Find Academic Supervisors

Annie LiangView profile

Senior Lecturer

• Air Traffic Management
• Trajectory-Based Operations
• Machine Learning
• +7 more

Dr. Annie Liang is a Senior Lecturer in the School of Engineering at RMIT University, specializing in Air Traffic Management (ATM), Trajectory-Based Operations (TBO), machine learning, and big data analytics with a focus on autonomous systems and Advanced Air Mobility (AAM). Her work addresses challenges in urban air mobility, sustainable aviation, and airspace optimization. She holds licenses in air traffic control and is an active member of professional societies like the Royal Aeronautical Society and AIAA/IEEE. Research interests include intelligent air traffic systems, unmanned traffic management, and safety-centered AAM. Her UN Sustainable Development Goals alignment includes Industry, Innovation and Infrastructure; Sustainable Cities and Communities; and Climate Action. Teaching focuses on aviation law, avionics, and sustainable aviation practices. Key projects involve trajectory prediction, wind disturbance analysis for Urban Air Mobility, and drone fleet management for agriculture. She supervises Master’s and PhD students in topics like air traffic flow optimization and autonomous systems. Collaborates with labs such as Intelligent Automation & Autonomy for Aerospace and Information in Society.

• Transportation and Freight Services
• Adaptive Agents and Intelligent Robotics
• Building Construction Management and Project Planning
• +5 more

Panagiotis Angeloudis is a Professor of Transport Systems & Logistics at Imperial College London's Department of Civil and Environmental Engineering, Faculty of Engineering. He leads the Transport Systems & Logistics (TSL) Laboratory, focusing on autonomous systems, multi-agent modeling, and network optimization for transportation and logistics. His research spans freight distribution, passenger transport, and the integration of AI and robotics in mobility systems. He holds a PhD (2009) and MEng (2005) in Civil & Environmental Engineering from Imperial College London. His professional roles include Programme Director of the MSc in Transport and Data Science, Director of Teaching for the Transport Section, and transport champion for the Institute for Security Science and Technology. He also serves as an Associate Editor for the Journal of Maritime Policy and Management and on the UK Government's Future of Mobility review team. Research interests include autonomous vehicles, logistics optimization, and resilience engineering. His work addresses challenges in urban mobility, energy networks, and infrastructure resilience through advanced modeling and AI-driven solutions. Recent projects focus on deployment strategies for autonomous systems, maritime policy, and critical infrastructure resilience in geopolitical disruptions. His funded projects involve EPSRC, InnovateUK, and industry partners. Key areas of application include smart cities, emergency logistics, and sustainable transport systems. TSL Lab's innovations include digital twin simulations, trajectory prediction algorithms, and decision-support frameworks for autonomous systems.

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +4 more

Nan Wang is a prominent Professor at East China University of Science and Technology's School of Information Science and Engineering, with additional affiliation at SenseTime Research in Hangzhou. Their research spans artificial intelligence, computer vision, and interdisciplinary applications in agriculture, medicine, and environmental science. Wang's research interests focus on deep learning architectures, computer vision applications, and AI-driven solutions for real-world problems. Their work demonstrates particular expertise in object detection systems (including YOLO variants), medical image analysis, remote sensing applications, and security-enhanced AI systems. The research program shows strong industry collaboration, particularly with SenseTime, bridging academic innovation with practical implementation. Recent publication trends reveal increasing focus on cross-domain AI applications, with significant contributions to agricultural technology (particularly crop monitoring and pest detection), medical diagnostics (especially cardiovascular imaging and cancer treatment response prediction), and environmental monitoring systems. The work consistently integrates cutting-edge deep learning techniques with domain-specific challenges. Wang has established a robust research program with extensive publication output across top venues including IEEE Access, Remote Sensing, and Pattern Recognition Letters, demonstrating leadership in both theoretical AI development and practical implementation across multiple sectors. The collaborative nature of the work is evident through numerous co-authorship networks spanning Chinese academic institutions and industry partners.

Torsten HoeflerView profile

Professor

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +3 more

Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.

• Supply Chain Digitalisation
• Supply Chain Optimisation
• Operations Management
• +5 more

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 SoltaniView profile

Assistant Professor

• Machine Learning
• Robotics
• Automation
• +5 more

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.

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

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 LiuView profile

Professor

• Transportation Engineering
• Automotive Engineering
• Artificial Intelligence
• +4 more

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.

Priyanka AlluriView profile

Assistant Professor

• Transportation Safety
• Highway Safety Manual
• Pedestrian and Bicycle Safety
• +3 more

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 DimitrijevicView profile

Assistant Professor

• Transportation Systems Analysis
• Transportation Planning
• Multimodal Freight Transportation
• +5 more

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.

• Theoretical Biology
• Statistical Physics
• Nonlinear Dynamics
• +9 more

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.

• Computational Fluid Dynamics
• Aerodynamic Design
• Ship Hull Reconstruction
• +4 more

Nikolos Ioannis serves as a Professor at the School of Production Engineering and Management, Technical University of Crete, currently on leave but maintaining active research status. His work spans computational fluid dynamics, aerodynamics, and marine engineering within the Department of Production Engineering and Management. His primary research focuses on computational fluid dynamics with emphasis on ship hull reconstruction from 2D drawings, hypersonic flow simulation, and wind turbine optimization. Recent publications demonstrate expertise in geometric modeling , rarefied gas dynamics , and renewable energy systems . The 2025-2020 article portfolio reveals consistent contributions to naval architecture, aerodynamic shape optimization, and laser-matter interactions with strong methodological focus on B-spline functions, DSMC methods, and differential evolution algorithms. Professor Ioannis maintains an active email contact ( inikolos@tuc.gr ) and office location at D4.107 in the MPD Building. His work shows significant interdisciplinary connections between production engineering, computational physics, and marine applications with particular strength in translating theoretical models to practical engineering solutions.

Dirk HelbingView profile

Professor

• Computational Social Science
• Complexity Science
• Digital Society
• +10 more

Dirk Helbing is a Full Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences at ETH Zurich, with an affiliation at the Department of Computer Science. He is also a member of the External Faculty at the Complexity Science Hub Vienna. His research integrates computational modeling, data science, and social theory to study complex socio-technical systems, with a focus on digital societies, smart cities, and democratic innovation. ETH Zurich – Department of Humanities, Social and Political Sciences Affiliate, Department of Computer Science, ETH Zurich External Faculty, Complexity Science Hub Vienna Former Affiliate Professor, TU Delft (Technology, Policy and Management) Helbing’s research spans computational social science, complexity science, urban sustainability, and digital democracy. He pioneered work in pedestrian and traffic modeling and expanded into socio-economic systems, agent-based simulation, and systemic risk. His team investigates topics such as digital governance, participatory budgeting, resilience, and ethical smart city design. He emphasizes interdisciplinary integration and real-world policy impact. The recent publications reflect a strong trend toward using AI, agent-based modeling, and data analytics to address urban challenges—particularly in traffic management, air quality monitoring, and participatory governance. His work increasingly focuses on ethical and democratic dimensions of technology, advocating for decentralized, inclusive, and resilient digital societies. Idee Suisse Award Honorary PhD, Delft University of Technology (2014) Helbing has advised numerous PhD and postdoctoral researchers and led major initiatives such as the FuturICT project and the doctoral program on Engineering Social Technologies at TU Delft. His research is supported by significant grants, including an ERC Advanced Investigator Grant. He has co-founded several interdisciplinary centers, including the Risk Center, the Decision Science Laboratory (DeSciL), and the Competence Center for Coping with Crises. He leads the Computational Social Science (COSS) group at ETH Zurich, which combines fundamental research with applied policy innovation. The team uses evolutionary game theory, simulations, and experiments to study collective behavior, cooperation, and societal transformation.

Simon DemangeView profile

Researcher

• Fluid Dynamics
• Aeroacoustics
• Turbulent Boundary Layers
• +5 more

Simon Demange is a post-doctoral researcher at the Technical University of Berlin, working within the Institute for Fluid Mechanics and Technical Acoustics (ISTA) in the Faculty of Traffic and Machine Systems. His research focuses on linear stability and resolvent analyses of turbulent boundary layers developing over airfoils, with particular emphasis on developing low-order physics-based models describing noise generation mechanisms from coherent structures in turbulent flows. Dr. Demange completed his research master and PhD thesis within the aerospace and aeronautics department of the von Karman Institute for Fluid Dynamics in 2021. His doctoral research focused on the onset of absolute and global instabilities in high-enthalpy round jets, establishing his expertise in flow instability analysis. Dr. Demange's research interests span several critical areas of fluid dynamics with practical engineering applications: Turbulent boundary layer dynamics over airfoils and wings Resolvent analysis for modeling coherent structures in turbulence Trailing edge noise mechanisms in wind turbines and aircraft Physics-based low-order modeling of flow phenomena Plasma jet instabilities and high-enthalpy flow dynamics Multi-physics flow modeling and simulation Analysis of Dr. Demange's publication record reveals a strong trajectory of increasingly sophisticated work bridging fundamental fluid dynamics with practical engineering applications. His research demonstrates particular expertise in applying stability analysis techniques to understand and model noise generation in aerodynamic systems, with significant implications for wind turbine design and aircraft noise reduction. His collaborative work spans multiple institutions and shows strong interdisciplinary connections between theoretical fluid dynamics, computational methods, and experimental validation. Dr. Demange has developed specialized computational tools including FELiCS, a versatile linearized solver for multi-physics flows, demonstrating his capability in both theoretical development and practical implementation. His work on wind turbine noise addresses a critical challenge in renewable energy technology, while his research on high-speed train aerodynamics contributes to transportation engineering advancements.

Xin WangView profile

Assistant Professor

• Smart Cities
• Logistics Systems
• Transportation Engineering
• +3 more

Dr. Xin Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, with additional affiliation to the Civil and Environmental Engineering department. His research focuses on mathematical modeling for smart cities, particularly in logistics and transportation systems. Primary Affiliation: Industrial and Systems Engineering Secondary Affiliation: Civil and Environmental Engineering Research domains include: Sustainable logistics system planning Electric vehicle sharing networks Smart parking solutions Vehicle-grid integration Connected automated vehicle systems His publications address heterogeneous traffic flow modeling, EV sharing optimization, and intelligent transportation systems. Funded by multiple NSF grants totaling over $1.4 million, he leads projects on traffic fundamental diagrams and energy sponge services. Co-chair, IEEE Intelligent Transportation Systems Society (ITSS) Emerging Transportation Technology Testing (ET3) Committee