Kevin Riehl is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich, specializing in transportation systems and fairness mechanisms. He is affiliated with the Gruppe Strassenverkehrstechnik and holds a Master's degree in Genetics and Bioinformatics from the University of Cambridge and degrees in Industrial Engineering from Darmstadt University of Technology. Education: 2020–2021: Master's in Genetics and Bioinformatics, University of Cambridge 2018–2020: MSc Industrial Engineering with IT, Darmstadt University of Technology 2014–2017: BSc Industrial Engineering with IT, Darmstadt University of Technology His research focuses on multi-agent systems, resource allocation fairness, and karma-based mechanisms for equitable traffic management. Notable contributions include work on reproducibility in transportation simulations and karma economies for sustainable urban mobility. He has published extensively in journals like European Transport Research Review and npj Sustainable Mobility and Transport . Recent projects include BikeZ for cycling service modeling and microscopic traffic simulation courses. His work bridges computer science, economics, and transportation engineering to address systemic fairness in infrastructure.
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نمایش ۴۶-۴۹ از ۴۹ استاد راهنما
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Nasser Sabarمشاهده پروفایل
مدرس ارشد
Dr. Nasser Sabar is a Senior Lecturer in the Department of Computer Science & Information Technology at La Trobe University. His research focuses on developing adaptive algorithms using hyper-heuristics and evolutionary computation to address real-world optimization challenges, with applications in healthcare scheduling, transportation systems, and cloud computing. He has collaborated on projects involving anaesthetist rostering, patient admission scheduling, and freeway traffic optimization. Research Expertise: Artificial Intelligence Evolutionary Computation Operations Research Optimization Techniques Health Informatics Transportation Systems Key Contributions: Dr. Sabar’s work includes pioneering studies on dynamic scheduling algorithms for healthcare operations, cooperative lane-changing advisory systems for freeways, and hybrid methodologies for complex combinatorial problems. His research often bridges theoretical optimization frameworks with practical applications in industries such as logistics, transportation, and healthcare. Grants & Collaborations: Collaborative Research Project with ITMAN Pty Ltd (2022): Sensory Data Analysis Research Trends: His recent articles emphasize optimizing healthcare operations (e.g., patient admission scheduling, anaesthetist rostering) and transportation systems (e.g., freeway lane-drop bottlenecks, variable speed limits). He also explores machine learning advancements like evolutionary GANs and recurrent neural computing for stochastic networks.
Amirreza Kandiriمشاهده پروفایل
پژوهشگر ارشد
- Transportation Engineering
- Intelligent Transportation Systems (ITS)
- Infrastructure Resilience
- +۷ مورد دیگر
Dr. Amirreza Kandiri is a Postdoctoral Research Fellow at the School of Civil Engineering, University College Dublin (UCD), where he conducts advanced research in transportation engineering, intelligent transportation systems (ITS), and infrastructure resilience. He earned his PhD at UCD, focusing on AI-driven traffic prediction and optimization under variable speed limits. He also holds an MSc in Construction Management and a BSc in Civil Engineering. Education: PhD, University College Dublin MSc, Islamic Azad University BSc, Buali Sina University His research centers on the application of machine learning and data analytics to solve critical challenges in civil and transportation engineering. He specializes in real-time traffic management, smart mobility, structural materials modeling, and remote sensing for infrastructure monitoring. His work integrates predictive modeling, optimization, and uncertainty analysis to enhance safety, efficiency, and sustainability in urban systems. The recent publications of Dr. Kandiri reflect a strong interdisciplinary trend combining civil engineering with artificial intelligence. His work spans intelligent traffic control using autonomous agents, deep learning for InSAR data analysis, and machine learning models for predicting concrete properties. These studies highlight a consistent focus on optimization, model accuracy, and real-world applicability across transportation and construction domains. Scientific Awards: 2024 Dean’s Award, UCD STEM Early Career Researchers’ Symposium Recognized for one of the most cited papers in Applied Sciences Dr. Kandiri has been actively involved in academic service, including peer reviewing for journals and serving on the UTSG 2025 organizing committee. He has contributed to teaching at both University College Dublin and Chang’an University in China. While no formal students are listed, his collaborative research involves multiple co-authors across disciplines. He has not received mention of grants, but his research output suggests involvement in funded projects related to smart infrastructure and sustainable materials. His work is closely tied to real-world applications, particularly on the M50 motorway in Dublin, and leverages platforms like Google Scholar, ResearchGate, and ORCID (0000-0001-8683-7984) for dissemination. He is part of a growing collaboration network in transportation and structural engineering research.
Peter Kissmannمشاهده پروفایل
پژوهشگر
Dr. Peter Kissmann is a researcher in the Computer Science Department at Universität des Saarlandes, Germany, affiliated with the Foundations of Artificial Intelligence (FAI) Group. His work focuses on automated planning systems, particularly in optimal and net-benefit planning domains. Research Interests: His primary research areas include Artificial Intelligence, Automated Planning, Symbolic Reasoning, Heuristic Search, and Knowledge Representation. He specializes in BDD-based planning, variable ordering, and symbolic search algorithms for optimal planning. The 15 most recent publications reflect his sustained contributions to the International Planning Competitions (IPC) from 2008 to 2014, showcasing evolution in planner design, including support for conditional effects, bidirectional search, improved variable ordering, and integration of modern BDD libraries like CUDD. These works demonstrate deep technical innovation in planner efficiency, robustness, and scalability. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students or grant funding in the provided material. However, his leadership in developing and releasing competitive planning systems suggests involvement in research projects, possibly with student contributors or collaboration within the FAI group. Labs and Teams: He is a member of the Foundations of Artificial Intelligence (FAI) Group at Universität des Saarlandes, which develops advanced planning systems and participates in international competitions like the IPC.