جست‌وجوی استاد و پژوهشگر

David REYمشاهده پروفایل

استاد

• Optimization
• Integer Programming
• Bilevel Optimization
• +۷ مورد دیگر

David Rey is a Professor at SKEMA Business School in Sophia Antipolis, France. His research focuses on optimization, game theory, and artificial intelligence applied to transportation and logistics. He holds a PhD in Operations Research from Université Grenoble Alpes (2012) and a Habilitation à Diriger des Recherches (2023) from Université de Toulouse. Previously, he was a Senior Lecturer at UNSW Sydney (2016–2021) and has held postdoctoral positions at rCITI UNSW. He leads the URBANE project, exploring green last-mile delivery solutions, and serves on editorial boards like Transportation Letters . Awards include multiple ARC grants and the INFORMS TSL Best Paper Award Committee role (2025). Education: Habilitation à Diriger des Recherches (2023), Operations Research, Université de Toulouse PhD (2012), Operations Research, Université Grenoble Alpes Master (2008), Sciences/Mathematics, Pontifical Catholic University of Rio de Janeiro Engineering Degrees (2004–2005), Electrical Engineering, University of Montpellier Research Interests: David’s work emphasizes optimization techniques (bilevel, stochastic, mixed-integer programming), game theory, and AI applications in transport systems. Key areas include drone delivery, autonomous vehicle integration, congestion pricing, and resilient logistics networks. His methods address real-world challenges like pandemic response, electric vehicle charging infrastructure, and fair resource allocation. Grant Highlights: URBANE (EU Horizon-funded), 2022–present 4 ARC grants as Lead Chief Investigator, including projects on concrete mixes, ethics in transport systems, and workforce logistics optimization Advising & Grants: Directed/co-directed 15+ PhD theses across SKEMA and UNSW Sydney Active in grant writing and editorial roles, including Transportation Science and Logistics Society Workshop (2024–2025) Labs/Teams: Leads the SKEMA Centre for Analytics and Management Science, focusing on analytics-driven solutions for business and urban systems.

Marc-Antoine Weisserمشاهده پروفایل

پژوهشگر

• Network Optimization
• Algorithm Design
• Graph Theory
• +۳ مورد دیگر

Marc-Antoine Weisser is a researcher with a focus on network optimization, algorithm design, and graph theory. His work spans telecommunications, electrical networks, and computational complexity. He has contributed to studies on inter-domain network hierarchies, optical network optimization, and combinatorial problems such as Steiner trees and bin packing. Weisser's research often involves developing polynomial and approximation algorithms for real-world network challenges. Key research areas: Network topology analysis, algorithmic design for resource allocation, and optimization in electrical/optical networks His publications highlight contributions to congestion avoidance mechanisms, optical ring networks, and inter-domain routing architectures. Weisser collaborates frequently with institutions like the University of ... [university name missing in source text].

Jonathan Bardمشاهده پروفایل

استاد

• Operations Research
• Industrial Engineering
• Logistics Optimization
• +۶ مورد دیگر

Jonathan Bard is a Professor of Operations Research & Industrial Engineering at the University of Texas at Austin, holding the Industrial Properties Corporation Endowed Faculty Fellowship in Engineering. He serves as Associate Director of the Center for the Management of Operations Logistics and Assistant Graduate Advisor for the Manufacturing Systems Engineering Program. His academic journey includes a D.Sc. in Operations Research from George Washington University, an M.S. in Aeronautics & Astronautics from Stanford University, and a B.S. in Aeronautical Engineering from Rensselaer Polytechnic Institute. Dr. Bard's research focuses on algorithm development for airline operations, vehicle routing, machine scheduling, and large-scale optimization. He is internationally recognized for expertise in bilevel programming and postal operations. His work integrates decomposition techniques for hierarchical planning and multicriteria decision-making in socio-economic systems. Bard advises government agencies and corporations, and contributes to editorial boards of journals like International Journal of Production Research and IEEE Transactions on Engineering Management . His recent articles address challenges in freight rail scheduling, kidney exchange systems, waste management optimization, and healthcare logistics. Awards include Fellowships from INFORMS and IIE, and Senior Membership in IEEE. Bard’s consulting spans industries, emphasizing real-world applications of operations research principles.

Roger Zimmermannمشاهده پروفایل

استاد

• Multimedia and Distributed Systems
• Multimedia and Mobile Networking
• Spatio-temporal Data Management
• +۸ مورد دیگر

Roger Zimmermann is a Full Professor at the School of Computing, National University of Singapore (NUS), where he is also a Co-PI at the Grab-NUS AI Lab and leads the Location AI project. He previously served as Deputy Director of the NUS Smart Systems Institute (SSI) and Co-Director of the Centre of Social Media Innovations for Communities (COSMIC), both funded by Singapore’s National Research Foundation (NRF). Before joining NUS, he was a Research Area Director and Research Assistant Professor at the University of Southern California (USC). Ph.D. in Computer Science, University of Southern California (1998) M.S. in Computer Science, University of Southern California (1994) His research focuses on multimedia systems , spatio-temporal data management , streaming media architectures (especially DASH), machine learning applications , AR/VR , and location-based services . He leads the Media Management Research Lab (MMRL) at NUS, which conducts cutting-edge work in distributed multimedia and intelligent systems. His work combines theoretical depth with real-world applications in urban computing, smart mobility, and immersive media. The recent publications reflect a strong trend toward multimodal learning , spatio-temporal AI , adaptive streaming , and urban intelligence . His team explores zero-shot learning, 3D scene understanding, traffic forecasting, and open-vocabulary audio-visual segmentation, often leveraging foundational models and deep neural architectures. There is a clear emphasis on real-time, scalable systems for smart cities and immersive experiences. Dr. Zimmermann has received numerous accolades, including: DASH-IF Excellence in DASH Award (multiple years) Best Paper Awards at ACM SIGSPATIAL, IEEE ICME, and ACM MMSys Silver Award at ACM MMSys 2020 Grand Challenge IEEE Communications Society Best Editor Award (2017) ACM Distinguished Member (2017) Top 1% Publons Reviewer in Computer Science (2018) He has advised numerous students and led major research initiatives funded by MOE, NRF, A*STAR, NSF, and industry partners like Seagate, Intel, and HP. He has served as General Chair for IEEE MIPR 2023, ACM Multimedia 2020, and IEEE ISM 2015, and as TPC Co-Chair for several top-tier conferences. His editorial roles include Associate Editor for IEEE Transactions on Multimedia (TMM), ACM TOMM, and IEEE OJ-COMS. He leads the Media Management Research Lab (MMRL) , which focuses on intelligent multimedia systems, spatiotemporal data mining, and immersive media technologies. The lab develops scalable solutions for real-world challenges in urban computing, smart transportation, and interactive media.

Meead Saberi Kalaeeمشاهده پروفایل

دانشیار

• Traffic Flow Theory
• Transport Network Modelling
• Mixed Autonomy Networks
• +۳ مورد دیگر

Dr. Meead Saberi Kalaee is an Associate Professor at the School of Civil and Environmental Engineering at the University of New South Wales (UNSW), Sydney. His academic journey includes a PhD in Transportation Systems Analysis and Planning from Northwestern University (2013), MSc in Transportation Engineering from Portland State University (2010), and BSc in Civil Engineering from Ferdowsi University of Mashhad (2007). He leads the CityX research lab under rCITI, focusing on urban mobility through data analytics, simulation, and network modeling. Current Research : Traffic flow theory, mixed autonomy networks, pedestrian dynamics, active transport modeling, and AI-assisted infrastructure mapping (via footpath.ai spinout) Selected Grants : ARC Linkage Grant LE240100118 (National Cycling Data Platform), ARC Discovery DP220102382 (AI footpath modeling), DP210102089 (Sustainable bicycling exposure), LP190100650 (Logistics optimization) Recent Publications (2023–2025) span advanced topics like pedestrian traffic assignment , mixed autonomy emissions , reinforcement learning for vaccine allocation , and AI-driven transport equity , with methodological innovations in simulation-based optimization and network fundamental diagrams. Industry Collaborations include Adiona, Mitsubishi Heavy Industries, Cisco, KPMG, and government bodies like Transport for NSW and VicRoads. Supervision : Current PhD projects on traffic signal optimization, pedestrian activity estimation, and AI mapping. Former students' work addresses autonomous driving, dynamic demand modeling, and urban safety.

Jordi Domingo-Pascualمشاهده پروفایل

استاد

• 5G Network Architecture
• Software-Defined Networking (SDN)
• Locator/Identifier Separation Protocol (LISP)
• +۱۰ مورد دیگر

Jordi Domingo-Pascual is a Full Professor at the Universitat Politècnica de Catalunya - BarcelonaTech (UPC), affiliated with the Department of Computer Architecture within the Faculty of Informatics of Barcelona (FIB). He is a member of the UPC CBA - Broadband Communications Systems and the IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group. His research spans networking, telecommunications, and ethical aspects of technology design. Research Interests: His primary areas of expertise include 5G network architectures, software-defined networking (SDN), Locator/Identifier Separation Protocol (LISP), traffic engineering, quality of service (QoS), network monitoring, and ethics in engineering education. He investigates both technical and societal dimensions of emerging network technologies. Publication Trends: His recent scholarly output (2020–2022) emphasizes SDN controller placement optimization, open-source traffic engineering tools, and the integration of ethical considerations in engineering curricula—particularly in the context of AI and pandemic-era digital tracing applications. Earlier works focus on ATM multicasting, QoS, and broadband network characterization. Scientific Awards: Premi o reconeixement Advising and Grants: While specific advisees are not listed, his extensive research activity—including participation in European projects—suggests involvement in supervising students and securing competitive R&D funding. His work on traffic engineering, SDN, and LISP indicates leadership in national and international collaborative grants. Labs and Research Groups: He is actively involved in the UPC CBA research group focused on broadband and communication systems and the IDEAI-UPC group advancing data science and AI. These affiliations reflect a strong interdisciplinary research profile bridging networking and intelligent systems.

• Control Systems
• Mechatronics
• Vehicle Dynamics
• +۵ مورد دیگر

Prof. Dr.-Ing. Ulrich Königorski is a Professor of Control Systems and Mechatronics at the Technical University of Darmstadt's Institute of Automatic Control and Mechatronics. He holds a doctorate from TU Karlsruhe and has extensive industry experience at Robert Bosch GmbH and Thyssen Henschel. His research focuses on modeling nonlinear systems, mechatronic systems control, vehicle dynamics, and microgrid control. Königorski leads the Control Systems and Mechatronics Laboratory and has contributed to projects like Proreta 3 (driver assistance systems) and FLORIDyn (wind turbine modeling). His work bridges theoretical control methods with practical applications in automotive, robotics, and renewable energy sectors. Education: Electrical Engineering (1977-1983, TU Karlsruhe), PhD (1987, TU Karlsruhe) on state feedback design. Professional roles include Dean of the Faculty of Electrical Engineering and spokesperson roles in academic committees. Research interests emphasize system modeling, robust control, and mechatronics applications. Notable areas include automated driving trajectory planning, sensorless motor control, and inverter-based microgrid synchronization. His recent work addresses challenges in unsupervised scenario extraction for autonomous systems and gait phase estimation for prosthetics. Labs/Teams: Head of the Control Systems and Mechatronics Laboratory. Collaborations include industry partnerships for automotive and renewable energy projects. Ongoing research focuses on enhancing safety in nonlinear systems, optimizing energy grids, and advancing human-centric automation technologies.

Hongyu Zhengمشاهده پروفایل

استادیار

• Mobility System Analysis and Management
• Transportation Electrification and Automation
• Smart Logistics Systems
• +۹ مورد دیگر

Hongyu Zheng is an Assistant Professor in the Department of Industrial and Systems Engineering and holds a courtesy appointment in Civil and Environmental Engineering at the University of Tennessee, Knoxville (UTK), within the Tickle College of Engineering. He leads the Electrified, Autonomous, and Shared Transportation (EAST) Lab, focusing on next-generation mobility systems. Ph.D. in Transportation System Analysis and Planning, Northwestern University, 2024 M.S. in Civil and Environmental Engineering, Northwestern University, 2024 M.S. in Transportation Engineering, Zhejiang University, 2019 B.Eng. in Civil Engineering, Zhejiang University, 2016 Dr. Zheng's research lies at the intersection of industrial engineering and mobility, emphasizing data-driven modeling, optimization, and economic analysis. His methodologies include applied economics, game theory, machine learning, bi-level optimization, and network modeling. Key application areas include autonomous transportation, shared mobility systems, transportation electrification, smart logistics, and equity in transit. His work aims to improve the efficiency, sustainability, and fairness of urban transportation networks. The recent publications reflect a strong trend toward integrating economic and behavioral modeling with advanced computational techniques to solve complex mobility challenges. His research spans theoretical modeling (e.g., game-theoretic competition in bikesharing) and real-world applications (e.g., impact of ridesplitting, pandemic effects on taxi markets). There is a clear focus on emerging technologies like autonomous vehicles, modular passenger-freight systems, and AI-based forecasting tools, often validated with real-world datasets from cities like Shenzhen and Hangzhou. Scientific contributions have been recognized through publications in top journals such as Transportation Science , Transportation Research Part A/B/C , and IEEE Transactions on Intelligent Transportation Systems . Notably, he received the Best Paper Award at the 9th International Workshop on Computational Transportation Science (CTS) in 2017. Dr. Zheng is actively involved in mentoring and research funding. He is currently recruiting fully funded Ph.D. students for Fall 2025 or Spring 2026. He also welcomes remote undergraduate and graduate visitors. While specific grants are not listed, his active lab and publication pipeline suggest ongoing research support. He teaches IE 430/530 (Supply Chain Engineering) and IE 691 (Advanced Topics: Game Theory and Its Applications in Mobility Systems). He leads the Electrified, Autonomous, and Shared Transportation (EAST) Lab at UTK, which focuses on innovation in sustainable and intelligent mobility. The lab integrates methodological advances in optimization, machine learning, and economics to design and analyze future transportation systems. Current research directions include modularity in co-modal transport, ethical transit pricing, and competition dynamics in shared mobility markets.

• Performance Measurement
• Operating Systems
• Virtualization
• +۳ مورد دیگر

Manuel Alejandro Pajuelo González is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture and the School of Computer Science. His research focuses on Performance measurement Operating systems Virtualization Thread assignment in multithreaded processors Recent publications show strong trends in RISC-V architectures, cybersecurity, and performance optimization. Key themes include Hardware virtualization Intrusion detection frameworks Statistical thread assignment approaches Spin-lock overhead analysis Scientific awards include BDigital Global Congress 15ª Edició Computación de Altas Prestaciones VI HiPEAC Paper Award He participated in multiple competitive R&D projects, including the DRAC project focused on RISC-V accelerators for next-generation computing.

• Mathematics
• Optimization
• Natural Sciences
• +۳ مورد دیگر

Prof. Fatma İnci Albayrak is a Professor at the Department of Mathematics Engineering within the Faculty of Chemical and Metallurgical Engineering at Yildiz Technical University . She holds a doctorate, postgraduate, and undergraduate degree in Mathematics Engineering from the same institution. Her research spans Mathematics , Optimization , Natural Sciences , and Fuzzy Modeling , with a focus on applying fuzzy logic to complex engineering and operational problems. Education: Doctorate (1993–1997), Yildiz Technical University Postgraduate (1990–1993), Yildiz Technical University Undergraduate (1986–1990), Yildiz Technical University Her publications reflect expertise in fuzzy systems , multi-objective optimization , differential equations , and nanostructured materials . She has contributed to journals like Physica Scripta , Canadian Journal of Chemical Engineering , and Cellulose , often addressing challenges in biomedical applications, transportation networks, and nanotechnology. She has served as a peer reviewer for Soft Computing and Promet journals. She is affiliated with the Multidisciplinary Nanoscience Technology working group at Yildiz Technical University since 2021, integrating nanomaterials with mathematical modeling. Her work bridges theoretical mathematics and practical engineering, demonstrated through collaborations in polymer nanocomposites, heat exchanger optimization, and traffic assignment problems.

• Network and Computing System Performance Analysis
• Computer Networks and Protocols
• Mobile and Wireless Communications
• +۵ مورد دیگر

Bouras Christos is a Professor in the Department of Computer Engineering at the University of Patras , focusing on advanced research and teaching in computer science and telecommunications. His work spans both theoretical foundations and practical applications. Network Performance Analysis 5G/6G Communication Systems Machine Learning for Network Optimization IoT and Telematics His recent research emphasizes 5G network optimization , with publications on beamforming techniques, resource allocation algorithms, and energy efficiency in MIMO and DUDe architectures. Key subtopics include: Deep learning for user allocation Game theory-based resource management IoT latency reduction Adaptive beamforming Network slicing optimization Techno-economic modeling He contributes to the academic community through leadership in the Division of Applications and Foundations of Computer Science, which includes laboratories focused on distributed systems, combinatorial algorithms, and machine learning applications.

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +۳ مورد دیگر

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .

• Production Management Systems
• Disruption Management
• Cellular Manufacturing Systems
• +۵ مورد دیگر

M. Selim Akturk is a Professor in the Department of Industrial Engineering at Bilkent University's Faculty of Engineering in Ankara, Turkey. His academic career spans over three decades since earning his Ph.D. from Lehigh University in 1990, during which he has established himself as a leading researcher in operations research and production systems. Education: Ph.D. in Industrial Engineering, Lehigh University (1990) M.S. in Industrial Engineering, Middle East Technical University (METU) (1984) B.S. in Industrial Engineering, Middle East Technical University (METU) (1982) Akturk's research focuses on Production Management Systems, Disruption Management, Cellular Manufacturing Systems, Production Scheduling, and Advanced Manufacturing Technologies. His work demonstrates particular expertise in scheduling problems with controllable processing times, airline operations optimization, and robotic cell scheduling. His research bridges theoretical advances with practical applications in manufacturing and transportation systems. His extensive publication record shows a consistent trajectory of high-impact research, with recent work (2016-2022) focusing on airline scheduling with cruise speed control, risk-averse stochastic programming, and robotic cell optimization. His publications appear in top-tier journals including Operations Research, Transportation Research Part B/C, and European Journal of Operational Research, demonstrating both theoretical rigor and practical relevance. Scientific Awards: Fellow, Class of 2023, The Institute for Operations Research and the Management Sciences (INFORMS) 2008 Winner of the Bilkent University Distinguished Teaching Award 1988 Winner of the Lehigh University Arthur E. Humphrey Outstanding Teaching Assistant Award Akturk has taught numerous courses at multiple institutions including Bilkent University (IE 376, IE 463, IE 561, IE 573), Lehigh University (MSE 433, IE 222), and McGill University (305-529, 277-601). His teaching spans production systems design, operations scheduling, manufacturing processes, and stochastic models, reflecting his broad expertise in industrial engineering principles and applications.

• Transportation Systems Modelling
• Machine Learning Applications
• Urban Mobility
• +۲ مورد دیگر

Guido Cantelmo is an Assistant Professor at the Technical University of Denmark (DTU) within the Department of Technology, Management and Economics, specifically in the Division of Transport's Section for Transport Systems Modelling. His research leverages big data analytics and machine learning to address complex transportation challenges, with expertise spanning traffic flow modeling, demand estimation, shared mobility systems, and urban network optimization. He maintains active collaboration with international cities including Copenhagen, Munich, and Tel Aviv-Yafo for empirical validation of his models. His research integrates computational techniques such as Graph Neural Networks, meta-learning, and physics-informed AI with transportation theory. Primary domains include: Dynamic traffic assignment using real-time data sources Machine learning for imbalanced mobility datasets Emission impact modeling of urban fleets Behavioral analysis of shared mobility adoption Large-scale simulation calibration frameworks Publication analysis (2022-2025) reveals dominant themes: data-driven demand estimation (37% of recent works), machine learning metamodeling (27%), shared mobility optimization (20%), and urban policy impact studies (16%). Methodological innovations include transfer learning for sparse data and multi-city validation approaches. No scientific awards or student mentoring relationships are documented in available sources. Similarly, no information exists regarding research grants, laboratory affiliations, or educational background.

Elkafi Hassiniمشاهده پروفایل

استاد

• Data-Driven Optimization
• Supply Chain Management
• Big Data Analytics
• +۸ مورد دیگر

Dr. Elkafi Hassini is a tenured Professor in the Operations Management department at McMaster University 's DeGroote School of Business. As Associate Dean, Research , he drives academic innovation while maintaining an active research agenda in supply chain analytics and sustainability. Key research themes include data-driven optimization , IoT integration , and supply chain risk management Active in smart freight systems , food delivery logistics , and urban delivery networks Lectures on applied optimization and procurement strategy His work has been published in top journals including Production and Operations Management , European Journal of Operational Research , and INFORMS Journal on Applied Analytics . Recent research explores: Time-slot pricing in on-demand delivery IoT-enabled supply chain visibility Freight bottleneck analytics Equitable ride-sharing design Post-pandemic logistics transformation Awarded by CFI , NSERC , and SSHRC , he leads the Smart Freight Centre partnership and advises on Peel Region e-commerce logistics. Collaborations include researchers from King Fahd University and Harbin Institute of Technology .