Find Academic Supervisors

• Traffic Data Acquisition
• Traffic Data Analysis
• Video-Based Microscopic Traffic Flow Simulation
• +2 more

Dr. Adrian Fazekas is a Lecturer at the Institute of Highway Engineering, RWTH Aachen University, and collaborates with the Federal Highway Research Institute (BASt). He holds a Dr.-Ing. in Computer Science from RWTH Aachen (2005–2011), specializing in Media Engineering. His professional trajectory includes roles as a Research Assistant at RWTH Aachen and industry experience as a Software Developer at Continental AG. Research interests focus on traffic data acquisition , microscopic traffic flow simulation , and intelligent transportation systems . Key projects include: DROVA: Drone-based traffic analysis for infrastructure optimization ESIMAS: Real-time tunnel safety management Digital Twin Road: Physical-informational mapping of future highways AUTUKAR: Automated tunnel monitoring systems His publications emphasize real-time traffic detection , safety analytics , and data-driven modeling , with recent work exploring thermal-camera nudging systems and weigh-in-motion accuracy. He actively contributes to the Research Association for Roads, Earth and Tunneling (SETAC). No awards or student advising roles are documented.

Yasir AliView profile

Senior Lecturer

• Transport Engineering
• Traffic Safety
• Machine Learning
• +3 more

Dr. Yasir Ali is a Senior Lecturer specializing in Cooperative Intelligent Transport Systems, focusing on traffic flow modelling, traffic safety, and advanced data analysis techniques in transport engineering. His research integrates machine learning and econometrics to address challenges in connected/automated vehicles, mixed traffic dynamics, and vulnerable road user safety. He explores innovations like AI-based video analytics for next-gen traffic signals and real-time risk assessment systems. Education: Bachelor of Engineering (BE) Master of Science (MSc) Doctor of Philosophy (PhD) Research Interests: Traffic flow and safety in mixed vehicle environments Data-driven decision-making using machine learning Impact of connected/automated vehicles on traffic dynamics Innovative solutions for pedestrian and cyclist safety Key Projects: Real-time risk assessment systems for vulnerable road users AI-based traffic signal optimization for green intersections Modelling autonomous vehicle interactions in mixed traffic Notable Contributions: Advances in extreme value theory for crash risk estimation Development of safety-field theories for pedestrian risk assessment Analysis of distracted driving behavior in connected environments

Pan HeView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Artificial Intelligence
• +12 more

Pan He is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on developing deep learning techniques for real-time AI applications in infrastructure systems, including computer vision, 3D point cloud processing, and intelligent transportation. Education: Ph.D. in Computer Science, University of Florida B.E. in Software Engineering, Sichuan University (2015) His research interests include deep learning, computer vision, LiDAR, 3D perception, and adversarial machine learning. He develops AI techniques to empower downstream applications such as traffic signal control, anomaly detection, and infrastructure monitoring. Much of his work involves unsupervised and self-supervised learning on spatiotemporal data. His recent publications span top venues like TPAMI, ICLR, NeurIPS, CVPR, and IEEE T-ITS, focusing on topics such as explainable video anomaly detection, backdoor attacks in 3D detection, and efficient LiDAR annotation. These works demonstrate a strong trend toward robust, real-time, and interpretable AI systems for real-world infrastructure. Scientific Awards: 2025 ORAU Ralph E. Powe Junior Faculty Enhancement Award CVPR'22 and ICCV'21 Doctoral Consortium Awards Multiple Gartner Group Graduate Fellowships (2020–2022) Dean’s Award for Innovation and Creativity (SIAT, 2016) Best Bachelor Thesis Award (Sichuan University, 2015) Pan He advises students who have earned recognitions such as the CRA Outstanding Undergraduate Researcher Honorable Mention and the Wolotsz Graduate Fellowship. He has secured research grants and is actively recruiting PhD, Master’s, and undergraduate students. He has served as an Associate Editor for IEEE TNNLS and has taught courses on deep learning and computer vision. He leads a research group focused on foundational AI for pervasive computing and infrastructure intelligence.

• Traffic Conflict Analysis
• Connected and Automated Vehicles
• Human Factors and Driving Behaviour
• +5 more

Professor Md. Mazharul Haque is a distinguished academic and researcher in transportation engineering at Queensland University of Technology (QUT), Australia. He serves as Head of the School of Civil & Environmental Engineering and holds a Professorial rank. His expertise spans econometrics, artificial intelligence, and traffic safety. He has secured over $3 million in research funding, published over 110 peer-reviewed articles, and achieved an h-index of 27 (Scopus) and 33 (Google Scholar). His work is recognized through prestigious roles like Associate Editor of Accident Analysis & Prevention and ASCE Journal of Transportation Engineering . Research focuses include Traffic Conflict Analysis, Connected and Automated Vehicles (CAVs), Human Factors in Driving, and Black Spot Identification. He co-founded the startup Advanced Mobility Analytics Group (AMAG), developing AI-driven road safety tools. Awards include the TRB Outstanding Paper Award (2020, 2016) and the John Kirby Award (2014). He has advised multiple PhD students on topics like CAV integration, safety modeling, and traffic flow optimization. His grants address real-time risk assessment, safety evaluation of traffic signals, and CAV behavioral impacts. Education includes a PhD from the National University of Singapore (2005–2009) and degrees from Bangladesh University of Engineering and Technology (BUET). Professional memberships include TRB Committees, ASCE, and editorial roles in leading journals. Key contributions include pioneering AI-based video analytics for crash risk forecasting and advancing econometric models for transportation systems.

Armin SeyfriedView profile

Professor

• Pedestrian Dynamics
• Crowd Modeling
• Traffic Flow
• +5 more

Professor Armin Seyfried serves as Director of the Civil Safety Research division (IAS-7) at the Institute for Advanced Simulation within Forschungszentrum Jülich and holds a professorship at the University of Wuppertal where he leads the teaching and research area 'Computer Simulation for Fire Safety and Pedestrian Traffic.' His career bridges theoretical physics with practical safety applications, having established the 'Civil Security and Traffic' department at Jülich Supercomputing Centre in 2004, which evolved into his current division. Dr. Seyfried's research program focuses on pedestrian dynamics, crowd modeling, and traffic flow with significant applications in security and safety research. His interdisciplinary approach combines physics, computer science, psychology, and safety engineering to understand how collective phenomena emerge from individual behaviors in crowds. Key research areas include bottleneck dynamics, crowd safety at major events, pedestrian flow characteristics, and the development of simulation frameworks like JuPedSim. His extensive publication record reflects evolving research trends from fundamental pedestrian flow studies to cutting-edge AI applications in crowd analysis. Recent work emphasizes machine learning for pushing behavior detection, microscopic analysis of pedestrian movement, and interdisciplinary approaches that integrate physical and socio-psychological factors in crowd dynamics. This progression demonstrates his commitment to addressing increasingly complex safety challenges with innovative methodologies. Director of Civil Safety Research (IAS-7) at Forschungszentrum Jülich Professor at University of Wuppertal specializing in pedestrian traffic simulation Leader of research on crowd safety for major events including UEFA EURO 2024 Developer of simulation frameworks and analysis tools for pedestrian dynamics Organizer of international conferences on traffic and granular flow Professor Seyfried's work bridges theoretical understanding with practical safety applications through extensive collaboration with event organizers, architects, and safety authorities. His research group conducts laboratory experiments, field studies, and develops computational models to improve crowd management practices worldwide. Current projects focus on AI-driven crowd analysis, real-time safety monitoring, and establishing data standards for pedestrian dynamics research. His laboratory investigations of pedestrian movement patterns, combined with real-world applications at major public events, have established him as a leading authority in crowd safety research. The team's work on identifying dangerous crowd conditions before they escalate represents a significant advancement in preventive safety measures for mass gatherings.