جست‌وجوی استاد و پژوهشگر

Carmine Ornaghiمشاهده پروفایل

استاد

• Pharmaceutical Industry
• Water Sector
• Mergers and Acquisitions
• +۳ مورد دیگر

Professor Carmine Ornaghi is a Professor of Economics and Head of Department at the University of Southampton. He holds a PhD from Universidad Carlos III de Madrid (2004). His research focuses on empirical industrial organization, particularly the pharmaceutical industry, water sector, mergers and acquisitions, and firm productivity. He has advised the European Commission’s DG Comp on merger impacts and led a £385,000 ESRC grant (2019–2022) studying mergers’ effects on pharmaceutical innovation. Current projects include analyzing wage markdown power in oligopolistic markets and water consumption policies. Teaching includes undergraduate and postgraduate modules like Statistics for Economics and Industrial Economics. He supervises two PhD students and contributes to the Applied Economics research group. Notable grants include ESRC and British Academy funding. His work spans top journals such as the Journal of Economic Behavior & Organization and Journal of the European Economic Association.

Christopher Gilliamمشاهده پروفایل

استادیار

• Signal Processing
• Radar Systems
• Sampling Theory
• +۵ مورد دیگر

Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.

Plamen Angelovمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۵ مورد دیگر

Prof. Plamen Angelov holds a Chair in Intelligent Systems and is Director of Research at Lancaster University's School of Computing and Communications. He leads the Lancaster Intelligent, Robotic, and Autonomous Systems (LIRA) Centre, uniting 70+ faculty across 15 departments. With a PhD (1993) and DSc (2015), he is a Fellow of IEEE, IET, ELLIS, and AAIA. His research focuses on explainable AI, evolving systems, and computational intelligence, with 400+ publications (h-index 65) in top venues like TPAMI and IEEE Transactions. Notable achievements include the Dennis Gabor Award (2020) and ranking in Stanford's Top 0.2% AI researchers (2024). He leads projects funded by UK Research Councils, ESA, DSTL, and industry. As Editor-in-Chief of Springer's Evolving Systems , he drives standards in explainable AI through IEEE's P2976 Working Group. His work spans autonomous systems, cybersecurity, and biomedical applications through initiatives like AI4EO and H-UNIQUE.

Ferrante Neriمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization
• +۵ مورد دیگر

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

James Roscowمشاهده پروفایل

مدرس ارشد

• Ferroelectric Materials
• Piezoelectric Energy Harvesting
• Energy Storage
• +۳ مورد دیگر

James Roscow is a Senior Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Integrated Materials, Processes & Structures (IMPS), IAAPS, and the Institute of Sustainability and Climate Change. His research focuses on developing ferroelectric composites for energy harvesting, sensing, and energy storage, with expertise in material fabrication, property tuning, and numerical modeling. He holds a PhD in Mechanical Engineering from the University of Bath and a BSc in Materials Science from the University of Manchester. Research interests include porous ferroelectric ceramics, piezoelectric and pyroelectric materials, and their applications in renewable energy and sensors. He has led or contributed to 12 projects funded by organizations like EPSRC and Innovate UK, exploring topics such as low-cost transducers, nanofluid cooling for solar panels, and phase transformations in ceramics. Key publications (2021–2025) address piezoelectric energy harvesting, porous material design, and advanced manufacturing techniques. His work aligns with UN SDGs, particularly sustainable energy and innovation. Roscow supervises PhD students in functional ceramics, energy storage, and sensor technologies. Notable collaborations include projects on hydraulic energy harvesters, SONAR transducers, and self-healing materials. He has contributed datasets on piezoelectric composites and energy storage systems, emphasizing reproducibility and applied research.

Ahmad Hemmatiمشاهده پروفایل

دانشیار

• Maritime Logistics
• Inventory Routing Problems
• Combinatorial Optimization
• +۳ مورد دیگر

Dr. Ahmad Hemmati is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on optimization models and algorithms for logistics, transportation, and maritime systems. He holds a PhD from the Norwegian University of Science and Technology (NTNU) in 2015. His work addresses challenges in inventory routing, drone delivery systems, and combinatorial optimization using methods like reinforcement learning and metaheuristics. Key contributions include models for maritime cargo optimization, synchronized truck-drone delivery, and feeder network design. He has collaborated internationally with institutions like MIT and the University of Antwerp. His findings are published in journals such as European Journal of Operational Research and Computers & Operations Research , with a focus on practical solutions for industrial logistics problems. Education: PhD in Operations Research (NTNU, 2015). Research Interests: Maritime Logistics, Inventory Routing Problems, Combinatorial Optimization, Drone Delivery Systems, and Algorithm Design. His work bridges theoretical models with real-world applications in transportation networks and sustainability. Publications Trends: Recent articles emphasize deep reinforcement learning applications and maritime environmental monitoring. Earlier work includes foundational contributions to tramp shipping and VMI service routing. His methodologies often blend heuristic algorithms with mathematical programming. Grants/Awards: No specific awards mentioned, but his work has been funded by the Research Council of Norway and industry collaborations.

Kyle O'Keefeمشاهده پروفایل

استاد

• Global Navigation Satellite Systems (GNSS)
• Indoor Positioning and Navigation
• Ultra-wideband Ranging
• +۶ مورد دیگر

Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.

Paul Leahyمشاهده پروفایل

مدرس

• Wind Energy
• Renewable Energy
• Circular Economy
• +۴ مورد دیگر

Dr. Paul Leahy is a Lecturer in Wind Energy Engineering at University College Cork (UCC) and a funded investigator in the SFI MaREI Centre for Energy, Climate, and Marine Research. He leads research on wind energy, circular economy applications for decommissioned turbine blades, and climate change impacts. His work includes the Re-Wind project, a transdisciplinary initiative funded by SFI, NSF, and Northern Ireland's Department for the Economy. He co-supervises six PhD students and has examined over 20 theses globally. Education: BE Electrical Engineering (1995), PhD Electrical Engineering (2001), Postgraduate Certificate in Teaching & Learning in Higher Education (2018). Research & Awards: Research focuses on wind energy forecasting, energy storage, and greenhouse gas emissions. Notable awards include the Environmental Research Letters' 2013 Highlight and an ISI Highly Cited Paper (2012). He contributed to IPCC Special Reports on climate change mitigation and co-edits Renewable & Sustainable Energy Reviews. Grants & Projects: Secured €320,000 for Re-Wind and led multiple EU, EPA, and industry-funded projects. Current research includes lifecycle assessments of repurposed turbine blades and hydrogen production via offshore wind. Teaching & Outreach: Coordinates modules like NE6003 Wind Energy Engineering and contributes to energy systems education. Engages in public lectures and policy initiatives like Energy Cork Cluster leadership. Labs & Teams: Collaborates with multidisciplinary teams on infrastructure repurposing (e.g., BladeBridges) and climate adaptation strategies for agriculture and peatlands.

Emanuele Crisostomiمشاهده پروفایل

دانشیار

• Smart Grids
• Electric Vehicles
• Control Systems
• +۲ مورد دیگر

Emanuele Crisostomi is Associate Professor of Electrical Systems at the University of Pisa's Department of Energy, Systems, Territory and Constructions Engineering. He holds PhD and graduate degrees in Automation Engineering from the University of Pisa, with research visits to University of Cambridge, University of Leicester, and Hamilton Institute. His research bridges control theory, energy systems, and smart mobility. His research integrates: Smart grid control and renewable energy integration Electric vehicle charging infrastructure and routing algorithms Urban traffic modeling and optimization Nonlinear system dynamics and Lyapunov stabilization Machine learning applications in energy systems Crisostomi's publications demonstrate consistent innovation in electric vehicle-grid integration, with recent focus on predictive charging control, highway EV circulation patterns, and hydropower monitoring. His work shows increasing application of deep reinforcement learning for multi-regional power systems and hybrid network models for infrastructure monitoring. He leads research on IoT-enabled energy efficiency in buildings and contributes to epidemic modeling through network science. Current projects examine battery sizing optimization, charging station scheduling under uncertainty, and synergetic building management systems integrating environmental monitoring with energy conservation.

Linda Katehiمشاهده پروفایل

استاد

• Microwave and Millimeter-Wave Circuits
• MEMS Devices
• Terahertz Applications
• +۵ مورد دیگر

Linda Katehi is a Professor of Electrical & Computer Engineering and Materials Science & Engineering at Texas A&M University, holding the O'Donnell Foundation Chair II. She is a Member of the National Academy of Engineering and American Academy of Arts and Sciences. Her research focuses on advanced electromagnetic systems, MEMS devices, and embedded intelligent sensors. Katehi earned her Ph.D. in Electrical Engineering from UCLA (1984), with prior degrees from UCLA and the National Technical University of Athens. Education: Ph.D., Electrical Engineering, UCLA (1984) M.S., Electrical Engineering, UCLA (1981) B.S., Electrical and Mechanical Engineering, National Technical University of Athens (1977) Research Interests: Katehi pioneers innovations in microwave circuits, MEMS-based reconfigurable systems, terahertz technology, and neuromorphic sensors. Her work emphasizes integrating artificial intelligence into hardware for adaptive sensing platforms. Recent projects include flexible electronics, time-domain system analysis, and sustainability in urban infrastructure. Awards & Recognition: Ramo Simon Founder’s Award (2015) Charter Fellow, National Academy of Inventors (2013) Leading Women in STEM Award (2012) Rudy E. Henning Mentoring Award (IEEE, 2011) Lab & Teams: Directs the Intelligent Electromagnetic Sensors Lab (IEMSL), advancing embodied intelligence in electronics. Her team develops AI-embedded sensors and reconfigurable systems for applications in healthcare, communications, and environmental monitoring. Collaborates across disciplines to address global sustainability and equity challenges.

• Signal Processing
• Machine Learning
• Acoustics
• +۷ مورد دیگر

Luis Antonio Azpicueta Ruiz is an Associate Professor in the Department of Signal Theory and Communications at Carlos III University of Madrid. He leads research in the Signal Processing and Learning Group (GTSA) and Machine Learning for Data Science (ML4DS) group, focusing on interdisciplinary applications spanning acoustics, telecommunications, and machine learning. Research Interests: His work bridges signal processing theory with practical applications in environmental acoustics, adaptive filtering systems, and machine learning. Key research themes include: Advanced adaptive filtering architectures for nonlinear systems Distributed estimation in sensor networks Acoustic echo cancellation and room equalization Psychoacoustic evaluation methods Machine learning applications in noise monitoring and sound analysis Research Projects: Principal investigator for multiple funded projects including: Diagnóstico del ruido de chorro en aeronaves (AEI, 2022-2025) LearnINg FLow and Noise Dynamics via AI (COMUNIDAD DE MADRID, 2024-2026) BODYinTRANSIT - Sensory-driven Body Transformation (EUROPEAN COMMISSION, 2022-2026) Aprendizaje Automático para análisis Big Data (MINISTERIO DE ECONOMÍA, 2018-2021)

Naeem Ayoubمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Robotics
• +۱۲ مورد دیگر

Naeem Ayoub is an Assistant Professor in the Department of Technology and Innovation at the University of Southern Denmark (SDU), affiliated with SDU Technology Entrepreneurship and Innovation. His research bridges computer science and engineering, focusing on intelligent systems and automation. Research Interests: His work spans machine learning, computer vision, robotics, and cyber-physical systems. He explores applications in autonomous drones, digital twins, power line inspection, and environmental monitoring. His research emphasizes real-time decision-making, energy efficiency, and anomaly detection in complex systems. The recent publications highlight a strong trend in deploying AI-driven robotics for industrial and environmental applications, particularly in infrastructure inspection and predictive maintenance. His work integrates neural networks, sensor networks, and autonomous navigation to solve practical engineering challenges. Scientific Contributions: Active contributor to 19 research outputs including journals and conference proceedings. Creator of an open-source dataset for pylon component and fault detection using machine learning. Involved in interdisciplinary collaborations across engineering, environmental science, and computer science. Advising and Grants: While no formal students are listed, he has participated in academic supervision as a censor in internal examinations. His projects suggest involvement in research grants related to autonomous systems and industrial digitalization, though specific funding details are not provided. Labs and Teams: He collaborates within research teams focused on robotics and intelligent systems at SDU, particularly in drone technology and cyber-physical systems. His work involves close collaboration with researchers in environmental monitoring, power systems, and industrial automation.

• Antenna Design
• Evolutionary Algorithms
• Wireless Communications
• +۷ مورد دیگر

Sotirios K. Goudos is a Professor at the Department of Physics, Aristotle University of Thessaloniki (AUTH), Greece, and Director of the ELEDIA@AUTH lab within the ELEDIA Research Center Network. His research focuses on antenna design, evolutionary algorithms, wireless communications, machine learning, and IoT applications. He holds a B.Sc. in Physics (1991), M.Sc. in Electronics (1994), Ph.D. in Physics (2001), and additional qualifications in Information Systems and Electrical Engineering. Prof. Goudos is a Senior Member of IEEE and serves as Editor-in-Chief of the Telecom open access journal (MDPI) and Associate Editor for IEEE Transactions on Antennas and Propagation, IEEE Access, and IEEE Open Journal of the Communication Society. He has organized multiple special issues in journals like EURASIP Journal on Wireless Communications and Networking and has authored/edited books on antennas and AI in networks. His awards include multiple IEEE Access Outstanding Associate Editor recognitions (2019–2023) and inclusion in Stanford University's top 2% scientists list (2020–2024). He teaches courses on telecommunications, Java programming, and microwave systems, and has supervised over two dozen master's students since 2009. His work spans antenna optimization, AI-driven communications, and IoT security, with contributions to 5G/6G, RIS systems, and smart agriculture. Prof. Goudos actively contributes to IEEE Greece Section leadership roles, including Secretary (2022) and Vice-Chair (2023–2024). His labs and teams focus on ELEDIA's research in electromagnetics, optimization, and AI applications.

Hamid Nawabمشاهده پروفایل

استاد

• Computational signal processing
• Applied artificial intelligence
• Analysis of EMG signals
• +۳ مورد دیگر

Hamid Nawab is a Professor in the Department of Electrical and Computer Engineering at Boston University, with an affiliated appointment in the Department of Biomedical Engineering. He holds a PhD from MIT (1982) and has been recognized for exceptional teaching, including the College of Engineering inaugural Teaching Excellence in the Core Curriculum Award (2025) and multiple ECE Department Excellence in Teaching Awards. His research focuses on computational signal processing, applied artificial intelligence, and biomedical signal analysis, particularly in EMG and patient activity monitoring. He has taught courses such as Signals and Systems, Digital Signal Processing, and graduate teaching seminars. His work integrates signal processing with biomedical applications, including Parkinson’s disease monitoring via wearable sensors and EMG signal decomposition. He has authored influential texts like Signals and Systems and contributed to over 50 peer-reviewed publications. Awards include Fellow of the American Institute for Medical and Biological Engineering (2006) and the Metcalf Award for Excellence in Teaching (1993). His advising and grants emphasize interdisciplinary engineering education and biomedical signal processing. Collaborations span clinical and academic institutions, focusing on translational research in healthcare technology.

Sandra M. Đosićمشاهده پروفایل

دانشیار

• Electronics
• Real-Time Systems
• Fault-Tolerant Systems
• +۷ مورد دیگر

Sandra M. Đosić is an Associate Professor at the Faculty of Electronics in Niš, University of Niš, Serbia, specializing in Electronics and Embedded Systems. She has been actively contributing to research in real-time systems, fault tolerance, wireless sensor networks, and indoor localization technologies. Research Interests: Her work spans fault-tolerant real-time systems, energy-efficient computing, UWB-based indoor localization, and communication protocols for wireless sensor networks. She explores techniques such as dynamic voltage and frequency scaling (DVFS), tone-based contention resolution, and deflection routing in networks-on-chip to enhance system reliability and efficiency. Publication Trends: Her recent publications (2009–2022) demonstrate a strong focus on improving robustness and performance in embedded and distributed systems. The research integrates signal processing, network optimization, and energy-aware design, primarily applied in industrial and indoor environments. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: While no specific students are listed, she is currently participating in two national research projects, indicating active involvement in funded research. Her collaborations with researchers such as Igor Stojanovic and Milica Jovanovic suggest a strong team-based research approach. Labs and Research Teams: Although no formal lab or team name is specified, her repeated co-authorship with colleagues from the Faculty of Electronics implies active participation in a research group focused on electronics, communications, and real-time systems.