جست‌وجوی استاد و پژوهشگر

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.

• 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.

• 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 .

• 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 .

Pieter Vansteenwegenمشاهده پروفایل

استاد

• Public Transportation Optimization
• Metaheuristics
• Demand-Responsive Transport Systems
• +۴ مورد دیگر

Prof. Pieter Vansteenwegen is a full professor at KU Leuven's Faculty of Engineering Science, leading the Centre for Industrial Management/Traffic and Infrastructure (CIB) and chairing the KU Leuven Institute for Mobility (LIM). With a PhD in Operations Research (2008), he works on robust public transport systems, metaheuristics for logistics, and demand-responsive mobility solutions. Chair, KU Leuven Institute for Mobility Program Director, Master of Mobility and Supply Chain Engineering Board member, International Association of Railway Operations Research His research integrates transportation engineering , logistics optimization , and smart mobility systems , focusing on: Demand-responsive public transportation Metaheuristic algorithm development Inventory routing problems Smart waste collection strategies Multi-robot scheduling Recent publications demonstrate expertise in variable neighborhood search , Benders decomposition , and genetic algorithms applied to public transport, satellite scheduling, and circular economy logistics. His work has been cited over 9,400 times (Google Scholar H-index 51). Scientific recognition includes: Multiple IAROR best paper awards Best Reviewer European Journal of Operational Research BIVEC-GIBET PhD awards (2009, 2021, 2023) RASIG-INFORMS Student Paper Prizes He directs KU Leuven's Planning and Operational Research subdivision , serves on faculty councils, and founded spin-off dyNAVic for electronic tourist guides. His teaching portfolio includes courses in Public Transportation Design , Distribution Logistics , and Operational Management .

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.

Mark Wallaceمشاهده پروفایل

استاد

• Optimization
• Constraint Programming
• Artificial Intelligence
• +۵ مورد دیگر

Professor Mark Wallace is a renowned academic at Monash University , specializing in modelling and software platforms for planning, scheduling, and optimization problems . He has led groundbreaking projects such as the development of the ECLiPSe constraint programming platform (acquired by Cisco Systems) and the G12 hybrid optimization platform , which was commercialized through his company, Opturion . His research consistently bridges theoretical innovation with real-world applications, supported by industry funding from major companies like British Airways, Qantas, and Woodside Energy. Education: MA in Mathematics and Philosophy from Oxford University; MSc in Artificial Intelligence from Queen Mary College, London; PhD in Communicating with Databases in Natural Language from Southampton University. His work focuses on application-driven research in constraint programming, hybrid optimization systems, and decision support technologies. Recent publications highlight advancements in public transport routing , autonomous vehicle fairness constraints , and human-centred feasibility restoration . He actively collaborates with industry partners on projects involving Melbourne Water , Woodside , and the Alertness CRC , driving innovations in smart infrastructure and transport systems.

Shivam Guptaمشاهده پروفایل

دانشیار

• Procurement
• Supplier Management
• Innovation Management
• +۱ مورد دیگر

Shivam Gupta is an Associate Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln's College of Business. He joined the university in 2018 as an Assistant Professor and was promoted to his current rank in 2024. His office is located in HLH 511K, Lincoln, NE. Education: Ph.D. in Management Science, The University of Texas at Dallas (2011-2016) M.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2009-2011) B.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2003-2007) His research expertise spans procurement systems, supplier relationship management, agricultural operations optimization, and incentive mechanism design. He develops analytical models to address challenges in supply chains, focusing on both theoretical frameworks and practical applications in agricultural markets. His work integrates game theory, optimization techniques, and economic analysis to solve complex operational problems. Gupta's publications demonstrate consistent focus on mechanism design and operational challenges, with recent work emphasizing dynamic incentive structures, procurement optimization, and agricultural policy impacts. His articles appear in premier journals including Management Science, Operations Research, and Production and Operations Management. Honors: First Place, INFORMS Public Sector OR Best Paper Competition (2015) Finalist, POMS SCM Student Paper Competition (2016) He teaches undergraduate and graduate courses in business analytics, including SCMA 350: Business Analytics/Information Analysis and SCMA 855: Prescriptive Analytics.

• Telecommunications
• Wireless Communication
• Terahertz Technology
• +۷ مورد دیگر

Kazuhiko Tamesue is an Associate Professor at the Faculty of Science and Engineering , Waseda University , with a focus on Terahertz Communication , Artificial Intelligence , and IoT Network Security . His academic journey spans academia and industry, including long-term roles at Panasonic Corporation (1991–2009) and current leadership in advanced wireless systems since 2022. Research Areas : Telecommunications, Wireless Hardware, Data Science, Machine Learning, Atmospheric Sensing Key Contributions : Pioneering 300GHz OFDM transceivers, AI-driven GNSS spoofing detection, and dual-frequency THz radar for cloud physics Projects : NICT-funded terahertz networks (2023–2024), ultra-low-latency systems (2022–2024), and security-enhancing radar technologies His 15 most recent publications (2024–2008) demonstrate expertise in (1) terahertz propagation for NTN/HAPS platforms, (2) machine learning for security (LSTM, GANs), and (3) next-generation IoT protocols (LPWAN, distributed ledger). He holds 20+ patents in antenna design, direct-conversion receivers, and power line communication. As an IEEE and IEICE member, he contributes to standards in 5G/6G and EMC.

• Distributed Systems and Algorithms
• Decentralized search algorithms
• P2P systems and applications
• +۱۴ مورد دیگر

Angelo Furno is an Associate Professor at ENTPE (National School of Public Works) within Université Gustave Eiffel in Lyon, France. He serves as a researcher at the LICIT laboratory and collaborates with INRIA AGORA and the CITI-Lab of INSA-Lyon. His academic journey began at the University of Sannio in Italy where he earned his degree in Computer Engineering in 2010, followed by a PhD in Information Engineering completed in 2014 with a thesis titled "Scalable Service Composition in Autonomic Computing." After his PhD, he conducted post-doctoral research at both the University of Sannio and INRIA in Lyon. Furno's research spans multiple domains with a strong focus on transportation systems and network analysis. His work integrates distributed systems, mobile networking, and data science to address urban mobility challenges. He specializes in analyzing multi-source traffic data to support urban planning and transportation infrastructure management. His research interests include mobility pattern recognition, travel demand estimation, road-network modeling, land use detection, and social network analysis. He has developed expertise in graph analysis, clustering algorithms, and recommendation techniques applied to transportation contexts. His technical skills encompass Python, Scala, Java, and various web technologies. His publication record demonstrates consistent contributions to transportation research, with recent work focusing on park-and-ride systems optimization, traffic demand prediction using deep learning, privacy-preserving analysis of origin-destination data, and geometric classification of urban road networks. His research often addresses practical urban challenges, including pandemic-related transportation adaptations and accessibility analysis of transit networks in cities like Lyon. Best Paper Award (2012) Furno teaches courses on Intelligent Transportation Systems, Data Science and Machine Learning, and Big Data at ENTPE. His reviewing activities span numerous prestigious journals including IEEE Transactions on Mobile Computing, Transportation Research Part C, and Sensors. His collaborative work extends to multiple institutions across Europe, with particular emphasis on French research networks.

• real-time decision-making
• dynamic optimization under uncertainty
• online markets
• +۴ مورد دیگر

Vahideh Hosseinikhah Manshadi serves as the Michael H. Jordan Professor of Operations at Yale University's School of Management, with a secondary appointment as Assistant Professor in the Department of Statistics. Her academic profile reflects interdisciplinary expertise spanning operations research, statistics, and public health modeling. Professor Manshadi's research focuses on real-time decision-making and dynamic optimization under uncertainty , with significant applications across multiple domains. Her work addresses critical challenges in online markets , health-care operations , maritime port logistics , and matching markets . She has developed sophisticated optimization frameworks that account for network structures, user behavior in crowdsourcing platforms, and fair resource allocation in constrained environments. Analysis of her recent publications reveals a strong trend toward addressing complex matching problems in dynamic environments. Her research bridges theoretical optimization with practical applications in humanitarian contexts (refugee resettlement), platform economics, and social services. The interdisciplinary nature of her work is evident in publications spanning top journals in operations research, management science, and computer science. Professor Manshadi maintains an active research agenda as evidenced by consistent high-impact publications in premier journals including Management Science , Operations Research , and Manufacturing & Service Operations Management . Her work demonstrates significant methodological innovation while addressing real-world operational challenges across multiple sectors. Her academic leadership extends to the Public Health Modeling initiative at Yale, where her optimization expertise contributes to addressing complex public health challenges through quantitative approaches. While specific information about research grants and advising is not detailed in the provided text, her publication record indicates active collaboration with numerous researchers across different institutions.