جست‌وجوی استاد و پژوهشگر

• Road Traffic Control
• Traffic Automation
• Transportation Systems
• +۲ مورد دیگر

Dr. Varga István is a Full Professor at the Budapest University of Technology and Economics (BME) in the Faculty of Transportation Engineering and Vehicle Engineering, and a Scientific Advisor at the HUN-REN Institute for Computer Science and Control. He holds a D.Sc. from the Hungarian Academy of Sciences (2020), a Ph.D. (2007), and an M.Sc. in Transportation Engineering (1998). His primary research focuses on road traffic control and traffic automation systems , with expertise in intelligent transportation solutions and vehicle mechatronics. His research integrates control theory with practical applications in urban mobility, including dynamic traffic light systems, autonomous vehicle impacts, and emission modeling. Recent work explores V2X communication, platooning technologies, and microscopic traffic simulation for urban optimization. He has led major projects such as the national innovation project 'Dynamic, adaptive traffic control services based on digital data sources' (2020-2024) and industrial collaborations with Knorr-Bremse, Siemens, and nuclear energy sectors. Awards include the Hungarian Academy of Sciences Young Prize (2007) and multiple recognitions for technological innovation. Educational activities include courses on Road Traffic Control, Mathematical Methods, and Vehicle System Modeling. Laboratory leadership includes the Road Traffic Control Lab with industry partnerships like Bosch and SWARCO.

João Pedro Machado Vitorinoمشاهده پروفایل

مدرس پاره‌وقت

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۳ مورد دیگر

João Pedro Machado Vitorino is a part-time lecturer and researcher at the Polytechnic Institute of Porto's School of Engineering, affiliated with GECAD. He is pursuing a Ph.D. in Informatics Engineering (Artificial Intelligence) at the University of Porto and holds an MSc in Artificial Intelligence Engineering. Education : Ph.D. (in progress), MSc, and BSc in Informatics/Artificial Intelligence Engineering Research : Focuses on machine learning robustness, explainability, and adversarial attack simulation for cybersecurity applications Projects : Involved in 7 EU/National R&D initiatives (2021-2025), including BEHAVIOR (2024-2025) and CYDERCO (2023-2025) His recent publications (2023-2025) span cybersecurity datasets , adversarial learning , malware detection , LLM security , and energy-efficient AI . He co-organized the 36th European Simulation & Modelling Conference (2022) and peer-reviewed for Pattern Recognition and Computers & Security . Awards : IEEE Outstanding MSc Thesis (2023), Engineers Association Innovation Award (2024), multiple merit certificates Skills : Machine Learning, AI Security, Network Analysis, and 3 professional certifications (Cisco, Microsoft, Airbus)

• Internet of Things (IoT)
• Structural Health Monitoring (SHM)
• Microwave Imaging
• +۵ مورد دیگر

Igor Bisio is a Full Professor at the Department of Naval, Electrical, Electronics, and Telecommunications Engineering (DITEN) at Università di Genova. His research focuses on IoT-driven structural health monitoring, microwave imaging for biomedical applications, and wireless surveillance systems. Teaches courses on telecommunications, IoT, and machine learning Pioneers low-cost IoT solutions for SHM and post-stroke rehabilitation Develops microwave tomography techniques for pediatric stroke diagnostics Advances privacy-preserving Wi-Fi-based crowd monitoring Recent research trends include edge AI integration, compressive sensing for vibration analysis, and multi-class object tracking in aerial scenes. He also explores UAV-based monitoring systems, WiFi fingerprinting for localization, and Banach space inversion models for electromagnetic imaging. His work spans interdisciplinary domains combining electrical engineering, biomedical applications, and wireless network security.

Roy E. Welschمشاهده پروفایل

استاد

• Statistics
• Data Science
• Statistical Learning
• +۱۰ مورد دیگر

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

Xiaoyan Hongمشاهده پروفایل

دانشیار

• Distributed Systems
• Internet of Things (IoT)
• Wireless Communications
• +۴ مورد دیگر

Dr. Xiaoyan Hong is an Associate Professor in the Department of Computer Science at The University of Alabama's College of Engineering. Her research focuses on mobile/wireless networks, vehicular networks, and delay-tolerant systems. Ph.D., Computer Science, University of California-Los Angeles (2003) M.S., Computer Science, Zhejiang University (2000) Research spans Internet of Things (IoT) , Connected Vehicles , and Underwater Wireless Networks . Key projects include NSF-funded underwater robot communication infrastructure and smart traffic light systems. Recent work explores Named Data Networking (NDN) in vehicular environments, Task Synchronization for autonomous vehicles, and V2I Communication for traffic optimization. NSF Research Experience for Undergraduates (REU) grant recipient $1.5M NSF grant for underwater robotics networking Her research integrates with multiple engineering centers, including the Center for Advanced Vehicle Technologies and Center for Transportation Operations .

Xiaojun Caoمشاهده پروفایل

استاد

• Optical Networks
• Wireless Networks
• Network Security
• +۳ مورد دیگر

Dr. Xiaojun Cao is a faculty member at the Andrew Young School of Policy Studies, Georgia State University, specializing in optical and wireless network architectures, protocols, and security. His research addresses challenges in bandwidth scalability, network survivability, and future Internet design through innovations in multi-granular switching, traffic grooming, and cross-layer interactions. B.S., Engineering Physics, Tsinghua University, 1996 M.S., Electrical Engineering, Chinese Academy of Sciences, 1999 Ph.D., Computer Science and Engineering, State University of New York at Buffalo, 2004 His work spans three core areas: Optical Network Optimization : Developing waveband switching frameworks for backbone networks, focusing on multi-layer architectures and performance evaluation. Network Survivability : Designing proactive and reactive strategies for protection and restoration in optical and wireless systems. Edge Network Security : Advancing secure service function chain deployment in multi-vendor and zero-trust environments. Recent research trends highlight his leadership in Network Functions Virtualization (NFV) , Edge Computing , and AI-Driven Networking , with publications on blockchain-based federated learning, prompt chain deployment, and asymmetric protection mechanisms. His work bridges theoretical rigor with practical applications in 5G/6G, UAV networks, and smart infrastructure. Dr. Cao leads a research group exploring optical-wireless convergence and has contributed to foundational studies in network virtualization and traffic management.

• Digital Forensics
• Cybersecurity
• Incident Response
• +۵ مورد دیگر

Professor Vasilis Katos is a Professor in Cyber Security at Bournemouth University, specializing in digital forensics, incident response, and intellectual property security. With over 160 publications and extensive industry experience as an Information Security Consultant, he serves as an expert witness in criminal courts in both the UK and Greece. His research has significant impact in digital forensics for intellectual property infringement investigations through collaborations with the EU Intellectual Property Office (EUIPO) and UNICRI. Professor Katos holds a Diploma in Electrical Engineering from Democritus University of Thrace, an MBA from Keele University, and a PhD in Computer Science (network security and cryptography) from Aston University. He is a certified Computer Hacking Forensic Investigator (CHFI) with extensive practical experience in cybersecurity. His research primarily focuses on digital forensics and incident response, with recent emphasis on intellectual property infringement investigations, IoT security, blockchain applications, and traffic prediction systems. His work bridges academic research with practical security applications, particularly in intellectual property protection and smart city security frameworks. Professor Katos has coordinated significant research projects including Illegal IPTV in the European Union and IP Infringement on online trading platforms, funded by the EUIPO Observatory. His extensive publication record shows strong trends in digital forensics, cybersecurity for intellectual property protection, IoT security, and increasingly in smart city security frameworks. The research demonstrates a progression from foundational cybersecurity work to specialized applications in intellectual property protection and circular economy security models, with recent incorporation of AI and machine learning techniques for threat intelligence and traffic prediction. Certified Computer Hacking Forensic Investigator (CHFI) Editorial Board Member of Computers & Security Journal Professor Katos has successfully supervised multiple PhD students including Amalia Damianou (Digital Forensics in Smart, Circular Cities), Christos Iliou (Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots), and Mohammed Al Qurashi (Intrusion Detection for IoT). He has secured significant research funding including the ECHO project (European network of Cybersecurity centres), IDEAL-CITIES, and multiple EUIPO-funded initiatives focusing on intellectual property protection. His work connects with the Centre for Intellectual Property Policy & Management (CIPPM) at Bournemouth University, where he contributes to research on intellectual property in emerging technologies. Professor Katos maintains active collaborations with international organizations including UNICRI and the EU Intellectual Property Office, focusing on practical applications of digital forensics in intellectual property protection.

Shen-Shyang Hoمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Shen-Shyang Ho is a Full Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His work spans machine learning, data mining, and edge computing with applications in urban mobility, precision agriculture, and data privacy. He leads NSF-funded research projects on dynamic graph analysis and spatiotemporal anomaly detection. Ph.D. in Computer Science, George Mason University Post-Doctoral Associate, Caltech & NASA JPL B.S. in Mathematics with Computational Science, National University of Singapore Research expertise includes graph-based machine learning, conformal prediction, cooperative inference, and privacy-preserving ML. Current work focuses on federated learning for edge devices and anomaly detection in evolving systems. He has developed tools like SplitTracer for cooperative inference evaluation and ParkGauge for urban mobility monitoring. Recent publications highlight his contributions to 2026 Pattern Recognition journal (martingale-based graph analysis), 2025 IEEE ICAIC conference (blockchain gas optimization), and 2024 ACM SAC symposium (shared mobility systems). His work integrates machine learning with real-world constraints across energy grids, transportation, and agricultural technology. NSF Grant (2022) for Dynamic Graph Anomaly Detection NSF Grant (2018) for Spatiotemporal Analysis Google Scholar Classic Paper Recognition (2017) for 2006 Radar Micro-Doppler Study Professional memberships include the Association for Computing Machinery (ACM). His teaching portfolio ranges from introductory programming to advanced ML courses. He previously held positions at Nanyang Technological University before joining Rowan in 2016.

Michael Dinitzمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +۵ مورد دیگر

Michael Dinitz is an Associate Professor in the Department of Computer Science at Johns Hopkins University with a secondary appointment in the Department of Applied Mathematics and Statistics . He is a member of the Algorithms and Complexity group and the Mathematical Institute for Data Science . His research focuses on Theoretical Computer Science , with emphasis on approximation algorithms , online algorithms , and distributed algorithms , particularly in applications to computer networking , distributed systems , and machine learning . His recent work involves differential privacy , dynamic networks , and resilient network design . His publications cover diverse topics in graph theory , privacy-preserving algorithms , and online optimization , with a focus on practical applications of theoretical results. He has received multiple NSF grants including a CRII award and Algorithms in the Field funding, and was honored with the Professor Joel Dean Excellence in Teaching Award . He advises a research group with PhD, MSE, and undergraduate students , and serves on program committees for leading theory conferences like FOCS , STOC , and ICALP . Education PhD in Computer Science, Carnegie Mellon University (2010), advised by Anupam Gupta AB in Computer Science, Princeton University (2005), advised by Sanjeev Arora Grants & Awards NSF CRII Grant (2015) NSF Algorithms in the Field Grant (2016) Multiple NSF Algorithmic Foundations grants (2019, 2022, 2025) NSF Graduate Research Fellowship (2005-2010) ARCS Foundation Scholarship (2005-2010) Best Paper Awards at ICDCS (2014) and DISC (2017)

• Control Engineering
• Automotive Engineering
• Model Predictive Control
• +۵ مورد دیگر

Dr.-Ing. Frank-Josef Heßeler is a Senior Research Engineer (Geschäftsführender Oberingenieur) and Deputy Institute Director at the Institute of Control Engineering (IRT), RWTH Aachen University . His work focuses on control systems for automotive and urban mobility applications, including model predictive control, vehicle localization, and intelligent infrastructure. He actively contributes to research in autonomous driving, hybrid drivetrains, and thermal systems optimization. Control Engineering Automotive Systems Model Predictive Control Thermal Diagnostics Urban Traffic Simulation His publications highlight advancements in connected vehicle localization, scenario-specific motion modeling, and hybrid drivetrain control. He collaborates extensively with Dirk Abel and other researchers. No scientific awards or student advisement details are explicitly mentioned in the provided texts. He is affiliated with the Institute of Control Engineering, engaging in projects like CERMcity (autonomous urban driving testbed) and Galileo-based navigation systems. His work integrates simulation platforms, Neuro-Fuzzy models, and hardware-in-the-loop testing for automotive control solutions.

• Edge-Cloud Computing
• Resource Optimization
• Applied Machine Learning
• +۵ مورد دیگر

Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He holds a PhD in Computer Science from the University of Bradford (2010), and M.Eng/B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology. His research specializes in edge-cloud computing, federated learning, and AI-driven optimization for networks and IoT systems. Research Interests: Hu's work spans resource optimization, applied machine learning (particularly in distributed settings), network security, blockchain integration, and intelligent systems for electric vehicles and Industry 4.0. His recent projects focus on federated edge AI, 6G-enabled industrial IoT, and real-time federated learning via hardware-algorithm co-design. Publications: His 150+ papers emphasize federated learning, edge computing, and reinforcement learning applications. Recent works (2020–2025) show a strong trend toward optimizing AI at the network edge, with themes like digital twins, blockchain security, and EV-integrated systems dominating. Awards & Recognition: Best Paper Awards: IEEE SOSE'16, IUCC'14 Outstanding Service & Leadership Awards for IEEE conferences Top 4% contributor to EPSRC Peer Review Fellow of the Higher Education Academy (HEA) Grants & Projects: Secured €4.7M+ funding from EU Horizon, EPSRC, and Royal Society for projects including: SAILING (Secure AI for Smart Internet-of-Energy, €3.6M) REFINE (Real-time Air Quality Monitoring with UAVs, €897K) SustainAIRA6G (Energy-Efficient AI for 6G Networks, £118K) Advising: Supervised 12 PhD students to completion; currently mentoring 7 students in federated learning, edge computing, and AIoT.

Yuanchang Xieمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic safety
• Traffic operations and simulation
• +۵ مورد دیگر

Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.

Jose Paolo Talusanمشاهده پروفایل

پژوهشگر

• Smart Transportation Systems
• Cyber-Physical Systems
• Middleware
• +۴ مورد دیگر

Dr. Jose Paolo Talusan is a Research Scientist at the Department of Computer Science and Computer Engineering , Vanderbilt University, specializing in smart transportation systems , distributed computing , and cyber-physical systems . He is affiliated with ScopeLab , a research group focused on smart cyber-physical systems. Education: PhD from Nara Institute of Science and Technology, Japan (2020) Research Interests: His work addresses challenges in urban mobility through middleware architectures, optimization algorithms, and machine learning. Key areas include incident detection in transportation systems, privacy-preserving route planning, and vehicle-to-building charging optimization. Publication Trends: Recent publications focus on real-time transit optimization (2024-2025), leveraging reinforcement learning for heterogeneous agents in vehicle-to-building systems, and privacy-aware route planning in smart cities. His work integrates IoT , edge computing , and graph neural networks to tackle imbalanced data and sparsity issues in transit analytics. Labs & Teams: Actively contributes to ScopeLab at Vanderbilt University, collaborating on interdisciplinary projects with researchers in computer science, electrical engineering, and urban planning.

Jingen Zhouمشاهده پروفایل

استادیار

• Supply Chain Risk Management
• Cruise Industry
• Collision Avoidance Technology
• +۵ مورد دیگر

Jingen Zhou serves as an Assistant Professor in the Department of Operations, Supply Chain and Information Management at KEDGE Business School, bringing international expertise from academic and professional experiences across the United States, Japan, China, and Australia. His doctoral research at the Australian Maritime College (University of Tasmania) established his specialization in maritime supply chain systems. Dr. Zhou's scholarly foundation includes a PhD in Management Science and Commerce with explicit focus on Maritime Supply Chain, reflecting his deep engagement with global maritime systems. His research portfolio demonstrates consistent thematic alignment with operational challenges in maritime contexts. His primary research thrust centers on maritime supply chain risk management, where he integrates collision avoidance technologies, ship AIS data analytics, and climate change risk assessment. This work frequently examines cruise industry dynamics through empirical studies in Shanghai and China, addressing pandemic impacts and supply chain resilience. His methodological approach combines Bayesian networks, deep learning, and complex network analysis to solve real-world navigation and logistics problems in coastal and riverine environments. Recent publications reveal a clear trajectory toward data-driven maritime solutions, with 14 articles published between 2023-2025 spanning marine traffic prediction, cruise network analysis, and multimodal hub optimization. His work consistently employs empirical methodologies focused on Asian maritime contexts, particularly China's evolving port and cruise ecosystems, while leveraging cutting-edge computational techniques for risk assessment. Dr. Zhou actively contributes to academic discourse as a reviewer for multiple journals and maintains collaborative research ties with industry partners and international scholars. His pre-KEDGE research involvement in Australian and Chinese projects demonstrates sustained engagement with maritime logistics challenges across different regulatory and operational environments.

• Geoinformatics
• Cartography
• Geodetic Science
• +۵ مورد دیگر

Prof. Monika Sester is a distinguished Professor and Executive Director of the Institute of Cartography and Geoinformatics at Leibniz University Hannover, within the Faculty of Civil Engineering and Geodetic Science. She also serves as Spokesperson for the Leibniz Research Center FZ:GEO and holds multiple leadership roles including Faculty Information Officer (FIO) for the Faculty of Civil Engineering and Geodetic Science, Ombudsman for Good Scientific Practice, and Exchange Coordinator for Geodetic Science and Geoinformatics. Her research focuses on the intersection of geospatial information science, cartography, and urban mobility. Prof. Sester's work spans several key areas: Geospatial data processing and analysis Cartographic representation and visualization Urban mobility and transportation systems Spatial data uncertainty and quality Digital mapping technologies and applications Historical map analysis and interpretation Prof. Sester's recent publications demonstrate a strong focus on applying advanced computational techniques to geospatial problems. Her work shows increasing emphasis on machine learning applications for map analysis, urban mobility optimization, and 3D spatial modeling. She has been particularly active in researching applications of deep learning for historical map interpretation, urban mobility patterns, and spatial uncertainty visualization. Her contributions to the field have been recognized through leadership positions in major research initiatives: Executive Director, Institute of Cartography and Geoinformatics Spokesperson, Leibniz Research Center FZ:GEO Faculty Information Officer, Faculty of Civil Engineering and Geodetic Science Ombudsman for Good Scientific Practice Member of multiple academic committees including the Admissions and Examination Board Prof. Sester actively collaborates with students and researchers across multiple projects focused on geospatial information systems, urban mobility, and cartographic visualization. Her leadership extends to guiding research directions within the Leibniz Research Center FZ:GEO, which brings together interdisciplinary expertise to address complex spatial challenges.