جست‌وجوی استاد و پژوهشگر

• Catchment Modelling
• Urban Hydrology
• Climate Change Impact on Flood Estimation
• +۳ مورد دیگر

James Ball is an Adjunct Professor at the School of Civil and Environmental Engineering , University of Technology Sydney. With a career spanning academia, consulting, and government roles, his expertise focuses on catchment modeling , urban drainage systems , and climate change impacts on hydrology. He has held positions at UNSW Australia and contributed to foundational guidelines like Australian Rainfall and Runoff . Education: Doctor of Philosophy, Master of Engineering, and Bachelor of Civil Engineering from the University of Newcastle, Australia. Research Interests: Development of catchment modeling systems for urban environments, integration of deep learning in land use/land cover classification, hydrological software calibration with genetic algorithms, and stormwater-green infrastructure interactions such as green roofs. Recent Trends: His 2024–2021 publications emphasize agent-based groundwater-society interactions , deep learning for impervious surface estimation , and hybrid machine learning methods for dam reservoir evaporation under climate change scenarios. Articles also address flood modeling accuracy , scour mitigation structures for bridges, and socio-hydrology in resource conflicts. Leadership: Editor-in-Chief of the International Journal of River Basin Management , editorial roles in Urban Water Journal , Hydrology , and Water Science and Technology . Past Vice-President of IAHR and technical editor for Australian Rainfall and Runoff . Supervision: Available for Masters and PhD student supervision . Funded Research: Projects include stormwater modeling , hydrological ensemble prediction , phosphorus release studies , and stochastic rainfall generation for flood estimation.

Rui Yaoمشاهده پروفایل

مدرس

• Large-scale equilibrium modeling in multi-modal transport systems
• Individual mobility choice modeling
• Demand management strategies
• +۳ مورد دیگر

Rui Yao is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering (ENAC) and the Department of Civil Engineering. Additionally, he works as a Scientist in the Laboratory for Human-Oriented Mobility Eco-system (HOMES) within EPFL. His research focuses on large-scale equilibrium modeling in multi-modal transport systems, individual mobility choice modeling, and demand management strategies. Education: B.Sc. in Civil Engineering, Technion – Israel Institute of Technology Direct-track Ph.D. in Transportation Engineering, Technion – Israel Institute of Technology His research spans both theoretical and applied domains, including stochastic traffic equilibrium , multi-passenger ridesharing systems , perturbed utility models , and deep learning for choice analysis . He has contributed to advancements in data-driven route choice modeling , integrated equilibrium models for electrified logistics , and stable matching frameworks for mobility platforms . Rui Yao is affiliated with the Human-Oriented Mobility Eco-system (HOMES) lab at EPFL, which focuses on innovative transportation solutions. His work bridges theoretical modeling with real-world applications in smart mobility and transportation policy.

• Transportation Systems Optimization
• Operations Research
• Machine Learning
• +۷ مورد دیگر

Emma Frejinger is a full Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Science, Université de Montréal. She holds the Canada Research Chair in Demand-Driven Optimization of Transport Systems and the CN Chair in Railway Operations Optimization. Her work bridges operations research and statistical learning to solve large-scale transportation challenges. Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland Her research focuses on transportation network optimization , demand forecasting , and discrete choice modeling . She develops data-driven methodologies for railway operations, EV charging infrastructure, and traffic prediction, emphasizing scalability and real-world applicability. Recent publications highlight trends in integrating machine learning with combinatorial optimization , particularly for MIPs , competitive facility location , and stochastic transport systems . These works span freight logistics , urban mobility , and reinforcement learning applications . Scientific Awards: Two-time INFORMS Transport Science and Logistics Society best Ph.D. thesis award 2017 Grand Prix d'excellence en transport (freight category) She has supervised 21+ graduate students in topics ranging from locomotive routing to electric vehicle adoption , with projects funded by NSERC, FRQ, and industry partners like CN Rail and Purolator. Her lab affiliations include CIRRELT and OPTIM , focusing on simulation and optimization.

Günter Leugeringمشاهده پروفایل

استاد

• Optimization
• Optimal Control
• Partial Differential Equations
• +۵ مورد دیگر

Prof. Dr. Günter Leugering holds the Chair of Applied Mathematics 2 at Friedrich-Alexander University Erlangen-Nuremberg (FAU). His academic work focuses on mathematical optimization and control theory with applications to complex systems. His research interests span Optimization with Partial Differential Equations , Optimal Control , and Mathematical Modeling of physical systems. Specific focus areas include traffic flow modeling, gas network optimization, and multiscale simulation approaches. His work bridges theoretical mathematics with practical engineering applications, particularly in infrastructure systems. Prof. Leugering has been actively involved in numerous coordinated research programs including SFB-TRR 154 (Modeling, Simulation and Optimization Using Gas Networks), DFG-SPP 1253 (Optimization with Partial Differential Equations), and the DFG Cluster of Excellence Engineering of Advanced Materials (EAM). His public lectures at venues like the Planetarium Nuremberg demonstrate his commitment to science communication, with topics ranging from traffic jam mathematics to optimization for new materials. His research team participates in several major initiatives: SFB-TRR 154 subprojects on mixed integer-continuous dynamic systems and decomposition methods EAM Research Area A3 on Multiscale Modeling and Simulation DFG-SFB 603 subproject on mathematical optimization for registration problems EU Frame program STRAP: PLATO-N

Michael Kuhlمشاهده پروفایل

استاد

• Simulation
• Operations Research
• Healthcare Systems
• +۴ مورد دیگر

Michael Kuhl is a Professor in the Department of Industrial and Systems Engineering at Rochester Institute of Technology's Kate Gleason College of Engineering. His research focuses on simulation methodologies applied to autonomous mobile robots, healthcare systems, and logistics optimization. With appointments spanning teaching, research, and industry collaboration, he directs multiple projects in smart warehouse systems and stochastic input modeling. His educational background includes a Ph.D. and M.S. in Industrial Engineering from North Carolina State University and a B.S. in Industrial Engineering from Bradley University. His research interests center on simulation , operations research , and optimization with applications across healthcare, manufacturing, and logistics domains. Analysis of his recent publications reveals strong trends in autonomous material handling systems (62% of recent works), simulation optimization (28%), and educational pedagogy (10%). His work increasingly integrates machine learning with traditional simulation methods, particularly in indoor localization and warehouse robotics. Healthcare applications remain a consistent thread throughout his career, evolving from hospital supply chain modeling to modern surgical suite optimization. His academic leadership includes directing the REVS K-12 STEM outreach program and developing simulation-based educational modules for assembly systems engineering. Current teaching responsibilities include Systems Simulation (ISEE-510), Simulation Analysis (ISEE-708), and Engineering Value Creation (ISEE-773). Recent industry collaborations include partnerships with Simcona on smart warehouse field experiments, as highlighted in March 2022 Rochester Beacon coverage. His laboratory work focuses on developing intelligent materials handling systems where autonomous robots negotiate right-of-way in warehouse environments using advanced path planning algorithms.

• Artificial Intelligence
• Machine Learning and Deep Learning
• Generative AI
• +۷ مورد دیگر

Sasanka Potluri serves as Professor of General Computer Science and Medical Informatics at Karlshochschule (Karlsruhe University of Education) since September 2025. He is actively engaged in teaching and research within the Department of Computer Science and Medical Informatics, focusing on the intersection of artificial intelligence and healthcare applications. His academic leadership spans multiple research projects aimed at transforming healthcare delivery through technological innovation. His educational background includes: Dr.-Ing. in Electrical Engineering and Information Technology from Otto-von-Guericke University Magdeburg (Germany) Dipl.-Ing. in Information Technology from Alpen-Adria University Klagenfurt (Austria) B. Tech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (India) Professor Potluri's research spans the cutting edge of artificial intelligence applications in healthcare, with particular expertise in machine learning, deep learning, and generative AI. His work bridges technical innovation with practical healthcare solutions, focusing on clinical decision support systems, biomedical statistics, and digital signal processing. He has developed novel approaches for healthcare logistics optimization, synthetic health data generation, and addressing digital health equity issues. His research methodology combines theoretical rigor with practical implementation, often working at the intersection of computer science, medical informatics, and systems engineering. His publication record reveals a consistent trajectory from industrial control systems security toward healthcare applications of AI. Early work focused on intrusion detection in industrial control systems using deep learning techniques, while recent publications demonstrate a strategic shift toward healthcare logistics, patient transportation optimization, and blood product management. This evolution reflects both his technical expertise in AI and his commitment to addressing critical challenges in healthcare delivery systems. His research increasingly incorporates generative AI approaches to solve complex healthcare resource allocation problems. Professional service includes: Member and Reviewer at GMDS (German Society for Medical Informatics, Biometry and Epidemiology) since 2024 Reviewer for European Federation for Medical Informatics since 2024 Reviewer for IEEE Transactions on Network and Service Management since 2020 Reviewer for Elsevier Journals including Engineering Applications of Artificial Intelligence since 2017 Professor Potluri actively supervises B.Sc, M.Sc, and PhD students in medical informatics, AI applications, generative AI, clinical decision support systems, and healthcare logistics. His current research projects focus on hospital resource and process optimization, synthetic health data generation, digital health equity studies, and generative AI in healthcare. He previously held research positions as Junior Research Group Leader at University Hospital Jena, Project Leader at Otto von Guericke University Magdeburg, and Research Assistant for EU Projects, building a strong foundation for his current interdisciplinary work.

Karol Żarskiمشاهده پروفایل

مدرس ارشد

• Transportation Engineering
• Traffic Modeling
• Road Safety
• +۴ مورد دیگر

Karol Żarski serves as a Senior Lecturer at Gdańsk University of Technology, specializing in transportation engineering and traffic modeling. His academic work focuses on applying simulation methods to solve practical urban mobility challenges. His research interests span multiple critical areas of modern transportation systems, including microscopic traffic simulation , road safety assessment , sustainable urban mobility planning , and intelligent transport systems . Dr. Żarski's work bridges theoretical modeling with practical applications for improving urban transportation networks, particularly through analyzing vehicle interactions, emissions, and safety metrics. His publication record from 2019-2024 reveals a strong focus on traffic flow optimization across various transportation modes. Recent work examines car-following models' impact on safety and emissions at signalized intersections (2024), bicycle traffic modeling for sustainable cities (2021), and speed management effects on highway safety (2020). His research consistently applies simulation methodologies to evaluate transportation interventions before implementation. Dr. Żarski frequently collaborates with researchers like J. Oskarbski, demonstrating strong interdisciplinary connections within the transportation research community. His work has practical implications for urban planners and transportation engineers seeking evidence-based solutions for traffic management challenges.

Najmeh Salehiمشاهده پروفایل

استادیار

• Applied Mathematics
• Mathematical Modeling
• Control Theory
• +۳ مورد دیگر

Najmeh Salehi serves as an Assistant Professor in the Department of Mathematics and Computer Science at Saint Mary's College of California, with research centered on applied mathematics and real-world problem-solving through mathematical modeling and computational techniques. Her work bridges theoretical frameworks with practical applications in transportation systems and environmental phenomena. BSc. from Shahrood University of Technology MSc. from Sharif University of Technology PhD from Temple University Her research expertise spans Applied Mathematics , Mathematical Modeling , Control Theory , and Scientific Computation , with specialized focus on traffic flow dynamics, conservation laws, and optimal control of spatial propagation fronts. She develops macroscopic traffic models validated through SUMO microsimulations and applies mathematical frameworks to diverse challenges like sourdough fermentation analysis and Lego robot path optimization, emphasizing data-driven solutions for complex systems. Her scholarly output includes publications in high-impact journals such as Mathematical Models and Methods in the Applied Sciences (2022), demonstrating consistent innovation in modeling propagation fronts and traffic network dynamics. Her work reveals a strong trajectory in merging theoretical control theory with transportation engineering and environmental applications. While no formal awards are documented, her professional activities include significant teaching contributions across calculus and computational courses at Temple University and community engagement through STEM outreach initiatives like Sonia Kovalevsky Day and cryptography workshops for children. Her research collaborations span industry partnerships (e.g., Amber Grain Bakery for sourdough modeling) and academic labs including Penn State's group under Professor Alberto Bressan. She maintains active research teams focused on traffic flow validation using SUMO simulations, autonomous vehicle testing with Duckiebots, and Raspberry Pi robotics development, alongside postdoctoral work on conservation law applications for contaminant spread control.

Ursula Voßمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Urban Environmental Simulation
• +۳ مورد دیگر

Prof. Dr. Ursula Voß is a Professor of Applied Mathematics at Stuttgart University of Applied Sciences (HFT Stuttgart) since 2014, serving in the Department of Mathematics and as head of the Mathematics/BPS examination board. She teaches Applied Mathematics for the Bachelor's program in Applied Mathematics and AI and the Master's program in Mathematics. Her educational background includes: Diploma in Mathematics (1993), University of Karlsruhe (KIT) PhD in Mathematics (1997), University of Karlsruhe (KIT) Post-Doctoral Researcher, École Polytechnique Palaiseau, France (1997-1998) Prof. Voß specializes in computational fluid dynamics for urban environmental modeling, focusing on air pollution dispersion, wind flow in complex urban geometries, and energy management systems. Her research integrates 3D city models, geospatial visualization, and high-resolution simulation techniques to address urban sustainability challenges. Her publication record demonstrates consistent innovation in urban wind simulation, with recent work emphasizing vehicle-induced turbulence modeling, semi-automated CFD workflows, and collaborative VR environments for environmental analysis. These contributions advance urban planning through precise modeling of street canyon effects and pollutant dispersion. She leads major interdisciplinary projects including iCity 2: Ensim4iCity (urban energy management simulation) and iCity 2: SenSim4iCity (sensor-integrated environmental modeling), collaborating with geospatial specialists and VR developers to create integrated urban simulation frameworks.

Stanislava Stoilovaمشاهده پروفایل

دانشیار

• Uniform Distribution of Series
• Monte Carlo Methods
• Quasi-Monte Carlo Methods
• +۲ مورد دیگر

Assoc. Prof. Dr. Stanislava Stoilova is Deputy Dean for Scientific and International Activities at the Faculty of Transport Engineering, University of Architecture, Civil Engineering and Geodesy (UACEG). She holds a permanent position in the Department of Mathematics, teaching courses including Traffic Flow Theory, Mathematical Analysis, and Probabilities and Statistics. Education: Secondary: St. St. Cyril and Methodius, Radomir (1992). PhD: Southwestern University 'Neofit Rilski' (2003), dissertation titled Orthonormal Functional Systems and Uniform Distribution of Series . Fluent in Russian and English. Research Focus: Her work spans quantitative network analysis, pseudo-random number generation, and applications of low-discrepancy series. She specializes in Monte Carlo simulations for engineering problems, with collaborations in Hungary and Slovakia. Leadership: Heads UACEG's mathematics competition teams and organizes academic contests. Previously held roles include Researcher at the Bulgarian Academy of Sciences (2004–2011) and progressive academic positions at UACEG since 2011.

• Wireless Communication
• Wireless Sensing
• Wireless Network Systems
• +۵ مورد دیگر

Professor Jiang Liu is a distinguished academic at Waseda University's Faculty of Science and Engineering, where he serves as a Professor in the Global Center for Science and Engineering. Previously, he held positions as Associate Professor (2017-2024), Assistant Professor (2014-2017), Junior Researcher (2012-2014), and Research Assistant (2009-2012), demonstrating a steady progression in his academic career at the same institution. Dr. Liu holds a Doctor of Science and Master of Science in Global Information and Telecommunication Studies from Waseda University. His professional affiliations include the Institute of Electrical and Electronics Engineers (IEEE), IET, and the Institute of Electronics, Information and Communication Engineers (IEICE). His research spans multiple cutting-edge domains with a strong focus on Wireless Communication and Sensing , Wireless Network Systems , and Optical Wireless Communications . Dr. Liu has pioneered non-contact medical measurement techniques using microwave and infrared technologies for blood pressure monitoring, blood glucose measurement, and arteriosclerosis detection. His work bridges engineering and healthcare applications, developing AI-powered diagnostic systems for cardiovascular diseases, breast tumor classification, and autism detection. He also contributes significantly to energy systems research, particularly in photovoltaic power forecasting and building energy management. Dr. Liu has an impressive publication record with 109 papers, accumulating 868 citations (Scopus) and 1190 citations (Google Scholar), with h-indices of 14 and 15 respectively. His recent work shows a strong trend toward interdisciplinary research combining wireless communications, machine learning, and healthcare applications, with numerous 2024-2025 publications reflecting his active research program. His scientific contributions include innovative approaches to non-contact medical diagnostics, wireless communication systems, and energy-efficient computing. Dr. Liu's work on wireless-powered edge computing networks, non-invasive blood glucose monitoring, and brain-computer interfaces has established him as a leader in applying communication technologies to solve healthcare challenges. As an advisor and researcher, Dr. Liu leads projects that integrate multiple disciplines, from developing machine learning models for medical diagnostics to creating novel wireless communication frameworks for Internet of Vehicles and edge computing applications. His laboratory focuses on practical implementations of theoretical concepts, with many projects resulting in tangible healthcare and communication technologies.

Annie Liangمشاهده پروفایل

مدرس ارشد

• Air Traffic Management
• Trajectory-Based Operations
• Machine Learning
• +۷ مورد دیگر

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
• +۵ مورد دیگر

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
• +۴ مورد دیگر

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 Hoeflerمشاهده پروفایل

استاد

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +۳ مورد دیگر

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.