جستجوی استاد راهنمای دانشگاهی

Lacra Pavelمشاهده پروفایل

استاد

• Game Theory
• Control Theory
• Optimization
• +۷ مورد دیگر

Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.

• Operational Research
• Logistics
• Stochastic Optimization
• +۳ مورد دیگر

Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Com. from McGill University, and both an M.Sc. and Ph.D. from the University of Montreal. His research focuses on operational research with applications in logistics, transportation, energy systems, and telecommunications. He is affiliated with several prestigious research centers including the Institute for Data Valorization (IVADO), the Trottier Energy Institute (IET), and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport (CIRRELT). Professor Gendreau's research interests span operational research, with particular emphasis on stochastic optimization methods applied to transportation and logistics problems, energy systems management, and telecommunications. His work often addresses real-world challenges through mathematical modeling and algorithm development, with applications ranging from bike-sharing systems to emergency response planning and electricity grid management. The analysis of his recent publications reveals a strong focus on vehicle routing problems under uncertainty, maintenance optimization, and the integration of stochastic programming with machine learning techniques for improved decision making. Professor Gendreau has received numerous prestigious awards recognizing his contributions to the field of operations research. In 2022, he was named a Fellow of the International Federation of Operational Research Societies (IFORS). In 2010, he was awarded Fellow status by INFORMS (Institute for Operations Research and the Management Sciences). Most notably, in November 2015, he received the Robert M. Herman Lifetime Achievement Award from the Transportation Science and Logistics Society of INFORMS, which is considered the most prestigious distinction for operational researchers working in logistics and transportation. Throughout his career, Professor Gendreau has supervised 25 doctoral students and 18 master's students, contributing significantly to the development of the next generation of operations research experts. His research has been supported by numerous grants from organizations including NSERC (Natural Sciences and Engineering Research Council of Canada), with expertise recognized in Operational Research and Management Science (NSERC subject 1601) and Logistics (NSERC subject 1603). Professor Gendreau is actively involved in several research teams and laboratories, particularly those focused on data valorization, energy systems, and transportation logistics. His current work continues to push the boundaries of stochastic optimization and its applications to complex real-world problems, with recent publications addressing challenges in urban transportation, energy management, and emergency response systems.

FATME YUSEINOVA RASHIDOVAمشاهده پروفایل

مدرس ارشد

• Computer Science
• Educational Technology
• Cybersecurity
• +۳ مورد دیگر

Fatme Yuseinova Rashidova is a Senior Lecturer at the Technical University - Gabrovo , affiliated with the College of Engineering and the Department of Automation, Information and Control Technology . Her work bridges computer science , educational technology , and systems engineering . Research Focus: Automated scheduling, distance learning platforms, cybersecurity in Industry 4.0, and AI applications in education. Publications: 29 scientific works, including a 2025 textbook on internet-based systems and recent papers on timetabling optimization, SWOT analysis in education, and AI challenges. Projects: Participated in 14 research initiatives, including electronic voting systems, smart medicine dispensers, and cybersecurity frameworks for industrial environments. Contributions: Developed centralized timetabling systems, virtual admission environments, and online testing platforms. Her projects emphasize reducing administrative burdens, improving accuracy in educational processes, and enhancing student satisfaction.

N. van Oortمشاهده پروفایل

دانشیار

• Public Transport
• Transport Demand Analysis
• Mobility Systems
• +۳ مورد دیگر

N. van Oort is an Associate Professor in the Department of Transport, Mobility and Logistics at Delft University of Technology. He specializes in public transport systems, sustainable mobility, and data-driven decision making. His research focuses on optimizing transport networks, electrification strategies, and future mobility scenarios. He has led the Smart Public Transport Lab, contributing to projects like the CriticalMaaS initiative (2019-2023), exploring Mobility as a Service dynamics. His work spans over 249 publications and 8 recognized prizes, including awards for innovative transport solutions and research excellence. Van Oort actively engages in public discourse, appearing in media like NPO Radio 1 and Trouw, discussing topics such as 2050 mobility futures and challenges in Dutch public transport. He has advised on policy matters and serves as a keynote speaker at industry events.

Tommaso Schettiniمشاهده پروفایل

استادیار

• Electric Vehicle Charger Location
• Public Transit
• Metro Timetabling
• +۴ مورد دیگر

Dr. Tommaso Schettini is an Assistant Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on transportation systems optimization, including electric vehicle charger location, metro timetabling, and demand-driven scheduling strategies. He holds an ORCID identifier (0000-0003-2578-1539) and supervises Master's and PhD students in Industrial Engineering. His work integrates operations research techniques like Benders decomposition and metaheuristics to solve real-world transportation and manufacturing challenges. Research Areas: Electric Vehicle Infrastructure Planning, Metro Timetabling Strategies, Integer Programming, Discrete Simulation-based Optimization, and Combinatorial Optimization. Notable contributions include developing pattern-based algorithms for short-turning metro lines and optimizing micro-mobility integration with public transit. His articles span 2016–2024, emphasizing demand-driven approaches in transportation and manufacturing systems.

Rolf Wankaمشاهده پروفایل

استاد

• Algorithms
• Combinatorial Optimization
• Swarm Intelligence
• +۳ مورد دیگر

Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.

Pasi Lautalaمشاهده پروفایل

استاد

• Rail Transportation Engineering
• Highway-Rail Grade Crossing Safety
• Multimodal Transportation Logistics
• +۴ مورد دیگر

Pasi Lautala is a Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University (Michigan Tech) and currently serves as the Associate Dean for Research. He holds a BS from Tampere University of Technology (Finland) and MS/PhD from Michigan Tech. His research focuses on rail and highway transportation engineering, with emphasis on grade crossing safety, multimodal logistics, sustainability, railway capacity analysis, and engineering education development. Since 2007, Lautala has directed the Rail Transportation Program (RTP) within the Michigan Tech Transportation Institute (MTTI), expanding rail research collaborations across disciplines. He leads over $10M in external research funding, including projects on trespasser safety, freight logistics, and lifecycle analysis. Lautala is a key figure in rail education revitalization, serving as Rail Group Chair at the Transportation Research Board (TRB) and advising the Michigan Commission for Supply Chain Logistics. His teaching spans courses like Transportation Engineering, Railroad Design, and Logistics Management. Lautala has advised numerous undergraduate and graduate projects, emphasizing industry partnerships. Recent contributions include developing in-vehicle auditory alerts for rail crossings and AI-driven safety systems like RAIILS. Key collaborations include the Federal Railroad Administration (FRA) on grade crossing safety ($641K+ projects), U.S. DOT on rail modal analysis, and Battelle on connected vehicle systems. He mentors the Tracks to the Future youth program and co-leads international rail education initiatives.

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +۳ مورد دیگر

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

• Combinatorial Optimization
• Operations Research
• Stochastic Programming
• +۵ مورد دیگر

Hanyu Gu is a Senior Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), part of the Faculty of Science. He holds a PhD in Power Engineering and Automation from Shanghai Jiao Tong University (1999) and has extensive industry experience in telecommunications, airline optimization, and mining. His research focuses on combinatorial optimization, decomposition methods, stochastic programming, and machine learning applications. Notable awards include second place in the 2020 ROADEF competition. He collaborates with institutions like the UTS Transportation Research Centre and has contributed to projects such as optimisation engines for airline management and underground mining algorithms. Current research explores hybrid algorithms, Bayesian optimisation, and scheduling under uncertainty. Education: Bachelor in Industrial Automation, Shanghai Jiao Tong University (1994) Master in Control Theory and Application, Shanghai Jiao Tong University (1997) PhD in Power Engineering and Automation, Shanghai Jiao Tong University (1999) Industry Experience: ZTE (1999–2001): Senior Wireless Communication Engineer CTI, Melbourne (2007–2011): Airline Management Optimisation Researcher NICTA (2011–2013): Underground Mining Optimisation Researcher Grants: ARC Linkage Project LP0883855 (2008–2012): Developed optimisation tools for transportation crewing, valued at $840,000. Research interests span decomposition methods for large-scale problems (e.g., airline scheduling), stochastic programming for resource sharing, and hybridisation of mathematical programming with constraint programming. Recent work includes Bayesian optimisation for knapsack problems and relax-and-solve algorithms for project scheduling. His articles frequently address optimisation in logistics, healthcare, and transportation, emphasizing practical industry applications and algorithmic innovation. Awards: Second place in the ROADEF 2020 competition for maintenance planning solutions. Advising & Grants: Supervises Masters and PhD students in operations research and optimisation. Collaborates with Ausgrid, UGL, and ANC on optimisation projects (e.g., employee training timetabling, logistics). Active in the Optimisation Group of UTS Transportation Research Centre, he bridges academic research with real-world challenges in scheduling, logistics, and resource management. Ongoing efforts include advancing metaheuristics and integrating machine learning with traditional optimisation techniques.

Luca Di Gasperoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Combinatorial Optimization
• Metaheuristics
• +۱ مورد دیگر

Luca Di Gaspero is an Associate Professor of Information Technology at the University of Udine, specializing in metaheuristic optimization techniques. His research enhances combinatorial optimization through hybridization of algorithms for scheduling, routing, and industrial applications. Research spans artificial intelligence in optimization, scheduling algorithms for manufacturing/healthcare, and metaheuristic framework development. Recent publications focus on LLMs in optimization, parallel batch scheduling, and energy-efficient manufacturing. Key Contributions: Developed EasyLocal++ framework for local search algorithms Advanced multi-neighborhood simulated annealing techniques Applied metaheuristics to healthcare logistics and emergency services

Marika Karbsteinمشاهده پروفایل

پژوهشگر

• Operations Research
• Mathematical Optimization
• Public Transport Planning
• +۳ مورد دیگر

Dr. Marika Karbstein is a Researcher at the Zuse Institute Berlin (ZIB), where she has been employed since April 2006. Affiliated with the DFG Research Center Matheon "Mathematics for key technologies" from 2006-2014, she earned her doctoral degree in Mathematics from Technische Universität Berlin in 2013. Her work bridges theoretical optimization and practical public transport applications. Her academic background includes: 1999-2005: Studies of Applied Mathematics (Wirtschaftsmathematik) at Technische Universität Berlin 2003-2006: Student assistant at Konrad-Zuse-Zentrum Berlin 2013: Doctoral degree in Mathematics (Dr. rer. nat), Technische Universität Berlin Dr. Karbstein specializes in mathematical optimization for public transport systems, focusing on strategic planning, service design, and passenger behavior modeling. Her research integrates timetabling, line planning, and passenger routing through advanced integer programming and network optimization techniques. She develops practical solutions for infrastructure design, crew scheduling, and fare planning, emphasizing real-world applicability in urban transport networks. Her methodology combines theoretical rigor with implementation-focused approaches to improve system efficiency and user experience. Her publication record shows consistent innovation in periodic timetable optimization and integrated transport modeling. Early work established foundations in Steiner tree problems for transport networks, evolving into sophisticated frameworks for passenger routing and line planning. Recent contributions emphasize direct connections and transfer minimization, reflecting industry needs for seamless passenger journeys. The research demonstrates strong interdisciplinary connections between operations research, computer science, and transportation engineering. Her scientific recognition includes: Diploma Thesis Award of the German Operations Research Society (2006) Runner-Up for Richard-Rado-Prize (2014) Dissertation Award of German Operations Research Society (2014) VBKI Wissenschaftspreis (2014) Tucker Prize Finalist (2015) Best Paper Award at ATMOS 2013 As a core member of the DFG Research Center Matheon, Dr. Karbstein contributed to major collaborative projects funded by the German Research Foundation. Her work with Ralf Borndörfer at ZIB has led to practical implementations including Potsdam's 2010 line plan optimization. While specific grants aren't detailed, her research has been sustained through institutional support at ZIB and Matheon's interdisciplinary framework. She actively collaborates with transport operators to translate theoretical advances into operational improvements. Dr. Karbstein operates within ZIB's renowned applied mathematics environment, leveraging its high-performance computing resources. Her work extends through Matheon's consortium connecting Berlin's mathematics community. Current research focuses on integrating passenger behavior models with real-time scheduling systems, advancing the frontier of data-driven transport optimization.

• Optimization under Uncertainty
• Robust Optimization
• Combinatorial Optimization
• +۷ مورد دیگر

Christina Büsing is a Professor in Combinatorial Optimization at RWTH Aachen University. She leads the Teaching and Research Group on Combinatorial Optimization and is a principal investigator in the UnRAVeL Graduate College. Her research focuses on optimization under uncertainty , robust optimization , and combinatorial optimization , with applications to healthcare , energy systems , and transportation logistics . Alumni of TU Berlin, WWU Münster, and Universidad Complutense de Madrid Junior Professor for Robust Planning in Medical Care at RWTH Aachen (2016-2021) Her methodological expertise spans exact algorithms , heuristics , and complexity theory , with recent publications addressing network flows, facility location, and healthcare scheduling. She has received awards for teaching excellence and gender equality advocacy. 2022 Brigitte Gilles Award for women in science 2019 FAMOS Award for family-friendly leadership 2018 Best Teaching Award at RWTH Aachen Her work combines theoretical rigor with practical applications in urban railway traffic management , solar power systems , and agent-based pandemic response modeling .

• Innovative Learning Environments
• Educational Philosophy
• Education Policy
• +۳ مورد دیگر

Leon Benade is a Professor in the School of Education at Edith Cowan University (ECU). He holds a Doctor of Education from New Zealand (2012) and has established a national and international reputation in learning environments research. His work focuses on 21st-century learning imperatives, innovative teaching practices, and bicultural education policy. Benade is a Co-Investigator in the 2021 Marsden Fund project exploring Māori flexible learning spaces. He has received several awards, including the 2017 Emerging Researcher Award from AUT. **Research Interests** Innovative Learning Environments Educational Philosophy Teachers' Work Educational Policy Cultural Spaces in Education **Awards** 2017 Auckland University of Technology Emerging Researcher Award 2021 Marsden Fund Co-Investigator 2016 Innovation in Teaching Award Benade supervises PhD/Master's students on topics like sustainability education and rural Colombian education policy. He has held editorial roles at journals like New Zealand Journal of Educational Studies and New Zealand Journal of Teachers' Work . His recent publications (2021–2025) address Māori spatial pedagogy, flexible learning spaces, and peer review ethics in academic publishing.

Michael Ritterمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• Integer Programming
• Operations Research
• +۴ مورد دیگر

Dr. Michael Ritter is a researcher and academic staff member at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. His primary role involves advising Master's students in Mathematics programs and coordinating administrative duties for academic affairs. He is part of the Discrete Optimization research group and collaborates with professors like Stefan Weltge and Peter Gritzmann on advanced optimization projects. His research focuses on Combinatorial Optimization, Integer Programming, and real-world applications such as flight schedule design, semiconductor manufacturing optimization, and logistics systems. He has contributed to projects involving automated driving systems, production network resilience, and low-power semiconductor design. Ritter teaches courses on Discrete Optimization and Case Studies in Optimization, balancing theoretical instruction with practical problem-solving. He has co-advised numerous theses on topics ranging from supply chain optimization to timetabling algorithms, demonstrating his commitment to both academic research and student mentorship. His work bridges mathematical theory with industrial applications, addressing challenges in manufacturing, transportation, and electronics through rigorous optimization frameworks. He maintains an active publication record in reputable journals and conferences, reflecting his expertise in interdisciplinary optimization research.

• Answer Set Programming
• Artificial Intelligence
• Logic Programming
• +۴ مورد دیگر

Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.