Prof. Hadi Ghaderi is a Professor of Management and Major Discipline Coordinator for Logistics and Supply Chain Management at Swinburne Business School (Swinburne University of Technology). He leads the Supply Chain Decarbonisation initiative and the Disruptive Mobility research stream at Swinburne’s Smart City Research Institute. His work bridges research and teaching, emphasizing practical, problem-based learning. Notable recognitions include inclusion in Stanford’s 2023-2024 Top 2% Cited Scientists and the 2018 Faculty Teaching Excellence Award for inspiring student engagement. Research focuses on supply chain digitalisation, decarbonisation, intelligent transport systems, and urban logistics. Key projects include crowd-shipping optimization, 5G-enabled smart cities, and low-emission truck adoption strategies. He collaborates with industry and government to deliver solutions like AIoT-CitySense for infrastructure maintenance and OPL-based delivery systems. Professional accolades include finalist status in the Excellence in Research and Development Award (2023) and High Commendation in Environmental Excellence (2023). He supervises PhD candidates addressing decarbonisation, smart platforms, and sustainable urban delivery systems.
Iman Soltani is an Assistant Professor at the University of California, Davis, jointly appointed in Mechanical and Aerospace Engineering and Electrical and Computer Engineering, and affiliated with the Institute of Transportation Studies. His research integrates machine learning, control systems, and robotics across scales—from nanoscale materials manipulation to autonomous driving and medical device development. He leads the Soltani Lab, focusing on interdisciplinary projects involving computer science, electrical, and mechanical engineering principles. Key research interests include automated systems, anomaly detection, robotic manipulation, and AI-driven navigation. His work bridges theoretical advancements with experimental innovations, such as the Krysalis Hand and Cardreamer platforms. Awards include the MIT Carl G. Sontheimer Award and National Instruments Engineering Impact Award. Recent publications span autonomous infrastructure surveying, bimanual robotic manipulation, and marine navigation systems. Collaborations span academia and industry, including Ford Greenfield Labs. His lab is located in the Mechanical & Aerospace Engineering building at UC Davis.
Dr. Achilleas Kourtellis serves as Assistant Program Director and Teaching Professor in the ITS, Traffic Operations & Safety program at the University of South Florida's College of Engineering. He holds a PhD in Transportation (2009) and has been affiliated with CUTR since 2006. His research focuses on driver behavior, traffic safety, and intelligent transportation systems (ITS). He has led projects involving pedestrian safety, connected vehicle technologies, and crash avoidance systems for trucks and transit vehicles. Educations: PhD (2009), MS (2006), BS (2004) in Civil Engineering from USF Research interests include behavioral analysis of drivers/pedestrians, controlled driving tests for technology evaluation, and data-driven traffic management. He co-managed major studies like SHRP2 Naturalistic Driving Study and FDOT Naturalistic Bicycle Pilot. His work emphasizes practical applications of ITS and safety countermeasures in real-world environments. Recent articles explore pandemic impacts on driving behavior, connected vehicle cybersecurity, and infrastructure resilience. He actively participates in TRB committees on pedestrian and bicycle safety. No scientific awards explicitly listed, though his extensive peer-reviewed publications reflect recognition in the field. Advises on projects involving transit safety, automated vehicles, and data collection methods. Collaborates with industry on technology deployment and evaluation. His lab focuses on integrating emerging technologies like drones and AI for traffic monitoring and safety enhancement.
Henry Liu is a Joint Professor in Mechanical Engineering and Civil & Environmental Engineering at the University of Michigan's College of Engineering. His research focuses on the intersection of Transportation Engineering, Automotive Engineering, and Artificial Intelligence, with emphasis on cyber-physical transportation systems, autonomous vehicles, and traffic flow control. Key areas include connected and automated vehicle (CAV) testing, safety validation, and cooperative driving frameworks. He leads projects involving Mcity, a dedicated CAV testbed, and develops edge-cloud infrastructure for roadside perception systems. Education: Ph.D. in Civil and Environmental Engineering, University of Wisconsin – Madison, 2000 B.S. in Automotive Engineering, Tsinghua University, P.R.China, 1993 Research Interests: Prof. Liu's work spans traffic flow monitoring , CAV safety assessment , generative simulation for edge cases , and data-driven traffic control algorithms . He pioneers methods for anomaly detection in vehicle platoons, cybersecurity in traffic systems, and low-penetration-rate scenario optimization. His team creates tools like TeraSim (for unsafe event discovery) and LightEMMA (lightweight autonomous driving models). Publications Trends (2023–2025): Recent work emphasizes safety validation (e.g., behavioral safety assessments), roadside perception systems , and low-adoption CAV scenarios . Over 30+ articles address cybersecurity, cooperative control, and simulation-driven testing methodologies. His lab has developed frameworks like DeepScenario for city-scale scenario generation and MSight for edge-cloud perception. Awards & Grants: While no specific awards are listed, his research has been supported through initiatives like Mcity 2.0 development and federal grants for CAV infrastructure. He collaborates with the American Center for Mobility on testing environments. Labs & Teams: Leads the Mcity Augmented Reality Testing Environment and co-develops the Mcity testbed. His group works on cybersecurity for traffic systems and participatory traffic control strategies involving connected vehicles.
Dr. Priyanka Alluri is an Assistant Professor in the Department of Civil and Environmental Engineering at Florida International University (FIU). She holds a Ph.D. in Civil Engineering from Clemson University (2010) and is a licensed Professional Engineer in South Carolina. Her research focuses on transportation safety, including advanced safety analysis models, implementation of the Highway Safety Manual (HSM), pedestrian/bicycle safety, traffic control devices, and human factors in transportation. She currently serves as Faculty Advisor for the Institute of Transportation Engineers (ITE) Student Chapter at FIU. Education: Ph.D., Civil Engineering, Clemson University, 2010 Research interests emphasize transportation safety innovations, with a focus on crash analysis methodologies, traffic incident management, and smart infrastructure applications. Her work spans topics like secondary crash prevention, adaptive traffic control systems, and mobility benefits of TSMO strategies. She has pioneered studies on unique areas such as golf cart safety in retirement communities and the impact of pandemic conditions on crash severity. Publications highlight her contributions to TSMO strategy evaluation, safety performance functions for managed lanes, and data-driven approaches to mitigate wrong-way driving. Her research integrates advanced statistical modeling (Bayesian networks, neural networks) and simulation tools (VISSIM, PTV Epics) to address real-world transportation challenges. Notable projects include Florida-specific safety guidelines for ramp metering operations, evaluation of Road Rangers' impact on incident clearance, and development of safety assessment toolkits for bus stops. She actively contributes to transportation policy through studies on connected vehicle technologies and procurement frameworks for Florida's CV deployments.
Branislav Dimitrijevic is an Assistant Professor in the Department of Civil & Environmental Engineering at New Jersey Institute of Technology (NJIT). He holds a Ph.D. in Transportation Engineering from NJIT (2018) and specializes in transportation systems analysis, planning, and intelligent transportation systems (ITS), with a focus on road weather management, traffic safety, and drone applications in traffic operations. Education: Ph.D., Transportation Engineering, NJIT (2018) M.S., Transportation Engineering, NJIT (2001) B.S., Transportation Engineering, University of Belgrade (1999) Research Interests: His work spans transportation data analytics, multimodal freight systems, integrated corridor management, and innovative mobility solutions. He has contributed to projects like the federal TELUS program, developing land-use modeling software, and improving traffic signal prioritization for the New Jersey Department of Transportation (NJDOT). Recent research includes leveraging connected vehicle data, drones for traffic surveillance, and crash risk prediction models. Research Trends: His publications emphasize data-driven approaches to traffic management, safety, and infrastructure optimization. Key themes include real-time incident detection, dynamic pricing models, and the application of machine learning to crash severity analysis and work zone capacity estimation. Awards: No awards explicitly mentioned in the text. Grants & Advising: His work has been supported by NJDOT and federal grants. He has advised on projects involving smart arrival notification systems for paratransit services, adaptive traffic control systems, and hardware-in-the-loop simulations. No formal advisees are listed in the provided materials. Labs & Teams: Involved in NJIT’s Department of Civil and Environmental Engineering research teams, contributing to projects on traffic analytics, drone applications, and transportation infrastructure resilience.
M Carmen Romano is a Professor in the Department of Physics at the University of Aberdeen, within the School of Natural and Computing Sciences. Her research bridges physics and biology, focusing on theoretical and computational modeling of fundamental biological processes such as protein translation and cellular stress responses. She is also expanding her work into data science applications in healthcare and energy systems. Research Interests: Her primary interests lie in understanding protein translation dynamics, ribosome traffic, transcription-translation coupling, and combinatorial stress effects in yeast, using nonlinear dynamics and statistical physics. She also applies data science methods to model complex systems across disciplines. Recent Publication Trends: Her recent work (2018–2025) emphasizes the biophysical modeling of translation, tRNA dynamics, and codon optimization, often using exclusion process models and genome-wide simulations. There is a clear trajectory toward integrating computational tools with experimental data to decode gene expression regulation. Funding: SULSA studentship (modeling of translation) SABR grant (Combinatorial Responses In Stress Pathways) BBSRC grant (Ribosome Traffic Flow on the mRNA as a Regulator of Cellular Protein Production) Teaching Responsibilities: Electromagnetism (PX3008, 3rd year Physics) Introduction to Mathematics and Modelling of Biological Systems (SB5005, MSc in Cell and Molecular Systems Biology) She maintains affiliations with the Institute for Complex Systems and Mathematical Biology and the Institute of Medical Sciences, reflecting her interdisciplinary approach.
Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.
Rubi Debnath is a researcher at the Technische Universität München (TUM) affiliated with the Department of Embedded Systems and Internet of Things . They focus on Time-Sensitive Networking (TSN) , Machine Learning for TSN , and Optimization of TSN Scheduling Algorithms using heuristics, ILP, and DRL. Specializes in TSN Scheduling , Runtime Reconfiguration , and Wireless-TSN Actively teaches IoT Security , Software Architecture for Distributed Embedded Systems , and System Design for IoT since Winter Semester 2018/2019 Supervised over 15 Master's and Bachelor's theses on TSN, ML, and 5G-TSN integration Developed simulation frameworks like CyclicSim and 5GTQ Research Trends : Recent publications address TSN scheduling optimization , ML-assisted traffic classification , and 5G-TSN integration , with a focus on low-latency communication and industrial automation . Scientific Awards : IEEE ComSoc Four Minute PhD Thesis Competition - Third Prize Winner (Round 2), Round 1 Winner Global Fellows Program - Imperial College London, TUM, and NTU Singapore Advising and Grants : Supervised 15+ theses on TSN, ML, and 5G-TSN. Involved in the 6G Research Hub "6G-Life" and nIoVe cybersecurity framework for IoT.
Wenying Ji is an Assistant Professor in the Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering at George Mason University's Volgenau School of Engineering. His research focuses on the integration of advanced data analytics, complex system simulation, and construction management to enhance decision-support processes in the Architecture, Engineering, and Construction (AEC) industry. Dr. Ji received his PhD in construction engineering and management from the University of Alberta, a master's degree from Texas A&M University, and a bachelor's degree from Southeast University. His research interests span several interconnected domains including construction engineering, infrastructure systems, disaster management, data analytics, and complex system simulation. Dr. Ji has developed innovative approaches that apply Bayesian methods and machine learning to solve critical problems in infrastructure resilience, particularly during and after natural disasters. His work emphasizes the integration of real-time data from social media and other sources to improve infrastructure restoration processes following extreme events. A significant portion of his research focuses on highway systems, flood management, and emergency response planning, with particular attention to equity considerations in infrastructure restoration. Analysis of Dr. Ji's recent publications reveals a clear progression toward increasingly sophisticated integration of data analytics with infrastructure engineering problems. His work shows a strong emphasis on Bayesian methods, machine learning applications, and spatiotemporal analysis for disaster management and infrastructure restoration. The research demonstrates practical applications for improving decision-making in construction management, particularly in contexts of uncertainty and emergency response. Dr. Ji's notable awards include: 2017 Outstanding Reviewer Award from ASCE's Journal of Computing in Civil Engineering 2018 ASCE outstanding reviewer award for ASCE Journal of Construction Engineering and Management Jeffress Trust Awards Program in Interdisciplinary Research (2019) WSC Outstanding Reviewer Award (2019, 16 out of 746 reviewers) Dr. Ji actively mentors PhD students including Yitong Li, Yudi Chen, Minjie Xia, and Yuzheng Xie, with several receiving awards for their research. His research group has secured significant funding, including an NSF grant in 2022 on 'Strengthening American Electricity Infrastructure for an Electric Vehicle Future.' He serves as Assistant Specialty Editor for the ASCE Journal of Construction Engineering and Management and regularly reviews for top journals in his field. Dr. Ji's research team collaborates with multiple institutions and participates in conferences such as the Winter Simulation Conference and ASCE International Conference on Computing in Civil Engineering.
Xu Sun is a Postdoctoral Fellow at the Department of Industrial Engineering, UiT The Arctic University of Norway, Campus Narvik. His research bridges logistics, sustainability, and digital innovation within Industry 4.0/5.0 frameworks, with a focus on practical applications in Norwegian contexts including electric vehicle infrastructure and pandemic response. His research portfolio spans Reverse Logistics, Sustainable Logistics, Closed-Loop Supply Chains, Digital Twins, and Industry 5.0 integration. Key themes include applying generative AI to logistics network design, optimizing charging infrastructure for electric trucks, and developing digital twins for circular economy systems. His work emphasizes multi-objective decision-making to balance environmental, social, and economic sustainability in logistics operations. Analysis of Xu Sun's publication trends reveals a strategic shift toward human-centric digital solutions in logistics. Recent works integrate generative AI, digital twins, and simulation to address Industry 5.0 challenges, with notable emphasis on electric mobility infrastructure (2024-2025) and pandemic-related logistics (2021-2022). His research consistently employs case studies from Norway, demonstrating practical applicability while advancing theoretical models for sustainable supply chains. No scientific awards were documented in the source material. While specific student advising details are absent from the profile, Xu Sun's collaborative publication record indicates active mentorship within research teams. His projects frequently involve multi-institutional partnerships and industry engagement, though explicit grant information is not provided in the available text. Xu Sun contributes to the ArcLog research group's Intelligent Manufacturing and Logistics team and leads the 'Industry 5.0 enabled Smart Logistics' project. His work leverages tools like AnyLogic simulation and digital twin technology to develop human-centered logistics solutions, with physical operations based in Campus Narvik office D2150.
Shigeru Shimamoto is a Professor at Waseda University's School of Fundamental Science and Engineering, Faculty of Science and Engineering. His research spans wireless communication systems, biomedical sensing technologies, and intelligent transportation solutions. Since 2014, he has led the Communication and Computer Engineering department at Waseda University, previously serving as Director of the Global Information and Telecommunication Institute (2020-2024). 2008: Visiting Professor at Stanford University's Electrical Engineering 2000-2002: Research Assistant at University of Electro-Communications Key research areas include: Wireless Communication: OTFS modulation, NOMA, RIS-aided systems, and microwave-based vital sensing Smart Healthcare: Non-contact blood pressure monitoring, SpO2 estimation using microwave reflection Transportation Optimization: On-street parking analysis, traffic flow modeling, and energy-efficient vehicular networks Awarded the 2024 Commendation for Science and Technology from MEXT, his work demonstrates strong interdisciplinary impact combining communication engineering with medical applications. Recent publications focus on machine learning integration in gesture recognition, vehicular detection, and resource allocation for autonomous systems.
Raffaello Secchi is a Lecturer at the School of Engineering , University of Aberdeen, specializing in computer networks and satellite communications . Academic Focus: Transport layer protocols (TCP/QUIC), congestion control algorithms, and QoS architectures for satellite networks. Key Research Areas: Machine learning in network traffic classification, IPv6 extensions, DVB-RCS2 optimization, and latency mitigation. Recent Publications (2025–2014) examine congestion control resumption, QUIC protocol efficiency, and machine learning applications for satellite-based traffic classification. These works emphasize network performance , protocol design , and QoS enhancements . Grants & Collaborations include partnerships with Thales Alenia Space (SMILE project) and Astrium LTD , alongside ESA-funded initiatives like Satnex III for future web technologies over broadband GEO satellite networks. Publications span Computer Networks , Computer Communications , and International Journal of Satellite Communications and Networking , reflecting his interdisciplinary approach to transport protocols and satellite systems.
Krishna Kumar is an Assistant Professor in the Department of Civil Engineering at The University of Texas at Austin, holding the J. Neils Thompson Centennial Teaching Fellowship. His expertise lies in Geotechnical Engineering and Sustainable Systems Engineering, with a focus on multi-scale modeling of natural hazards, landslides, earthquakes, and debris flows. He employs advanced computational techniques such as the material point method, discrete element method, and lattice Boltzmann method, alongside high-performance computing and big data frameworks for infrastructure system modeling. Education: Ph.D., University of Cambridge, 2015 M.S., Indian Institute of Technology Madras, 2010 B.E., Anna University, 2008 Research Interests: His technical interests include computational geomechanics, granular material simulations, and the integration of machine learning with physics-based models. Recent work emphasizes inverse analysis using differentiable neural networks, explainable AI for geotechnical risk assessment, and innovative teaching methods like murder mysteries to enhance engineering education. Publications Trend: His articles from 2023–2025 highlight advancements in hybrid numerical methods (e.g., finite element-material point method), machine learning-driven simulations of granular flows, and computational tools for predicting geotechnical hazards. Cross-disciplinary themes include AI explainability, granular material behavior under cyclic loading, and scalable city-level traffic modeling. Teaching & Innovation: Beyond technical research, he explores pedagogical innovations such as gamified learning through murder mystery scenarios to engage students in complex engineering concepts.
Adam Wittek is a Professor and Deputy Director of the Intelligent Systems for Medicine Laboratory at the Department of Mechanical Engineering, University of Western Australia. He also serves as Mechanical Engineering Programme Chair. His research focuses on computational biomechanics, biomedical engineering, and impact injury analysis, with notable contributions to surgical simulation, abdominal aortic aneurysm (AAA) modeling, and computational grid optimization. He has held roles at Toyota Central R&D Labs, Japan Automobile Research Institute, and ESI Group prior to his academic appointment in 2004. Wittek’s education includes a PhD in Engineering (Chalmers University of Technology, 2000) and an MSc in Applied Mechanics (Warsaw University of Technology, 1992). His work aligns with UN Sustainable Development Goals, particularly in advancing medical technologies and safety innovations. His research interests span finite element analysis, meshless methods, and medical image computing. Recent articles highlight advancements in AAA kinematics, brain shift correction, and computational fluid dynamics in biomedical contexts. He has secured grants totaling over $8M, including ARC and NHMRC funding for projects like 'Brain-skull interface biomechanics.' Recipient of Sir George Julius Medal (2012) and SAE Ralph H. Isbrandt Automotive Safety Award (2004) Editorial roles at Modelling and Australian Journal of Mechanical Engineering Supervised 12 doctoral projects, with active collaborations across 10+ countries Wittek leads the Computational Biophysics for Medicine in 3D Slicer project, advancing open-source tools for clinical applications. His lab develops patient-specific models for neurosurgery and cardiovascular applications.
