil Engineering, Construction Management, and Environmental Engineering at Northern Arizona University, where he conducts influential research in transportation safety and traffic engineering. His work focuses on improving safety outcomes for vulnerable road users through rigorous analysis of crash data, traffic operations, and emerging mobility technologies, with significant contributions to Arizona-specific transportation challenges and national safety practices. Russo's research program centers on bicycle and pedestrian safety, crash severity analysis, and the integration of autonomous systems into transportation networks. He employs advanced methodologies including spatial analysis, statistical modeling (e.g., random parameters bivariate probit models), and observational studies to investigate traffic stress levels, intersection safety, and the impacts of infrastructure treatments. His work consistently bridges theoretical transportation engineering with practical applications for safer community design. Analysis of Russo's recent publications reveals a strong emphasis on emerging transportation technologies and their safety implications, particularly regarding autonomous delivery robots and vehicle-pedestrian interactions, while maintaining core focus on traditional safety concerns like bicycle crash frequency and severity. His research demonstrates increasing integration of spatiotemporal analysis and scenario-based testing methodologies, with a clear geographic concentration on Arizona metropolitan regions that provides valuable localized insights applicable to broader transportation contexts. No scientific awards were mentioned in the provided text. No specific information about advising responsibilities or grant funding was provided in the text, though his extensive publication record and dataset contributions indicate active research leadership. Russo collaborates within a robust research network centered on transportation safety, frequently partnering with colleagues including Gehrke, Smaglik, and Holliday on projects involving field data collection, bicycle infrastructure evaluation, and safety performance metrics. His work leverages both observational studies and simulation approaches to develop data-driven guidance for transportation practitioners, with particular attention to Arizona's unique transportation environment and metropolitan planning challenges.

Stefan LadwigView profile

Researcher

• Automated Driving
• Human-Machine Interaction (HMI)
• Traffic Psychology
• +5 more

Dr. phil. Stefan Ladwig is a researcher at the Institute for Automotive Engineering (Institut für Kraftfahrzeuge) at RWTH Aachen University, specializing in traffic psychology, user acceptance, and human-machine interaction for automated driving systems. His work addresses psychological and ergonomic aspects of emerging mobility technologies, including seating ergonomics, trust-building mechanisms, and communication strategies for autonomous delivery robots. Research Focus: Automated driving, interior design psychology, driver/passenger behavior, and safety requirements. Projects: Lead roles in UrbANT (autonomous delivery robot) and SteeringBow (dynamic driving experience). Collaborations: Affiliated with Aldenhoven Testing Center, Future Mobility Partnership e.V., and innocam.NRW. Ladwig’s recent publications examine rotated seating positions, visual dominance in speed adaptation, and trust-relevant driving scenarios, reflecting his interdisciplinary approach to optimizing human-vehicle interactions. His work spans ergonomics, energy management, and behavioral interventions to enhance safety and usability in next-generation mobility solutions.

Joyoung LeeView profile

Associate Professor

• Connected Vehicle (CV) Systems
• Traffic Management
• Autonomous Vehicle Control
• +5 more

Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.

• Vision science
• Ageing and vision
• Falls prevention
• +5 more

Dr. Alex Black is an Associate Professor in the School of Optometry & Vision Science at Queensland University of Technology (QUT), Faculty of Health. He serves as Course Coordinator for the Master of Optometry program and leads the Vision and Everyday Function research group within QUT's Centre for Vision and Eye Research. His expertise spans vision science, ageing-related vision decline, falls prevention, and driving safety. Dr. Black holds dual qualifications: a PhD in Vision Science (QUT, 2010) and a Masters of Public Health (University of Queensland, 2012). Teaching roles: Coordinates OP85 Master of Optometry program Research focus: Vision impairment impacts on mobility, driving safety, and academic performance Awards: FAAO (Fellow of American Academy of Optometry), FHEA (Fellow of Higher Education Academy) Research highlights include AUD $3.2 million in grants, over 100 publications, and contributions to international journals like Clinical & Experimental Optometry . His work bridges clinical practice and research, addressing real-world issues through innovative studies such as night-time pedestrian safety clothing design and advanced driver assistance system (ADAS) usability for older adults. Key collaborations include NHMRC-funded projects on injury prevention and Vision and Driving research laboratory studies. Dr. Black also serves editorial roles for Clinical & Experimental Optometry and peer review activities.

• Automated Transportation Systems
• Connected Vehicles
• Electric Vehicles
• +3 more

Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.

• Structural Health Monitoring
• Computer Vision
• Artificial Intelligence
• +3 more

Roya Nasimi, Ph.D., is an Assistant Professor in the Department of Engineering at California State University, East Bay, where she joined in Fall 2023. Her expertise spans structural engineering, computer vision, and artificial intelligence, with a focus on developing innovative solutions for infrastructure monitoring and safety. Dr. Nasimi's educational background includes: Ph.D. with distinction in Structural Engineering from the University of New Mexico Master’s degree in Structural Engineering from the University of Tabriz Bachelor’s degree in Civil Engineering from the University of Tabriz Her research focuses on structural health monitoring using advanced technologies. She integrates computer vision , artificial intelligence , and machine learning to develop systems for monitoring aging infrastructure, particularly bridges. Her work includes designing low-cost and high-end sensor systems, conducting full-scale bridge experiments, and collaborating on interdisciplinary projects to enhance infrastructure safety and resilience. Her recent publications (2021-2025) demonstrate a strong emphasis on non-contact monitoring techniques using drones, lasers, and computer vision. Key trends include the application of deep learning for displacement measurement, digital twinning for infrastructure, and rockfall prevention through machine learning. Her work bridges civil engineering with cutting-edge technology to address critical infrastructure challenges. Dr. Nasimi's research is supported by multiple grants: U.S. Army Corps of Engineers Transportation Research Board (TRB) Transportation Consortium of South-Central States (Tran-SET) New Mexico Consortium She serves on two TRB standing committees and mentors students in structural health monitoring and infrastructure technology. Dr. Nasimi leads interdisciplinary research teams focused on infrastructure monitoring, utilizing drones, lasers, and computer vision systems. Her work involves field experiments on bridges and rail systems, often in collaboration with government agencies and research consortia.

• Intelligent Transportation Systems
• Automated Vehicles
• Control Systems
• +6 more

Lingxi Li is a Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University's Indianapolis campus. His research focuses on modeling complex systems, connected and automated vehicles, intelligent transportation systems, and parallel intelligence. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008), and master's and bachelor's degrees from the Chinese Academy of Sciences (2003) and Tsinghua University (2000). Research Interests: Dr. Li's work bridges control systems, transportation engineering, and AI, with emphasis on human-machine interaction, autonomous vehicle systems, and scenario-based traffic modeling. His projects include developing frameworks for Industry 5.0 collaboration, enhancing traffic flow prediction through parallel learning, and advancing safety in micro-mobility systems like e-scooters. Recent Publications: Over 15+ articles (2023-2025) explore topics such as game-theoretic vehicle interaction modeling, vision-language systems for autonomous driving, and acoustic SLAM technologies. These studies reflect a focus on real-world validation and system integration in smart transportation. Labs & Initiatives: Leads research in autonomous mining systems and scenario engineering for intelligent vehicles, leveraging parallel intelligence concepts. Collaborates on projects like ParallelWorkforce (Industry 5.0 frameworks) and SceNDD++ (naturalistic driving datasets).

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +7 more

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

Jonathan RegehrView profile

Professor

• Transportation Information Systems
• Freight Transportation
• Railway Engineering
• +2 more

Jonathan Regehr is Professor and Associate Head (Undergraduate) in Civil Engineering at University of Manitoba's Price Faculty of Engineering. He holds BSc and PhD degrees in Civil Engineering from University of Manitoba. His research examines transportation information systems, freight logistics, railway operations, and road safety. As Co-Director of Urban Mobility and Transportation Informatics Group, he collaborates with government and industry partners to develop data-driven solutions for transportation policy challenges. Recent publications focus on freight system resilience, rail corrugation management, and traffic monitoring innovations. His work frequently addresses cold-region transportation challenges and has been recognized with nominations for Transportation Research Board awards. Professor Regehr serves as Vice-President of Heavy Vehicle Transport and Technology Forum and actively contributes to transportation policy development through committee leadership.

Lee S FriedmanView profile

Research Professor

• Injury Prevention
• Occupational Health
• Violence Prevention
• +8 more

Lee S Friedman, PhD , is a Research Professor in the Department of Environmental and Occupational Health Sciences at the School of Public Health, University of Illinois at Chicago (UIC). His research is centered on three principal areas: injury prevention in the workplace, violence across the lifespan, and the role of conflicts of interest in public health research. He employs advanced methodologies, specializing in the analysis of large population-based datasets, longitudinal cohorts, and data linkage from multi-center projects. His work on injury prevention uniquely integrates risk factors from the environment, home/community, and workplace, recognizing their interconnected impact. His current research projects, funded by NIOSH-CDC, focus on improving workplace hazard reporting for precarious workers, identifying risk factors for civilian injuries during law enforcement contact, understanding the health needs of people experiencing homelessness, and examining the opioid epidemic's impact on high-risk groups. His recent publications (2023-2025) reveal a strong trend in applying epidemiological methods to pressing public health crises. Key themes include the opioid epidemic's disparities and trends, the health and safety of vulnerable workers (e.g., coal miners, farmworkers, rideshare drivers), the intersection of community violence and public health, and the refinement of surveillance systems for occupational and environmental diseases. Dr. Friedman is the Principal Investigator of the Illinois Occupational Surveillance Program, a significant NIOSH-CDC grant, highlighting his leadership in federally funded research. His research team actively investigates a broad range of issues, including occupational injury prevention and surveillance, the long-term impact of injuries on both documented and undocumented workers, the role of social support in preventing elder neglect, and the influence of financial conflicts of interest on research in environmental and occupational health.

Saidi SiuhiView profile

Associate Professor

• Transportation Engineering
• Traffic Safety
• Transportation Planning
• +3 more

Dr. Saidi Siuhi serves as an Associate Professor of Civil Engineering at South Carolina State University, where he teaches undergraduate and graduate courses while conducting research and providing institutional service across departmental and university levels. His academic credentials include: Ph.D. in Civil Engineering from the University of Nevada, Las Vegas (2009) M.Sc. in Civil Engineering from Florida State University (2006) B.Sc. in Civil Engineering from the University of Dar-es-Salaam (2003) Specializing in transportation engineering, Dr. Siuhi's research focuses on traffic safety, transportation planning, and microscopic traffic simulation. His work addresses critical transportation challenges including distracted driving/walking behaviors, traffic management during special events (notably the 2017 solar eclipse), and the application of advanced computational methods to transportation networks. He integrates emerging technologies like virtual reality, machine learning, and deep learning to develop innovative safety solutions for complex transportation systems. Analysis of his recent publications (2021-2025) reveals a strong trajectory toward computational transportation safety, with increasing emphasis on AI-driven solutions for pedestrian safety, driver behavior analysis, and infrastructure monitoring. His work consistently bridges theoretical transportation models with practical safety applications, particularly in distracted behavior analysis and event-based traffic management. Dr. Siuhi actively mentors students through senior design projects (CE 459/460) and graduate coursework, though specific advisee names aren't documented. His service contributions span departmental, college, and university committees, supporting academic operations and strategic initiatives within the engineering program.

Matthew B. PanzerView profile

Professor

• Traumatic Brain Injury
• Injury Biomechanics
• Computational Biomechanics
• +2 more

Matthew B. Panzer serves as Associate Dean for Graduate Education and Post-Doctoral Affairs at the University of Virginia's School of Engineering and Applied Sciences, holding professorships in Mechanical & Aerospace Engineering and courtesy in Biomedical Engineering. As Deputy Director of the Center for Applied Biomechanics, he leads research in impact biomechanics and injury prevention. Education: B.S. in Mechanical Engineering, University of Waterloo, 2003 M.S. in Mechanical Engineering, University of Waterloo, 2006 Ph.D. in Biomedical Engineering, Duke University, 2012 Research Focus: Panzer's work employs computational and experimental methods to investigate high-rate non-linear mechanics in tissue mechanics, impact biomechanics, vehicle crashworthiness, military blast/ballistics, and sports injury. Current projects include traumatic brain injury mechanisms in football helmet impacts, biological tissue characterization, human body model development for automotive safety, and protective system design. His approach integrates finite element modeling with experimental validation to translate biomechanical findings into clinical and safety applications. Publication Trends: Recent work (2022-2025) emphasizes brain injury metrics, sex-specific biomechanical responses, and finite element model validation across automotive, sports, and military contexts. Key themes include rotational loading effects, biofidelic model development, and injury risk function derivation, demonstrating strong interdisciplinary integration of neuroscience, engineering, and computational science. Awards: Shannon Fellow (2024-2027) Copenhaver Fellow (2023) MAE Early Career Researcher of the Year (2021, 2022) UVA Research Achievement Award (2019) MAE Young Research of the Year (2018, 2019) Grants and Teaching: Panzer has secured over 40 research grants as Principal Investigator from federal agencies and industry partners. He teaches graduate courses including MAE 6710: Finite Element Analysis (annually since 2015), MAE 7030: Injury Biomechanics, and MAE 6952: Impact Mechanics, mentoring graduate students in biomechanics research despite no specific advisees listed in source materials. Laboratory Leadership: At the Center for Applied Biomechanics, Panzer directs teams conducting experimental testing and computational modeling to advance injury mechanism understanding and develop protective technologies for automotive, sports, and military applications.

Zhou ZhouView profile

Assistant Professor

• Neurotrauma Prevention
• Biomechanical Modeling
• Medical Imaging
• +5 more

Zhou Zhou is an Assistant Professor (tenure track) in Neuronic Engineering at KTH Royal Institute of Technology's Department of Biomedical Engineering and Health Systems. He is affiliated with the Digital Futures Faculty, a cross-disciplinary research center focusing on digital technologies for societal challenges. His research focuses on neurotrauma prevention, prediction, and protection through computational modeling, machine learning, and biomechanical validation. Dr. Zhou holds a PhD from KTH (2019) and completed postdoctoral training at Stanford University, specializing in sports safety and concussion prevention. His work is supported by VINNOVA, Swedish Research Council, and others. He received the 2025 Göran Gustafsson Prize for young researchers. Research interests include in silico modeling, fluid mechanics, and safety standardization for helmets. Zhou teaches courses such as Biomechanics and Neuronics (HL2035/CH213V) and leads labs in human and animal neurotrauma modeling. He seeks postdoctoral candidates for rat neurotrauma research. Education: PhD in Biomedical Engineering, KTH (2019); Postdoc at Stanford University (Sports Safety) Key Projects: InSilicoHealth ITN, helmet evaluation via virtual testing Labs/Teams: Neuronic Engineering Division, Digital Futures Collaboratory

Zachary DoerzaphView profile

Associate Professor

• Automotive Safety
• Connected and Automated Vehicles
• Driver Behavior Analysis
• +3 more

Zachary Doerzaph is an Associate Professor in Virginia Tech’s Department of Biomedical Engineering and Mechanics, and serves as Executive Director of the Virginia Tech Transportation Institute (VTTI) and President of the Global Center for Automotive Performance and Simulation (GCAPS). His research focuses on automotive safety, connected/automated vehicles, driver behavior, and infrastructure design. He leads a multidisciplinary team addressing next-gen transportation challenges through advanced technologies like big data analytics and AI. Education : Ph.D. Industrial and Systems Engineering (2007), Virginia Tech M.S. Industrial and Systems Engineering (2004), Virginia Tech B.S. Mechanical Engineering (2001), University of Idaho Research Interests : Connected/automated vehicle systems Driver-vehicle interaction Risk prediction and mitigation Infrastructure safety design Human factors in transportation Key Contributions : Developed PREPARES rear-end collision mitigation system Advanced LiDAR/radar fusion for vehicle sensing Guided automated vehicle handover studies Testified on autonomous tech impacts to U.S. Senate (2018) Awards : Virginia Business 100 (2022) Virginia Tech Distinguished Leader in Research (2021, 2023) Labs/Initiatives : Virginia Tech Transportation Institute Global Center for Automotive Performance and Simulation