Dr. Xiaowei (Tom) Shi is an Assistant Professor in Civil and Environmental Engineering at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science and serves as the Founder and Director of the Automated, Connected & Electric Mobility Systems (ACES) Lab. He holds a PhD in Transportation Engineering from the University of South Florida with additional graduate degrees from Beijing Jiaotong University. His research specializes in evaluating and developing technologies for connected/automated vehicles (CAVs), utilizing hardware-in-the-loop methodologies to advance cooperative driving automation systems. His work integrates transportation engineering, control systems, optimization, and data science. Publications demonstrate consistent focus on CAV technologies with evolving emphasis on human factors and sustainability (2021-2024). Early work centered on fundamental traffic modeling and vehicle operations, while recent publications increasingly address user acceptance, energy efficiency, and neural network applications in transportation systems. He teaches courses covering transportation systems analysis, intelligent transportation technologies, and traffic operations modeling. He maintains active participation in IEEE technical committees and TRB's Emerging Technologies in Network Modeling panel.
Dr. Juan Medina is a Research Associate Professor in the Civil & Environmental Engineering Department at the University of Utah . He holds a Ph.D. and M.S. from the University of Illinois at Urbana-Champaign, and a B.S. from Pontifical Xavierian University (Colombia). His expertise spans transportation safety , traffic control systems , Intelligent Transportation Systems (ITS) , and agent-based modeling . He leads the Utah Transportation and Public Safety - Crash Data Initiative (UTAPS-CDI) , focusing on improving highway safety through data-driven decision-making and crash information management. Education: Bachelor's in Civil Engineering, Pontifical Xavierian University (2001) Master's in Civil and Environmental Engineering, University of Illinois at Urbana-Champaign (2005) Ph.D. in Civil and Environmental Engineering, University of Illinois at Urbana-Champaign (2013) Research Interests: Dr. Medina’s work emphasizes predictive crash modeling , traffic simulation , work zone safety , and agent-based control strategies . His projects integrate machine learning, naturalistic driving data, and advanced analytics to address complex transportation challenges. He is actively involved in national conferences (TRB, IEEE, ASCE) and collaborative initiatives. Grants & Projects: UTAPS-CDI aims to establish Utah as a national hub for transportation safety research, developing tools to enhance crash data reliability and safety interventions. His work also explores microscopic traffic simulation validation , solar-powered traffic infrastructure , and LED lighting efficacy for roadways. Labs/Teams: Leads the UTAPS-CDI initiative, collaborating with state and federal agencies to advance safety research and data-driven policies.
Dr. Hanliang Guo is an Assistant Professor of Mathematics and Computer Science at Ohio Wesleyan University (OWU) , joining in 2022 after postdoctoral training at the University of Michigan. His research bridges applied mathematics, biological fluid mechanics, and scientific computing, with a focus on ciliary systems, microswimmer optimization, and fluid-structure interactions. He also serves as the department’s Industry Liaison, fostering academic-industry collaborations. Education : B.E. (Tsinghua University), M.S. and Ph.D. in Mathematics (University of Southern California, advised by Prof. Eva Kanso). Postdoctoral mentorship under Prof. Shravan Veerapaneni at the University of Michigan. Research Interests : Mathematical modeling of ciliary flows, optimal microswimmer design, fluid dynamics in biological systems, and data-driven approaches to fluid mechanics. Notable contributions include a Nature Physics paper on Stentor colonial hydrodynamics and a Journal of Fluid Mechanics study on microswimmer optimization. Awards & Grants : 2024 AMS-Simons PUI Grant. Recognized for computational tools like the Xlip MATLAB app for simulating axisymmetric microswimmers. Teaching & Mentorship : Teaches courses in calculus, linear algebra, numerical methods, and data mining. Mentored undergraduate research projects on traffic flow modeling, machine learning for microswimmer classification, and fluid dynamics in confined geometries. Software & Labs : Developed open-source Flow Simulator for traffic modeling and Xlip for microswimmer optimization. Active in collaborative projects with institutions like Tulane University and Kenyon College.
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.
Murat Bayrak is a Postdoctoral Researcher at Aalto University's School of Built Environment, specializing in transportation engineering and urban mobility optimization. He is affiliated with the Planning and Transportation research group. Research Interests: His work focuses on optimizing transportation networks through heuristic methods and machine learning algorithms. Key areas include dedicated bus lane placement, left-turn restriction strategies, transit signal priority, and queue spillback mitigation in urban grid networks. Article Trends: His publications (2018–2023) highlight advancements in intelligent transportation systems, with a consistent emphasis on network-level optimization techniques like population-based learning and heuristic algorithms. Topics span from bus lane connectivity to dynamic traffic management solutions. Research Groups: Planning and Transportation
Kshitij Jerath serves as Associate Professor in the Department of Mechanical and Industrial Engineering, Robotics at the Francis College of Engineering, University of Massachusetts Lowell. His research focuses on self-organized dynamics in complex systems, multi-agent control, and robotic swarms, with significant contributions to traffic flow theory and sensor characterization. He directs the Emergent Dynamics, Control and Analytics Labs (EXALABS), advancing bottom-up control algorithms for minimal-intervention system guidance. Dr. Jerath's academic background includes: Ph.D. in Mechanical Engineering from Pennsylvania State University (2014), dissertation: 'Influential subspaces in self-organizing multi-agent systems' M.S. in Electrical Engineering from Pennsylvania State University (2011), thesis: 'Sensor noise modeling, characterization and simulation: An Allan variance tutorial' M.S. in Mechanical Engineering from Pennsylvania State University (2010), thesis: 'Impact of adaptive cruise control on the formation of self-organized traffic jams on highways' Bachelor's equivalent in Mechanical and Automation Engineering from Amity School of Engineering and Technology, India His research spans self-organized dynamics , multi-agent systems , and robotic swarm control , applying statistical mechanics principles to model emergent behavior in transportation networks and complex systems. Current work focuses on influencing macro-scale dynamics through minimal intervention by small agent subsets, with extensions to social ensembles and neural systems. His methodologies integrate control theory, network science, and machine learning for real-world applications in autonomous vehicles and system reliability. Recent publications (2023-2025) reveal strong trends in relational network applications for multi-agent learning, adaptive data granulation techniques, and human-swarm interaction frameworks. Key developments include database-inspired algorithms for sensor characterization, renormalization group approaches to traffic modeling, and fault-tolerant recovery mechanisms for robotic teams. These works demonstrate increasing convergence of control theory, database systems, and reinforcement learning in addressing complex system challenges. Dr. Jerath has received notable recognition including: Two Best Presentation awards at American Control Conference (2014, 2012) Kulakowski Travel Award from Penn State (2014) National Merit-cum-Means Scholarship from Indian Government (2013) 2nd place in ITS America Student Essay Competition (2012) His research is supported by grants including the CPS: Medium project 'Automated Discovery of Data Validity for Safety-Critical Feedback Control in Connected Vehicles' (2019) and a Graduate Teaching Fellowship from Penn State (2013). EXALABS maintains active collaborations with transportation agencies and robotics researchers to translate theoretical advances into practical applications. The Emergent Dynamics, Control and Analytics Labs (EXALABS) develops frameworks for modeling, quantifying, and influencing collective behavior across scales. Current projects include human-guided swarm control in virtual reality, traffic flow optimization using connected vehicle networks, and adaptive granulation techniques for large-scale sensor data. The lab employs interdisciplinary approaches combining control theory, statistical mechanics, and machine learning to solve problems in robotics, transportation, and system reliability.
Ostap Okhrin serves as a Professor of Econometrics and Statistics at Dresden University of Technology, holding the Chair of Econometrics and Statistics with a special emphasis on Transportation Systems. His academic career is marked by a strong focus on methodological advancements in econometrics and statistics, applied to complex real-world problems in transportation and finance. Professor Okhrin's research interests span econometrics, statistical theory, copula modeling, time series analysis, and financial risk management. He has significantly expanded into machine learning and reinforcement learning applications for autonomous systems, with deep expertise in traffic flow modeling, autonomous driving, maritime navigation, and financial volatility estimation. His work bridges theoretical statistics with practical engineering challenges, particularly in transportation systems and risk forecasting, addressing high-dimensional data and dynamic environments through innovative methodological frameworks. Analysis of Okhrin's recent publications (2024-2025) reveals a pronounced interdisciplinary trajectory integrating reinforcement learning with transportation engineering. Key themes include drone-based trajectory data collection for traffic monitoring, algorithms for autonomous ships on inland waterways, and Sim2Real transfer frameworks for autonomous driving. Concurrently, he advances financial econometrics through high-frequency risk forecasting models incorporating realized moments. This dual focus demonstrates his ability to transfer statistical innovations across domains while maintaining rigorous theoretical foundations in copula theory and time series analysis.
Jean-Marc Lasgouttes is a Researcher at Inria Paris working within the Astra project team, a joint research initiative between Inria Paris and Valeo. He also holds a teaching position at INSA Rouen Normandie's Department of Mathematical Engineering, where he has instructed courses including Boosting methods (until 2024), Functional Data Analysis (until 2018), and general Data Analysis (until 2024) for both the Mathematical Engineering department and the Specialized Master's program in Data Science. Dr. Lasgouttes' primary research focuses on probabilistic modeling of large systems using statistical physics tools, with particular emphasis on Intelligent Transportation Systems. His work spans traffic flow modeling, vehicle platooning, urban traffic prediction, and geopositioning systems. He frequently employs Markov Random Fields, statistical physics approaches, and game theory to address complex transportation challenges, bridging theoretical statistical methods with practical applications. His research methodology often involves developing novel algorithms like the ★-IPS family for incremental GMRF estimation. Analysis of his recent publications reveals a consistent trajectory of applying advanced probabilistic models to increasingly sophisticated transportation scenarios. His work demonstrates strong expertise in spatio-temporal modeling, with publications covering car-following dynamics, landmark-based positioning, cooperative ITS, and autonomous vehicle systems. The interdisciplinary nature of his research connects statistical physics, machine learning, and transportation engineering to solve real-world mobility challenges. At INSA Rouen Normandie, Dr. Lasgouttes has developed comprehensive teaching materials for data analysis courses, including practical applications of principal component analysis and correspondence analysis using real-world datasets such as European protein consumption patterns and Titanic passenger data. His educational approach emphasizes hands-on implementation with R programming, reflecting his commitment to practical statistical applications. The Astra project team serves as Dr. Lasgouttes' primary research environment, facilitating collaboration between academic researchers and industry partners to address contemporary transportation challenges. His work has contributed to significant research events including the 2018 workshop on Large Random Networks and Constrained Walks honoring Guy Fayolle's 75th birthday, and the 2012 interdisciplinary workshop on inference associated with the Travesti ANR grant.
Xiaoyu Zheng is a Researcher at Universitat Politècnica de Catalunya (UPC) within the Department of Civil and Environmental Engineering, School of Civil Engineering. Affiliated with the BIT - Barcelona Innovative Transportation research group, Dr. Zheng focuses on advanced transportation systems and urban mobility solutions. Research interests span Transportation Engineering , Urban Mobility Systems , Autonomous Vehicle Safety , and Sustainable Transportation . Current work integrates AI methodologies including deep learning, graph neural networks, and transformer architectures to solve complex transportation challenges in rail transit, bike-sharing systems, pedestrian safety, and connected autonomous vehicles. Recent publications (2023-2025) demonstrate strong productivity with six documented outputs including four journal articles in 2025 alone. The research portfolio shows clear progression toward AI-enhanced transportation solutions with applications in urban planning and sustainable mobility. Collaborative work spans multiple departments at UPC including Statistics and Operations Research. Dr. Zheng participates in competitive R&D projects addressing transportation challenges aligned with Sustainable Development Goals 11 (Sustainable Cities) and 13 (Climate Action). Current projects focus on connected autonomous vehicle systems, urban rail transit optimization, and multimodal transportation integration. The BIT research group provides the primary laboratory environment for this work, focusing on innovative transportation solutions through interdisciplinary collaboration between engineering and data science disciplines.
Romesh Saigal is a Professor at the Department of Industrial and Operations Engineering in the College of Engineering at the University of Michigan , where he has been employed since 1986. His career spans multiple prestigious institutions, including Northwestern University, the University of California, Berkeley, and Bell Telephone Labs. Education: B.Tech (Hons), I.I.T. Kharagpur, India, 1961 M.Tech, I.I.T. Kharagpur, India, 1963 Ph.D., University of California, Berkeley, 1968 Romesh Saigal’s research focuses on stochastic programming , financial engineering , interior point methods , Kalman filtering , continuous optimization , and game theory applications . His work bridges theoretical advancements with practical applications in transportation systems and risk analysis. His recent publications highlight the application of stochastic modeling to traffic flow prediction, dynamic pricing in toll lanes, and combinatorial auction mechanisms for traffic allocation. These works demonstrate a strong emphasis on integrating optimization algorithms and data-driven approaches in operations research. Romesh Saigal has supervised several PhD students, including Li Yang , Jie Ning , Hao Zhou , and Katherina Best , whose dissertations explore topics like risk management , traffic pricing , and higher education value analysis . He is also known for authoring the book Linear Programming: A Modern Integrated Analysis and co-editing the Handbook on Semidefinite Programming . Additional contributions include foundational work on interior point methods , accessible via collaborations at Argonne National Lab. His research continues to influence both academic and industrial practices in optimization and transportation systems.
Gerald Ostermayer is a Professor at the University of Applied Sciences Upper Austria, affiliated with the Research Center Hagenberg. His research focuses on automotive/mobility engineering, traffic simulation, and vehicle communication systems. He leads projects like pDrive (platooning energy efficiency) and AutoSimAR (AR applications in automotive). He has been active in multiple research areas including surface acoustic wave technology, smart grids, and augmented reality in vehicles. Key research topics include microscopic traffic simulation, vehicular visible light communication, and security protocols for vehicle platoons. His work integrates interdisciplinary approaches across computer science, electrical engineering, and transportation systems. He has organized workshops such as the 2nd Automotive Mixed Reality Applications and contributed to conferences like IEEE WCNC. Awards include the 2020 Best Paper Award for vehicle platoon verification research. Projects like InterGrid and Localisation & Coexistence highlight his expertise in smart infrastructure and vehicular networking.
Pengbo Zhu is a researcher at the École polytechnique fédérale de Lausanne (EPFL), specifically affiliated with the Laboratory of Urban Transport Systems (LUTS) within the School of Architecture, Civil and Environmental Engineering (ENAC). His work focuses on developing innovative control algorithms to address critical challenges in urban transportation systems, particularly in the domain of Autonomous Mobility-on-Demand (AMoD) and ride-hailing services. Dr. Zhu's research interests center around vehicle repositioning strategies, hierarchical control frameworks, and data-enabled predictive control methods for optimizing urban mobility. His work bridges transportation engineering with control systems theory, developing solutions that balance passenger demand with vehicle supply in dynamic urban environments. He has made significant contributions to coverage control algorithms that align vehicle distribution with demand patterns across city districts. Analysis of his publication trends shows a consistent focus on hierarchical control approaches for vehicle repositioning, with increasing sophistication from 2022 to 2025. His research has evolved from basic coverage control methods to integrated multi-layer frameworks that combine macroscopic traffic modeling with microscopic vehicle guidance, demonstrating both theoretical depth and practical applicability in real-world urban networks. Dr. Zhu's work has been supported by prestigious funding sources including the Swiss National Science Foundation and the European Union's Horizon 2020 program. His research demonstrates strong potential for practical implementation, with simulations conducted on real urban networks (particularly Shenzhen, China) showing significant improvements in key performance metrics like passenger waiting times and service rates. As an active researcher at EPFL, Dr. Zhu collaborates extensively with Professor Nikolaos Geroliminis and other researchers in the urban transportation field. His work contributes to the development of more efficient, sustainable urban transportation systems that benefit customers, service providers, and the environment through optimized fleet operations in mobility-on-demand services.
Johannes Schlaich is a Professor of Mobility and Transport at the Berlin University of Technology , with a focus on integrated transport planning, traffic modeling, and digitalization in transport. His research includes strategic demand modeling, shared mobility, and future mobility systems.
Hao Xu serves as an Associate Professor at the University of Nevada, Reno, holding the Ralph E. and Rose A. Hoeper Professorship. His research laboratory operates from SEM Building, Room 337D, with contact via haoxu@unr.edu. His research spans critical domains in intelligent transportation systems: Intelligent control and machine learning for cyber-physical systems Networked control systems and unmanned aircraft applications Power control, smart grid integration, and wireless sensor networks Recent publications (2023-2025) demonstrate concentrated expertise in roadside LiDAR applications, developing algorithms for vehicle/pedestrian detection, trajectory prediction, and safety analysis under challenging conditions including snow and heavy traffic. His work integrates deep learning with optimization techniques to enhance data processing robustness, particularly for vulnerable road user protection and near-miss event quantification. While no specific scientific awards beyond his named professorship were documented, his research directly addresses critical transportation safety challenges through innovative sensor applications and data analytics. Information regarding student advising, research grants, and laboratory infrastructure details was not provided in available materials, though his publication output indicates active collaboration with transportation agencies on smart infrastructure development.
Dr. Karen Boyce serves as Senior Lecturer at Ulster University's Belfast School of Architecture & the Built Environment within the Faculty of Computing, Engineering and the Built Environment. With over 30 years of continuous research at FireSERT (Fire Safety Engineering Research and Technology Centre), she specializes in human behaviour during fire evacuations, particularly focusing on vulnerable populations including people with disabilities and elderly occupants. PhD in Fire Safety Engineering (1997), Thesis: "Towards the Characterisation of Disabled Persons for Evacuation" BSc in Mathematics and Computer Science (1984), Queen's University Belfast Her research program integrates experimental fire safety engineering with pedestrian dynamics, examining occupant movement through motion capture studies, funnel-shaped bottleneck analyses, and biomechanical measurements. Key contributions include an EPSRC-funded investigation of World Trade Centre evacuations on 9/11 and development of engineering data for the Society of Fire Protection Engineers Handbook. She has published over 60 research papers addressing evacuation modeling for diverse populations. Analysis of her 2019-2024 publications reveals accelerating focus on microscopic crowd flow modeling, with increasing emphasis on experimental biomechanics and cognitive components of pedestrian movement. Recent work quantifies stop/start processes in pedestrian traffic and examines demographic impacts on evacuation performance, reflecting growing urgency in adapting fire safety standards for aging populations and reduced mobility groups. Dr. Boyce holds significant leadership positions including Chair of the Programme Committee for the International Symposium Human Behaviour in Fire series and membership on editorial boards for Fire Science Reviews and Case Studies in Fire Safety. She serves on the Advisory Board of Fire and Materials and previously co-chaired the Planning Committee for the 12th International Association of Fire Safety Science Symposium (2017). As Principal Investigator for the "Means of Escape for Disabled People" project (2020-2023) and contributor to the HARMONISE infrastructure security initiative (2013-2019), she bridges academic research with regulatory impact. Her external roles include membership on the Northern Ireland Building Regulations Advisory Committee (NIBRAC) Part E technical sub-committee and External Examiner position at Glasgow Caledonian University since 2014.
