جست‌وجوی استاد و پژوهشگر

Vincent Liegeoisمشاهده پروفایل

استادیار

• Dynamical systems
• Control theory
• Operator theory
• +۳ مورد دیگر

Vincent Liegeois is an Assistant Professor in Mathematics at the University of Namur and member of the naXys research center since 2016. Holds MS in Aerospace Engineering and PhD in Applied Mathematics from University of Liège. Research focuses on dynamical systems theory, control theory, operator theory, and network analysis. Current projects include Koopman operator techniques for multiscale systems, frequency-domain analysis of nonlinear systems, and autonomous traffic management systems. Publications center on Koopman operator frameworks for nonlinear system identification, stability analysis, and control design. Recent work (2023-2024) develops data-driven methods for stability certification and sampling theorems for continuous-time systems. BELSPO return grant (2013) BAEF research fellowship (2011) FNRS fellowship (2007) Supervises research on multiscale dynamical systems and nonlinear control. Leads projects funded by national and international grants including AUTOMATic traffic management system. Leads research group developing operator-theoretic methods for complex systems. Collaborations include work with international partners on Koopman-based control frameworks.

Emre Bayraktarمشاهده پروفایل

استاد

• Construction Engineering and Management
• Infrastructure Maintenance
• Transportation Projects
• +۳ مورد دیگر

Dr. Emre Bayraktar is a Professor of Civil Engineering at The University of Southern Mississippi (USM), having previously held faculty positions at Florida International University (2006–2017) and Istanbul Technical University (2018–2022). He earned his M.Sc. and Ph.D. in Civil Engineering from Purdue University. His expertise spans construction engineering, infrastructure management, and transportation systems. He has secured over $2 million in research funding for projects related to construction efficiency, maintenance investment decisions, and public-private partnerships. Dr. Bayraktar’s research focuses on decision support frameworks for infrastructure maintenance, innovative revenue-generation strategies for highway systems, and dispute resolution in construction. He has authored over 25 articles in top journals and received the 2010 and 2012 Kauffman Professor Awards. He currently serves on the editorial board of the ASCE Journal of Management in Engineering . His career highlights include leading projects funded by the Florida DOT, Construction Industry Institute, and the European Commission. He has contributed to advancements in green building technologies, smart parking systems, and counterfeiting risk governance in supply chains. His work bridges technical and managerial aspects of construction, emphasizing practical applications and policy implications.

Fanyin Zhengمشاهده پروفایل

استادیار

• Empirical Operations Management
• Business Analytics
• Applied Econometrics
• +۴ مورد دیگر

Fanyin Zheng is an Assistant Professor at Imperial College Business School, specializing in Management Science and Business Analytics. She previously held an Assistant Professor position at Columbia Business School. Her research focuses on empirical operations management, business analytics, and applied econometrics, with an emphasis on decision-making in complex service systems like healthcare operations and platform markets. She earned her PhD from Harvard University, Cambridge, United States. Her research explores how individuals and firms leverage data-driven strategies in dynamic environments, particularly in healthcare systems and two-sided platforms. She serves as an Associate Editor for Management Science and Manufacturing & Service Operations Management . Her work addresses challenges such as resource allocation in hospitals, congestion management in transportation networks, and optimal design of marketplaces. Key themes in her publications include healthcare resource optimization, customer preference modeling in transportation systems, and structural estimation of intertemporal externalities in ICU admissions. She actively contributes to advancing methodologies in causal inference and network analysis for complex operational systems.

B. Brian Parkمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things
• Intelligent Transportation Systems
• +۸ مورد دیگر

B. Brian Park is a Professor in the Department of Civil and Environmental Engineering and the Department of Systems and Information Engineering at the University of Virginia . He directs the Traffic Operations Laboratory and has previously held research roles at the National Institute of Statistical Sciences and North Carolina State University. Education : B.S. and M.S. in Urban Engineering from Hanyang University, Ph.D. in Civil Engineering from Texas A&M University Dr. Park's research focuses on intelligent transportation systems , with special emphasis on: Cybersecurity Internet of Things Human Machine Interface Machine Learning Smart Cities Computational Statistics His recent publications address mixed traffic cooperative platooning, human-centric route prediction, and safety evaluation technologies for automated vehicles. These works span disciplines including control systems , transportation engineering , and cyber-physical systems . Scientific Awards : PTV America Best Paper Award ASCE Outstanding Reviewer Award Jack H. Dillard Outstanding Paper Award Council of University Transportation Centers C.V. Wootan Award ASCE ExCEEd Teaching Fellow Dr. Park teaches courses in traffic operations, simulation modeling, transportation systems, and safety engineering. His research projects include cooperative platooning algorithms, WiFi-based passenger tracking, and V2X communication systems for traffic signal optimization.

Alan Gousمشاهده پروفایل

استاد مدعو

• Computational Mathematics
• Network Engineering
• Traffic Optimization
• +۱ مورد دیگر

Alan Gous is an Adjunct Professor at Stanford University's Institute for Computational and Mathematical Engineering (ICME), where he applies mathematical modeling to network engineering challenges. His research spans network optimization, traffic engineering, and secure communication systems. With industry experience reflected in numerous patents, Dr. Gous has developed innovative solutions for network routing optimization, including latency-aware algorithms and segment routing techniques. His work balances theoretical foundations with practical implementations in telecommunications infrastructure. Earlier research contributions include statistical methodologies for model selection and dataset comparison. His current focus addresses security frameworks for network authorization systems and gateway communication protocols, demonstrating a consistent emphasis on efficient and secure network architectures.

Jyh-haw Yehمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning
• Data Privacy
• +۵ مورد دیگر

Jyh-haw Yeh is a **Professor of Computer Science** at Boise State University, serving as the **Program Co-director of the MS Cybersecurity Program**. He holds a Ph.D. in Computer and Information Science and Engineering from the University of Florida. His research focuses on **cybersecurity**, **machine learning**, **data privacy**, and **cryptography**, with contributions to malware detection, adversarial machine learning, and secure communication protocols. He leads initiatives to enhance cybersecurity education, such as the **GenCyber Teacher Camps**, and collaborates on projects like intelligent IoT systems and cloud-based security solutions. Dr. Yeh advises students in the Cybersecurity Emphasis program and actively contributes to community outreach, including workshops for educators and high-school students. His work bridges academic research and practical applications, addressing challenges in network security, data integrity, and AI-driven threat detection. Education: Ph.D., University of Florida, Computer and Information Science and Engineering Affiliations: College of Engineering, Computer Science Department Key Roles: MS Cybersecurity Program Co-director, Cybersecurity Curriculum Developer His research spans technical advancements in encryption, malware analysis, and AI ethics, with a focus on real-world implementations in industries like nuclear power plant decommissioning and smart cities.

Frank Schrödelمشاهده پروفایل

استاد

• Automation Technology
• Robotics
• Control Engineering
• +۳ مورد دیگر

Prof. Dr.-Ing. Frank Schrödel is a Professor of Drive, Automation, and Robotics Technology at Schmalkalden University of Applied Sciences' Faculty of Mechanical Engineering. His research focuses on control engineering, autonomous systems, Industry 4.0, and robotics applications such as social robots and autonomous driving. He leads training laboratories in Automation Technology, Drive Technology/Rapid Control Prototyping, and Robotics, as well as research labs in Industry 4.0 production systems and autonomous driving (in development). Prof. Schrödel holds a PhD in Control Engineering from RWTH Aachen University (2016) and a Diplom in System Technology from Otto-von-Guericke University Magdeburg (2010). His professional career includes roles as Team Leader at IAV GmbH (2016–2020) and Group Leader at RWTH Aachen's Institute of Automatic Control (2010–2016). His work bridges academic research and industry applications, emphasizing frugal innovations and model-based development. Research interests span robust control, model predictive control (MPC), and agile engineering processes. He actively develops educational infrastructure, including low-cost benchmark plants and open lab approaches for Industry 5.0 training. His publications address autonomous systems, collaborative robotics, and smart manufacturing, with a focus on practical implementation and interdisciplinary solutions. Current lab initiatives include nursing robotics (planned) and Industry 4.0 production systems. His work emphasizes human-centered technologies and digital transformation in mechanical engineering, reflecting a commitment to both academic rigor and real-world applicability.

• Data Analytics
• Complex Network Analysis
• Computer Science Education
• +۳ مورد دیگر

Dr. Matin Pirouz Nia is an Associate Professor in the Department of Computer Science at Fresno State, part of the College of Science and Mathematics. As the director of the Algorithms and Big Data lab, her research focuses on data analytics, complex network analysis, and computer science education. Her work has been supported by grants from the National Science Foundation, Google, and Amazon, resulting in over 50 publications in top-tier journals and conferences. She has received a congressional commendation for STEM mentorship and contributes to multidisciplinary projects spanning healthcare security, social network analysis, and transportation systems. Education: Ph.D. in Computer Science from the University of Nevada, Las Vegas. Research interests include data-driven decision making, network topology analysis, and machine learning applications in healthcare and education . Her lab develops algorithms for big data challenges in areas like social network modeling and real-time traffic analysis. Recent research trends show a focus on applying network analysis to medical anomaly detection, optimizing community detection algorithms, and leveraging machine learning for education technology. Her work bridges theoretical computer science with practical applications in smart cities and cybersecurity. Scientific Awards: Congressional commendation for STEM mentorship Grants & Advising: NSF-funded projects on network analysis, Google grants for data-centric research, and Amazon-sponsored work on cloud computing applications Labs/Teams: Leads the Algorithms and Big Data lab, collaborating with industry partners like Google and Amazon on real-world data challenges.

Yu Yangمشاهده پروفایل

استادیار

• Spatio-temporal Data Mining
• Cyber-Physical Systems
• Ubiquitous Computing
• +۴ مورد دیگر

Yu Yang is an Assistant Professor in the Department of Computer Science & Engineering at Lehigh University. His research focuses on spatio-temporal data mining, cyber-physical systems, ubiquitous computing, and machine learning applications in logistics and transportation systems. He holds a Ph.D. in Computer Science from Rutgers University, an M.S. in Computer Science from Rutgers, and a B.Eng. in Software Engineering from Northeastern University (China). Education: Ph.D., Computer Science, Rutgers University M.S., Computer Science, Rutgers University B.Eng., Software Engineering, Northeastern University (CN) His research explores cutting-edge methodologies for optimizing transportation networks, enhancing urban mobility, and improving last-mile delivery systems through AI-driven solutions such as reinforcement learning, hypergraph networks, and adaptive prediction frameworks. Key projects include tackling logistics challenges via LLM integration, improving traffic signal resilience during malfunctions, and designing cooperative delivery systems with real-time encounter detection. His work often bridges theory and practice, with deployments in real-world logistics platforms and urban infrastructure systems.

Rick S. Blumمشاهده پروفایل

استاد

• Machine Learning
• Statistical Signal Processing
• Cybersecurity
• +۹ مورد دیگر

Prof. Rick S. Blum is the Robert W. Wieseman Endowed Professor of Electrical & Computer Engineering at Lehigh University. He leads the Signal Processing and Communication Research Lab (SPCRL) and previously directed the Integrated Networks for Electricity Cluster and the Energy Systems Engineering Institute. His research focuses on machine learning, statistical decision making, and cybersecurity for IoT, cyber-physical systems, and energy networks. Blum holds a Ph.D., M.S., and B.S. in Electrical Engineering from the University of Pennsylvania and Pennsylvania State University. He is an IEEE Fellow, served as an IEEE AESS Distinguished Lecturer, and received the IEEE Third Millennium Medal and ONR Young Investigator award. His work spans theoretical foundations of machine learning, resilient control systems, and distributed signal processing, with applications to smart grids, radar, and sensor networks. Blum teaches courses on statistical decision making and communication theory. His lab develops algorithms for dynamic state estimation, adversarial attack mitigation, and sparsity-promoting control strategies. Education: Ph.D., Electrical Engineering, University of Pennsylvania M.S., Electrical Engineering, University of Pennsylvania B.S., Electrical Engineering, Pennsylvania State University Awards: IEEE Fellow IEEE AESS Distinguished Lecturer IEEE Third Millennium Medal ONR Young Investigator His recent publications address cybersecurity in sensor systems, adversarial resilience in dynamic networks, and data-driven modeling for renewable energy systems. Blum’s research emphasizes practical implementations of theoretical advancements, such as sparse regression filters for photovoltaic systems and eigenspectrum optimization for network vulnerability reduction.

Logan Beaverمشاهده پروفایل

استادیار

• Optimal Control
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Logan Beaver is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Old Dominion University (ODU), affiliated with the Batten College of Engineering and Technology. His research focuses on optimal control, robotics, and autonomous systems, with notable contributions to multi-agent coordination, energy-efficient trajectory planning, and swarm intelligence. He leads the Intelligent Systems Laboratory and has published extensively in top-tier journals like Automatica and IEEE Robotics and Automation Letters . Beaver's work bridges theoretical control frameworks and practical applications, such as autonomous vehicle systems and robotic task allocation. His recent projects include developing physics-informed neural networks for motion primitives and analyzing long-duration autonomy in connected vehicles. He collaborates on initiatives like scaled city simulations for traffic control and energy-optimal frameworks for autonomous agent coordination. His research emphasizes constraint-driven optimization, model predictive control, and game-theoretic approaches to solve complex problems in robotics and transportation. Beaver's publications span topics from microrobotics to highway platooning, reflecting a balance between foundational control theory and real-world engineering challenges.

David Porterمشاهده پروفایل

استاد

• Transportation Systems
• Manufacturing Systems
• Supply Chain Management
• +۳ مورد دیگر

Dr. David Porter is a Professor of Industrial and Manufacturing Engineering at Oregon State University's College of Engineering. His research focuses on information systems engineering with applications in transportation systems, manufacturing, supply chains, and transit networks. He joined Oregon State in 2001 and holds a PhD in Industrial Engineering from the University of Pittsburgh (2000). Dr. Porter's education includes a BS in Mechanical Engineering from Universidad Autónoma de Nuevo León (1991), followed by advanced degrees in Industrial Engineering and Manufacturing Systems from institutions in Mexico and the US. His work emphasizes modeling and performance evaluation of complex systems, with notable contributions to RFID-based transportation solutions, fleet management, and transit equity analysis. His research interests span transportation technologies (e.g., Bluetooth and RFID systems), supply chain optimization, and sustainable fleet modeling. Recent projects include strategic route planning for disease management and development of standards for transit ridership data (GTFS-Ride). Awarded the 2009 OSU Impact Award for Outstanding Scholarship, his work has addressed critical issues like Oregon’s Online Crash Reporting System and vehicle-miles-traveled taxation. He leads the Mobile Technology Solutions Lab, advancing sensor-based traffic monitoring and data collection systems. His grants and collaborations focus on integrating emerging technologies into transportation infrastructure, with outputs ranging from algorithmic optimization to policy-relevant solutions for public transit systems.

Alessio Iovineمشاهده پروفایل

پژوهشگر

• Control Systems
• Power Systems
• Cyber-Physical Systems
• +۴ مورد دیگر

Alessio Iovine is a CNRS researcher at the Laboratory of Signals and Systems (L2S) affiliated with CentraleSupélec (Paris-Saclay University). His research focuses on control systems for cyber-physical applications, including energy systems (e.g., microgrids, renewable integration) and transportation (e.g., autonomous vehicles, traffic control). He holds a European Doctorate in Information Science and Engineering from the University of L’Aquila and CentraleSupélec, with postdoctoral experience at institutions like UC Berkeley and Efficacity. Research Areas: Cyber-Physical Systems DC/AC Microgrid Control Autonomous Vehicles & Adaptive Cruise Control Predictive Grid Management Multi-Agent Systems Publications Trends: Recent works emphasize mesoscopic control approaches for vehicular systems, distributed control for multi-agent networks, and predictive algorithms for power grid congestion management. Over 20 peer-reviewed articles since 2017 reflect his expertise in nonlinear control, hybrid systems, and renewable integration. Opportunities: Actively recruiting PhD/Master students for projects in cyber-physical power systems, machine learning for grid identification, and autonomous traffic control. Hosts seminars on energy systems and signal processing at Paris-Saclay. Labs/Teams: Member of L2S’s Modelling and Estimation team, collaborating with groups like ILOCOS (Information, Learning, Optimization & Communication Sciences).

Zhenhui Yuanمشاهده پروفایل

استادیار

• Machine Learning
• Networking
• IoT
• +۸ مورد دیگر

Zhenhui Yuan is an Assistant Professor at the University of Warwick, School of Engineering, leading the Connected Robotics Lab. He holds a BEng in Software Engineering from Wuhan University (2008) and a PhD in Electronic Engineering from Dublin City University (2012). His research focuses on machine learning in networking, IoT, and robotics, with expertise in UAV communications, edge AI, and vehicular networks. Prior roles include Senior Lecturer at Northumbria University (2020–2023), Associate Professor at Hangzhou Dianzi University (2015–2019), and co-founder/CTO of RobSense Technology (2015–2019). He has received Best Paper Awards at IEEE ICCRE 2016 and IEEE BMSB 2014. His research interests span machine learning applications in networking, robotics, and IoT, with emphasis on UAV swarm systems, edge computing, and quality-of-service optimization. He serves as a Guest Editor for IEEE journals and chairs workshops on 6G-empowered robotic vehicles. He leads projects funded by the University of Warwick (£9k), British Academy (£9k), and Horizon Europe (£92k), among others. His work includes developing the Connected Robotics Lab’s testbeds for 5G emulation and drone swarm control. Key achievements include pioneering AirSlice (UAV network slicing), founding the SwarmLink radio, and advancing video quality metrics for 3D streaming. He actively supervises PhD candidates and collaborates on grants addressing holographic telesurgery, multimodal transport networks, and EV optimization. His labs focus on hardware-in-the-loop emulation and edge AI for robotic systems.

Tianhai Tianمشاهده پروفایل

دانشیار

• Computational Biology
• Computational Statistics
• Multi-scale Modelling
• +۴ مورد دیگر

Tianhai Tian is an Associate Professor at the School of Mathematics, Monash University. He holds a PhD in Computational Mathematics from the University of Queensland (2001), and has held prestigious positions including Australian Research Fellow at ARC, Lord Kelvin Fellow at University of Glasgow, and ARC Future Fellow. His research focuses on mathematical modeling of biological systems, with expertise spanning computational biology, statistical inference, and multi-scale modeling of complex networks. Research Interests: Mathematical modeling of genetic regulatory networks and cell signalling pathways Computational approaches to cancer therapy optimization Stochastic modeling of biochemical systems Network inference from high-dimensional biological data Multi-scale modeling approaches for complex biological systems His publication trends demonstrate strong focus on cancer modeling, computational immunology, and development of novel algorithms for biological data analysis. Recent works increasingly integrate machine learning with dynamical systems modeling. Awards and Honors: Australian Research Fellowship, ARC (2006) Lord Kelvin Fellowship (2007) ARC Future Fellowship (2011) Research Grants: Stochastic modelling of telomere length regulation in ageing research (ARC, 2012-2017) Stochastic modelling of genetic regulatory networks with burst process (ARC, 2011-2016) Multiscale stochastic modelling of tumour robustness (ARC, 2010-2016) Spatio-temporal modelling of Ras dependent MAP kinase activation (ARC, 2007-2011)