جست‌وجوی استاد و پژوهشگر

Pedro Pita Barrosمشاهده پروفایل

استاد

• Health Economics
• Microeconomics
• Economic Regulation
• +۲ مورد دیگر

Pedro Pita Barros is a Professor of Health Economics at the Nova School of Business and Economics , Universidade Nova de Lisboa, with a focus on health economics, regulation, and competition policy. He has held significant roles including Vice-Rector of Universidade Nova de Lisboa (2013-2017) and served on multiple national and international panels such as the European Commission’s Expert Panel on Effective Ways of Investing in Health and the Mission Board for Cancer (2019-2020). As Past-President of the Portuguese and European Associations of Health Economics, he has shaped academic discourse and policy. Education: 1997 - Agregação, Universidade Nova de Lisboa 1993 - PhD in Economics, Universidade Nova de Lisboa 1992 - MSt in Economics, Universidade Nova de Lisboa 1988 - BSc in Economics, Universidade Nova de Lisboa His research spans health economics, economic regulation, competition policy, and pandemic response, with over 50 journal articles and 10 book chapters. Recent studies analyze vaccine hesitancy, health system resilience, and socioeconomic disparities during crises. He has contributed to editorial boards and consulted for public and private entities in health economics and regulatory frameworks. Notable Committees & Roles: Portuguese National Council of Ethics for the Life Sciences (2015-2021) Portuguese National Health Council (2017-2021) Board of the Portuguese Energy Regulator (2005/2006) Governmental Commission for National Health Service Financial Sustainability (2006/2007) European Commission Mission Board for Cancer (2019-2020) Scientific Networks: Collaborated with researchers across Europe (e.g., Germany, Netherlands, Spain) and Brazil, focusing on cross-country pandemic responses, health equity, and policy effectiveness. His work integrates economics, public health, and data science, particularly through machine learning applications in healthcare.

• Multi-access Edge Computing
• High-Performance Networks
• Cognitive IoT Systems
• +۷ مورد دیگر

Prof. Ahmed Al-Dubai is a Professor at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University, where he leads the IoT and Networked Systems Research Group and serves as Cybersecurity and Cyber Physical Systems Research Lead. His interdisciplinary research spans Multi-access Edge Computing, High-Performance Networks, Cognitive IoT Systems, VANETs, AI, E-Health, Smart Cities, and Security . He earned his PhD in Computing Science from the University of Glasgow in 2004. His recent publications focus on edge computing architectures, digital twin security, Arabic NLP, wireless energy harvesting, and vehicular communication . His work has been recognized with IEEE Outstanding Service Award, Best Paper Awards at IEEE IUCC 2015 and ACM MoMM 2013 , and fellowships like Senior IEEE Member and British Higher Education Academy Fellow . He supervises 20+ PhD students and has served on 60+ IEEE/ACM conference committees. Current projects include AI-driven fish identification (Innovate UK £265K) , secure IoT protocols (Royal Society £12K) , and COG-MHEAR (EPSRC £3.26M) . He has held visiting professorships at Universite de Valenciennes, University of Sydney, and University of Shenyang.

Sofie Haesaertمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Control Theory
• Formal Verification
• +۳ مورد دیگر

Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.

• Soil Biogeochemistry
• Carbon Cycling
• Mineral-Soil Interactions
• +۳ مورد دیگر

Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.

• Control Theory
• Optimization
• Machine Learning
• +۹ مورد دیگر

Andrea Iannelli is a Tenure-Track Assistant Professor at the Institute for Systems Theory and Automatic Control (IST) , University of Stuttgart, Germany. He also serves as a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and participates in the Cluster of Excellence Data-Integrated Simulation Science (SimTech) . His research focuses on reconciling model-based and data-driven approaches for robust and adaptive control of uncertain dynamical systems. Ph.D. : Control and Dynamical Systems, University of Bristol (UK), 2019 Postdoctoral Researcher : ETH Zürich (Switzerland), 2019–2022 Harnessing the intersection of control theory, optimization, and machine learning , Iannelli’s work addresses data-driven modeling, uncertainty quantification, and robust control with applications in energy systems, intelligent transportation, and industry 4.0 . His recent publications highlight trends in LPV frameworks, online convex optimization, and hybrid control systems , emphasizing safety and efficiency. He contributes to the academic community as an Associate Editor for the International Journal of Robust and Nonlinear Control and as a member of international conference IPCs. His group, Trustworthy Autonomy for Smart Adaptive Systems (TASAS) , mentors PhD students in projects spanning adaptive control, uncertainty quantification, and reinforcement learning .

Tayfun Günelمشاهده پروفایل

استاد

• Microwave Circuits
• Radar Systems
• Antennas
• +۱۲ مورد دیگر

Tayfun Günel is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU) , Faculty of Electrical and Electronics Engineering. He holds a PhD (1993), MSc (1988), and BSc (1986), all from ITU. His research spans microwave circuits, radar systems, antennas, and optimization using genetic algorithms and soft computing. His research interests include Microwave Circuits , Radar and Antennas , Optimization , and Genetic Algorithms . His work focuses on impedance matching, microstrip antennas, noise modeling, and metamaterial-based microwave components. He has taught courses such as Electromagnetic Fields, Radar Systems, and Satellite Communication Systems. The recent publications reflect a strong trend in microwave circuit design , antenna miniaturization , and the application of evolutionary algorithms (genetic algorithms, PSO) and machine learning (neural networks, SVR) in electromagnetic design and optimization. There is a consistent focus on practical microwave components like transmission lines, patches, and amplifiers, often using nanomaterials (e.g., carbon nanotubes) and metamaterials . His work bridges theoretical modeling with computational optimization for real-world RF and radar applications. Email: gunelmur@itu.edu.tr Professor Günel has supervised 2 completed PhD theses, 2 ongoing PhD theses, 23 completed master's theses, and 1 ongoing master's thesis, demonstrating a significant contribution to student mentoring. There are no specific grants or funding sources mentioned in the provided text. He is affiliated with research in microwave systems and antenna design , likely operating within the broader research ecosystem of the Electronics and Communication Engineering Department at ITU, which includes labs such as the Microwave Systems and Antennas Laboratory and the Radar and Microwave Technologies Research Laboratory.

Tim J. Nyeمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Operations Research
• Process Modelling
• +۴ مورد دیگر

Tim J. Nye is an Associate Professor in the Department of Mechanical Engineering at McMaster University's Faculty of Engineering. He holds a Ph.D. in Mechanical Engineering (1997) from the University of Waterloo, following an M.Sc. (1989) at Ohio State and B.A.Sc. (1987) at Waterloo. His research focuses on applying operations research techniques to manufacturing systems, with specific expertise in optimization algorithms for sheet metal processes, hydroforming reliability, and adaptive control in forging. Education: Ph.D. Mechanical Engineering, University of Waterloo (1997) M.Sc. Mechanical Engineering, Ohio State (1989) B.A.Sc. Mechanical Engineering, University of Waterloo (1987) Research interests span multiple dimensions of advanced manufacturing: developing decision models for production investment, creating novel lot-sizing algorithms incorporating work-in-process costs, exact solutions for 2D nesting problems, and agent-based systems for reliability prediction using warranty data. His work bridges theoretical operations research with practical metal forming applications. Recent publications demonstrate consistent contributions to manufacturing optimization, with particular focus on stamping processes, sheet metal design, and hydroforming reliability. These align with McMaster's research clusters in Advanced Materials & Manufacturing and Infrastructure. Scientific awards include the 2002 CSME Best Student Paper competition win for machine vision research with S. Dworkin. He maintains active collaborations with industry partners, as evidenced by his research on industry-university R&D ventures. Current projects explore intelligent open die forging as a solid freeform fabrication method, demonstrating his commitment to both traditional manufacturing improvement and emerging rapid prototyping technologies.

Ahmed F Abdelghanyمشاهده پروفایل

استاد

• Commercial Airlines
• Airports
• Big Data Cloud Computing
• +۷ مورد دیگر

Dr. Ahmed F. Abdelghany is the Associate Dean for Research and Professor of Operations Management at the David O'Maley College of Business, Embry-Riddle Aeronautical University, since January 2006. He specializes in commercial airlines, airports, big data cloud computing, business analytics, and operations research models. Prior to his academic career, Dr. Abdelghany worked in enterprise optimization at United Airlines, Chicago. Education: Ph.D. in Civil Engineering (Transportation Systems) from the University of Texas at Austin (2001) Dr. Abdelghany’s research focuses on airline network planning, flight scheduling, simulation of complex transportation systems, and NextGen air traffic management. He has authored two influential books: Modeling Applications in the Airline Industry (Routledge 2010) and Airline Network Planning and Scheduling (Wiley 2018). His publications analyze airline operations, competitive dynamics, and crowd management in transportation facilities. He teaches courses like Airline Management (BA 315) and Airline Operations & Mgmnt (BA 609), and participates in industry short courses. Dr. Abdelghany contributes to research projects such as NextGen air traffic implementation, integrated airport initiatives, and benefit-cost analysis of arrival management systems. His work bridges academic theory with real-world airline and transportation challenges.

Mohsen Heidariمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Theoretical Machine Learning
• +۳ مورد دیگر

Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints

Anne-Cécile Orgerieمشاهده پروفایل

پژوهشگر

• Energy Efficiency
• Environmental Impacts
• Distributed Systems
• +۳ مورد دیگر

Anne-Cécile Orgerie is a permanent CNRS Research Scientist (Directrice de recherche) at the Magellan Team within IRISA in Rennes, France. She holds a PhD from École Normale Supérieure de Lyon (2011) and previously worked as a postdoctoral researcher at the University of Melbourne. Current roles: Director of GDRS EcoInfo (CNRS service group on ICT environmental impact), member of multiple editorial boards (e.g., IEEE TPDS, IJDSN), and TPC member for conferences like SC, IPDPS, and CCGrid. Research Focus: Her work centers on energy efficiency and environmental impacts of distributed systems, including cloud infrastructures, telecommunications networks, and smart grids. She explores renewable energy integration, resource optimization, and co-simulation frameworks for smart grid management. Key Projects: Leads initiatives like CARECloud (PEPR Cloud project reducing cloud environmental impacts, 2023–2030) and DECORUS (CNRS 80Prime project on renewable-powered edge computing). Co-leads the RennesGrid ADEME project (2017–2021) for smart grid demonstrators. Supervision: Advises/co-advises over 20 PhD students and postdocs, focusing on topics like energy-efficient fog infrastructures, co-optimization of electrical/communication networks, and edge computing models.

Kristin Y. Pettersenمشاهده پروفایل

استاد

• Nonlinear control theory
• Marine robotics
• Snake robotics
• +۵ مورد دیگر

Kristin Y. Pettersen is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering. She holds a PhD and MSc in Engineering Cybernetics from NTNU and serves as an Adjunct Professor at the Norwegian Defence Research Establishment (FFI). She co-founded and led Eelume AS as its first CEO. PhD in Engineering Cybernetics, NTNU MSc in Engineering Cybernetics, NTNU Her research focuses on nonlinear control theory, motion control of mechanical systems, and marine robotics. Key areas include autonomous vehicles, underactuated systems, and cooperative control. Her recent work involves snake robotics, vehicle-manipulator systems, and safety-critical control algorithms. Her publications demonstrate trends in marine robotics , nonlinear control systems , autonomous navigation , formation control , and adaptive algorithms . Emerging topics include energy-shaping control , extremum-seeking optimization , and task-priority frameworks for complex robotic systems. 2025: Norwegian Academy of Science and Letters (DNVA) 2020: ERC Advanced Grant 2017: IEEE Fellow 2016-2021: Board member, Eelume AS 2013-2023: Key scientist, NTNU AMOS She has supervised 30 PhD graduates and currently mentors 16 PhD candidates. Her grants include ERC PoC UR4energy (€150k), ERC AdG CRÈME (€2.5M), and CAROS (NOK 45M) for subsea autonomy. She leads teams at NTNU's Applied Underwater Robotics Laboratory and contributes to the Cluster of Excellence IntCDC.

• High-Performance Computing
• Big Data
• Interconnection Networks
• +۵ مورد دیگر

Jesus Escudero-Sahuquillo is a Full Professor at the Computing Systems Department (DSI) of the Faculty of Computer Science Engineering at the University of Castilla-La Mancha (UCLM), Spain. His academic journey began at UCLM where he completed his Degree in Computer Science in 2006, followed by a Master of Science in 2008, and a PhD in Advanced Computing Technologies in 2011. His professional experience includes: Full Professor at UCLM (current position) PostDoc at Technical University of Valencia (2015) PhD Senior Engineer at Oracle Norway (2013-2015) 5-year PostDoc position at UCLM funded by UCLM and European Commission (2016) Dr. Escudero-Sahuquillo's research focuses on high-performance computing and Big Data, with particular emphasis on interconnection networks and related optimization strategies. His work spans congestion management, routing algorithms, network topologies, and power saving techniques. He has extensive experience with InfiniBand technology and has contributed significantly to congestion control mechanisms in high-performance interconnects. His publication record demonstrates a strong focus on advancing network performance in high-performance computing environments. Over the past decade, his research has evolved from foundational work on routing algorithms and network topologies to more sophisticated congestion management techniques applicable to modern data centers and exascale computing architectures. His most recent work addresses cutting-edge challenges in packet identification, hybrid congestion control, and adaptive routing for next-generation interconnection networks. Dr. Escudero-Sahuquillo has served as a program committee member and reviewer for numerous prestigious conferences and journals including IEEE Micro, IEEE Transactions on Parallel and Distributed Systems, and Journal of Parallel and Distributed Computing. He was the co-organizer for five editions of the IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB). His research has been supported through participation in multiple projects funded by the European Commission and the Spanish Government. He has collaborated extensively with researchers across Europe and has been instrumental in advancing the state of the art in high-performance interconnection networks.

• Power Electronics
• Building Automation
• Smart Grids
• +۷ مورد دیگر

Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.

Hao Yangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Transportation Safety
• Transportation Equity
• +۴ مورد دیگر

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

Wei Gaoمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• On-Device AI
• Mobile and Embedded Computing
• +۷ مورد دیگر

Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.