Find Academic Supervisors

• Transportation Safety
• Traffic Control Systems
• Intelligent Transportation Systems (ITS)
• +4 more

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.

Kai WangView profile

Assistant Professor

• Transportation Safety
• Artificial Intelligence
• Machine Learning
• +4 more

Kai Wang is an Assistant Research Professor at the School of Civil and Environmental Engineering, College of Engineering, University of Connecticut, and serves as Project Manager for the Connecticut Transportation Safety Research Center. His work focuses on applying advanced computational methods to transportation safety challenges, leveraging partnerships with the Connecticut Department of Transportation (CTDOT). His academic credentials include: Ph.D. in Civil Engineering, University of Connecticut, 2016 Dr. Wang's research centers on transportation safety analytics through multiple technical domains: Statistical and Econometric Modeling for transportation data systems Artificial Intelligence, Machine Learning, and Deep Learning applications Big Data Analytics and Computing for traffic safety Optimization and Simulation in transportation networks Financial Analytics for infrastructure investment His methodology integrates cutting-edge computational techniques to solve real-world problems in crash prediction, traffic volume estimation, and infrastructure safety management. Recent publications demonstrate pioneering work in artificial data generation (e.g., DREDGE system), vulnerable road user safety, vehicle mix effects on crashes, and telematics-based safety monitoring. This research directly informs CTDOT's safety improvement programs through evidence-based modeling and systemic screening approaches. As Project Manager of the Connecticut Transportation Safety Research Center, he leads initiatives that bridge academic research with state transportation operations, collaborating closely with the Connecticut Transportation Institute to advance data-driven safety analytics for Connecticut's road networks.

• Evolutionary Computation
• Genetic Programming
• Smart Cities
• +3 more

Alina Patelli is a Senior Lecturer in Applied AI & Robotics at Aston University's School of Computer Science and Digital Technologies. Her research focuses on computational intelligence approaches for urban transportation systems, particularly evolutionary computation and transfer learning applications in traffic modeling and prediction. Key research areas include genetic programming for traffic pattern recognition, symbolic regression for transport decision-making, and deep learning integration with evolutionary algorithms. She also investigates educational methodologies for digital apprenticeships and work-based learning in technology fields. Patelli's recent publications demonstrate strong engagement with industry stakeholders including Birmingham City Council and the Department for Transport. Her projects aim to optimize urban infrastructure through predictive algorithms that address traffic congestion, pollution, and sustainable mobility solutions.

Marin MarinovView profile

Lecturer

• Rail Freight Logistics
• Urban Rail Systems
• Transportation Simulation Modeling
• +5 more

Marin Marinov is a Lecturer at the School of Engineering and Applied Science, Aston University (since 2019). Prior to this, he held roles as Senior Research Associate (2017–2019) and Research Associate (2009–2017) at Newcastle University's NewRail research centre, focusing on rail freight logistics and systems design. He holds a PhD in 'Analysis and Evaluation of Formation Yard Performances' from Newcastle University (2007). Research Interests: Urban Rail Systems, Freight Transport, Simulation Modeling, Rail Education, Sustainability Teaching: Simulation Modelling, Transport Logistics, Rail Freight, Systems Design, Queueing Theory Funding: Led the ASTONRail project (€450k EU Erasmus+ grant, 2020–2023), developing rail education standards across Europe. He coordinates RailUniNet (international rail academic network) and the ASTONRail strategic partnership, collaborating with institutions like SAPIENZA, KTH, and the International Union of Railways (UIC). His work emphasizes bridging rail education with industry needs, including AI integration and sustainable transport solutions. Awards: EU Erasmus+ grant for ASTONRail initiative. Editorial Roles: Sustainable Rail Transport (Springer), Urban Rail Transit (Springer).

Sara ReedView profile

Assistant Professor

• Transportation Logistics
• Vehicle Routing
• Last-mile Delivery
• +3 more

Sara Reed is an Assistant Professor of Business Analytics at the University of Kansas School of Business , specializing in transportation logistics and last-mile delivery optimization. Her research examines how parking constraints affect delivery routing and explores autonomous vehicle applications for urban logistics. Education: Ph.D. in Applied Mathematical and Computational Sciences, University of Iowa M.S. in Mathematics, University of Iowa B.A. in Mathematics and Economics/Finance, Simpson College Research Focus: Sara's work addresses critical challenges in urban delivery systems, including parking management policies, autonomous vehicle integration, and route optimization. Her research has been published in the Encyclopedia in Operations Management and Transportation Research Part E . Teaching: She instructs undergraduate courses in Data Analysis & Forecasting and Data Mining & Predictive Analytics, bridging mathematical rigor with practical business applications. Scientific Recognition: Honorable Mention for INFORMS TSL Best Paper Award (2023) Editorial Advisory Board member for Transportation Research Part E Organizer of Transportation and Logistics Early-Career Workshop (2025)

Juyeong ChoiView profile

Associate Professor

• Infrastructure Sustainability
• Disaster Risk Reduction
• Resilient Infrastructure Systems
• +5 more

Dr. Juyeong Choi is an Associate Professor in the Department of Civil and Environmental Engineering at the Florida A&M University-Florida State University College of Engineering. Her research focuses on sustainable infrastructure management, disaster resilience, and systems engineering. She holds a Ph.D. from Purdue University (2018), an M.S. from Purdue (2015), and a B.S. from the University of Seoul (2013). Education: Ph.D., Civil Engineering, Purdue University, 2018 M.S., Civil Engineering, Purdue University, 2015 B.S., Architectural Engineering, University of Seoul, 2013 Her research group, the Sustainable Infrastructure Management (SIM) Laboratory, addresses infrastructure challenges through interdisciplinary approaches, including system dynamics modeling, optimization techniques, and data analytics. Key research areas include: Infrastructure system-of-systems for disaster resilience Construction project management and analytics Sustainable demolition and debris management Transportation system performance for vulnerable populations Dr. Choi advises five graduate students and collaborates on projects funded by NSF and industry partners. She leads the RAPID project studying pandemic impacts on municipal waste systems and serves as a judge for Swift Creek Science Fair. The SIM Lab is located in Tallahassee, Florida, with members affiliated with both FAMU and FSU.

John SobanjoView profile

Professor

• Infrastructure Engineering
• Transportation Engineering
• Sustainability
• +4 more

John Sobanjo is a Professor of Civil & Environmental Engineering at the Florida A&M University-Florida State University (FAMU-FSU) College of Engineering. He holds a Ph.D. from Texas A&M University (1991), an M.S. from the University of Michigan (1984), and a B.S. from the University of Lagos (1980). His research focuses on infrastructure engineering, transportation systems, and sustainable construction materials. Notably, he has pioneered work in bridge management systems, pavement evaluation, and GPS/GIS applications in civil engineering. Prof. Sobanjo’s professional experience includes roles as an Associate Professor (2001–present) and prior engineering positions with state transportation departments. He has advised numerous projects for the Florida Department of Transportation (FDOT) and contributed to national/international conferences. His work emphasizes integrating advanced technologies like machine learning and semi-Markov models for infrastructure resilience and safety. Research interests span competing risks analysis in infrastructure deterioration, optimization of level crossings, and sustainable transportation networks. His awards include Fellow status in the American Society of Civil Engineers (F.ASCE). Key contributions include developing decision support tools for bridge management and improving safety at highway-rail grade crossings through multi-objective frameworks. Prof. Sobanjo teaches courses in construction materials, planning, and project controls. His lab focuses on smart sensors (e.g., triboluminescence-based systems) for structural health monitoring. Collaborations include federal agencies like FHWA and global institutions.

• Traffic Engineering
• Transportation Systems
• Data Mining
• +2 more

Dr. Ping Yi is a Professor of Civil Engineering at the University of Akron and Director of the Ohio Transportation Consortium. As President of NACOTA and member of ASEE/ASCE, his research advances intelligent transportation systems through sensor technologies and data analytics. Research spans traffic sensor evaluation, adaptive signal control, location-based systems, and traffic safety. Recent publications demonstrate strong focus on connected/automated vehicles (CAVs) and aging populations. Article analyses reveal dominant themes in intelligent traffic management, CAV integration for senior mobility, and optimization algorithms for mixed-traffic environments. Methodologies increasingly incorporate machine learning and human factors engineering. Education credentials include a Ph.D. in Traffic Engineering from the University of Minnesota (1992) and Professional Engineering licensure. Courses taught include Transportation Engineering, Highway Design, Traffic Flow Theory, and Adaptive Control Systems.

Wei YuView profile

Professor

• Cybersecurity
• Privacy
• Cyber-Physical Systems
• +3 more

Dr. Wei Yu is a Full Professor in the Department of Computer and Information Sciences at Towson University, USA, and serves as the Ph.D. program director in Information Technology since 2023. He holds a Ph.D. in Computer Engineering from Texas A&M University (2008), with prior degrees from Chinese institutions. His research focuses on cybersecurity, privacy, cyber-physical systems (CPS), IoT, data science, and machine learning applications. Key contributions include foundational work on secured edge intelligence in CPS domains like smart energy, manufacturing, and transportation. He has pioneered federated learning frameworks for privacy-preserving data analytics, digital twin architectures, and reinforcement learning for industrial IoT control. He has received prestigious awards including the NSF CAREER Award (2014–2021), USM Regents’ Faculty Award (2015), and Wilson H. Elkins Professorship (2016). His professional service includes senior editorial roles at IEEE Transactions on Information Forensics and Security, IEEE Internet of Things Journal, and IEEE Access. Research spans CPS security, IoT forensics, and AI-driven CPS applications. Notable projects include adversarial mitigation in federated learning, CAPTCHA robustness, and edge computing for smart transportation systems.

Md Rafiul IslamView profile

Assistant Professor

• Epidemiology
• Mathematical Modeling
• Machine Learning
• +2 more

Md Rafiul Islam serves as an Assistant Professor of Data Science in the Mathematics Department at the University of the Incarnate Word since 2022, following a postdoctoral position at Iowa State University. His research bridges mathematical modeling with critical public health challenges, particularly in infectious disease dynamics and vaccine strategy optimization. His academic credentials include: Ph.D. in Applied Mathematics, Texas Tech University (2020) M.S. in Mathematical Sciences, Clemson University (2015) M.S. in Applied Mathematics, University of Dhaka (2012) B.S. in Mathematics, University of Dhaka (2010) Dr. Islam's work centers on epidemiological modeling of pathogens like SARS-CoV-2 and chronic wasting disease, utilizing machine learning and social network analysis to evaluate vaccine allocation strategies. His autonomous vehicle research applies physics-based models to driver behavior, demonstrating interdisciplinary expertise spanning public health and transportation engineering. Current projects integrate mutation landscapes of viral variants with case-dependent vaccination rates for epidemic forecasting. His publication record (2017-2022) reveals three core thematic clusters: 40% focus on COVID-19 modeling (vaccine allocation, variant dynamics), 30% on wildlife disease transmission (chronic wasting disease, fungal pathogens), and 30% on transportation systems (driver behavior modeling for autonomous vehicles). This trajectory shows increasing emphasis on real-world policy applications, particularly in resource-limited settings like refugee camps. As an educator, he teaches Introduction to Probability and Statistics, Linear Algebra, and Introduction to Data Science while serving as Associate Guest Editor for Frontiers in Infectious Disease Epidemiology and reviewing for journals including PLOS ONE and Journal of the Royal Society Interface. His community service philosophy emphasizes collaborative problem-solving for global health challenges.

Benjamin SeiboldView profile

Professor

• Applied and Computational Mathematics
• Traffic Flow Modeling
• Radiative Transfer
• +7 more

Benjamin Seibold is a Professor of Mathematics and Physics at Temple University's College of Science and Technology. He leads the Center for Computational Mathematics and Modeling and holds dual appointments in Mathematics and Physics. His research focuses on Applied and Computational Mathematics, with expertise in high-order numerical methods, traffic flow modeling, radiative transfer, and invasive species dynamics. He has been at Temple since 2009, previously serving as an Instructor at MIT and earning his Dr.rer.nat. from the University of Kaiserslautern. His education includes a Diplom in Technomathematics (1997–2000) and a PhD in Applied Mathematics (2006) from the University of Kaiserslautern, with postdoctoral work at Lawrence Berkeley Lab and MIT. He has received prestigious awards such as the 2013 Greenshields Prize and the 2024 IEEE ITS Institutional Lead Award. His work spans computational modeling, numerical analysis, and real-world system analysis. Research Interests: Traffic flow modeling and control using autonomous vehicles Level set methods and jet schemes for interface evolution Meshfree/particle methods for fluid dynamics Radiative transfer moment approximations Invasive species population modeling (e.g., spotted lanternfly) Key Contributions: Developed the Particleclaw and StaRMAP software tools Advanced understanding of jamiton dynamics in traffic Received over $10M in NSF/USDA grants for traffic, radiative transfer, and invasive species projects Lab Activities: Directs Temple's Applied Mathematics and Scientific Computing Group, collaborating with global institutions on traffic control, radiation transport, and ecological modeling.

• Systems Engineering
• Decision Analysis
• Risk Analysis
• +6 more

Alisha Youngblood Ortiz is a Professor of Marketing at Southeast Missouri State University (SEMO), holding positions since 2009. She holds a Ph.D., M.S., and B.S. in Industrial Engineering from the University of Arkansas. Her research focuses on systems engineering, decision analysis, risk analysis, and logistics, with notable contributions to project success metrics, balanced scorecard methodologies, and transportation modeling. Professional experience includes roles at Worcester Polytechnic Institute (2008–2009), the University of Alabama in Huntsville (2003–2008), and adjunct teaching at the University of Arkansas (2002–2003). Awards include the Eugene L. Grant Award (2010) and multiple fellowships from the William Randolph Hearst and Earl H. Beling Foundations. Her research spans systems engineering processes, risk-adjusted financial calculations for government projects, and performance measurement systems in multinational organizations. Recent work addresses job satisfaction among civil engineering faculty and ammonia removal technologies in environmental engineering. Key contributions include developing frameworks for aligning strategy with capital allocation and analyzing trade-offs in project success metrics. She has also published on GPS system optimization and intermodal freight modeling.

Alina LazarView profile

Professor

• Machine Learning
• Data Science
• Database Systems
• +5 more

Dr. Alina Lazar is a Professor in the Department of Computer Science & Information Systems at Youngstown State University (YSU), part of the STEM College. She holds a Ph.D. in Computer Science from Wayne State University (2002) and a BS from Western University of Timisoara, Romania (1995). Her research focuses on Machine Learning, Data Science, and High Energy Physics applications, including Graph Neural Networks for particle tracking and traffic modeling using probe vehicle data. She has received multiple awards, including YSU Distinguish Professorships for Teaching (2025), Research (2022), and Service (2017). Lazar has secured a $600k grant from the DOE’s FAIR initiative for her work on large-scale models in HEP pattern recognition. Her teaching includes advanced courses like Deep Learning and Data Science. She has advised numerous master's theses and projects, with students focusing on topics like bug detection, database systems, and code readability. Lazar serves on NSF and DOE review panels and chairs conferences like OCWiC. Her interdisciplinary collaborations span physics (Lawrence Berkeley Lab), transportation, and software engineering.

Mohammad AlizadehView profile

Associate Professor

• Computer Networks
• Systems
• Machine Learning
• +6 more

Mohammad Alizadeh is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds additional roles as Director of the 6-A MEng Thesis Program and Industry Officer for EECS, overseeing the EECS Alliance industry outreach program. His research focuses on computer networks, systems, and their applications in datacenters, cloud computing, and edge computing. Notable areas include machine learning for systems, congestion control, and network protocols. Education: PhD (Stanford University), MS in Electrical Engineering (Stanford), BS (Sharif University of Technology). Industry Experience: Former roles at Microsoft Research, Insieme Networks, and Cisco Systems. Research Interests: Alizadeh’s work emphasizes improving network performance, robustness, and management in large-scale systems. Key projects include DCTCP, CONGA, and Pensieve (adaptive video streaming with reinforcement learning). His contributions span network architecture, programmable switches, and learning-based systems. Awards: NSF CAREER Award Alfred P. Sloan Research Fellowship SIGCOMM Rising Star Award Microsoft Research Faculty Fellowship Teaching: Courses include 6.888 (Advanced Topics in Networking), 6.829 (Computer Networks), and 6.02 (Digital Communication Systems). His teaching emphasizes cutting-edge networking research and practical system design. Labs/Teams: Active member of CSAIL, leading projects in distributed systems, network protocols, and cloud computing.

• Transportation Planning
• Urban Mobility
• Spatial Analytics
• +4 more

Associate Professor Chinh Ho is affiliated with the University of Sydney Business School, specializing in Spatial Analytics and Logistics. He holds a BE (Hons) from HCMUT, ME from Nagoya University, and a PhD from the University of Sydney. His research focuses on Mobility as a Service (MaaS), urban transport systems, and travel behavior analysis. He has led significant projects like the Sydney MaaS Trial and contributed to policy design for regional transport networks. His work bridges academic research with practical applications in public policy, including studies on post-pandemic travel patterns and rural mobility solutions. Education PhD, University of Sydney Master of Engineering, Nagoya University Bachelor of Engineering (Honors), HCMUT Research Interests Professor Ho explores transformative transport systems, emphasizing MaaS integration, sustainable mobility, and data-driven infrastructure planning. His recent work addresses challenges in on-demand transport networks, socio-economic disparities in travel behavior, and stakeholder collaboration in mobility ecosystems. He has published widely in journals like Transportation Research Part A and Transportation Planning and Technology . Grants & Projects Key grants include the iMOVE-funded Design of a Regional Town and Rural Hinterland MaaS Blueprint (2021). His research has been featured in media outlets such as ABC Radio and Channel 7 News, discussing urban tolls and public transport recovery post-COVID. Labs & Affiliations He is part of the Institute of Transport and Logistics Studies (ITLS) within the Business School, collaborating with industry and government agencies to advance transport innovation.