جست‌وجوی استاد و پژوهشگر

Piya Palمشاهده پروفایل

استاد

• High Dimensional Statistical Signal Processing
• Compressive Sensing
• Tensor Methods
• +۴ مورد دیگر

Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.

Alison Rodgerمشاهده پروفایل

استاد

• Biophysical Chemistry
• Physical Chemistry
• Analytical Science
• +۳ مورد دیگر

Prof Alison Rodger is a Professor in the Research School of Chemistry at The Australian National University, where she leads research in biophysical spectroscopy. Formerly at Macquarie University (2017–2024) and the University of Warwick (1990s–2017), she specializes in developing advanced spectroscopic techniques for biomacromolecule analysis. Her work integrates circular dichroism, linear dichroism, and Raman methods to study nucleic acids, proteins, and membrane systems. She co-directs the ARC-funded Industrial Transformation Training Centre in Facilitated Advancement of Australia’s Bioactives (FAAB) and runs an open-access biophysical spectroscopy lab. Key awards include Fellowships from the Australian Academy of Science (2021) and Royal Society of Chemistry (2000), and recognition in the Analytical Science Power List (2015). Education: BSc, PhD, DSc (Sydney University) MA (Oxford) DSc (Warwick) BA (Chester) Research Interests: Development of polarized-light spectroscopies for biomacromolecule analysis, including electronic/circular dichroism, Raman spectroscopy, and hybrid techniques. Applications span protein-DNA interactions, membrane biophysics, and biopharmaceutical characterization. She invented five spectroscopic techniques, including micro-volume Couette flow linear dichroism and fluorescence-detected linear dichroism. Awards & Roles: Fellow of the Australian Academy of Science Fellow of the Royal Society of Chemistry Emeritus Professor (University of Warwick) Recipient of Science Teachers of NSW Dedicated Service Award Consultant to European Science Foundation CASPER project Advising & Grants: Supervises PhD students in interdisciplinary biophysical chemistry. Led the EPSRC-funded Molecular Organisation and Assembly in Cells DTC at Warwick. Currently co-directs the ARC FAAB Centre, focusing on bioactive product characterization. Labs & Collaborations: Operates an open-access biophysical spectroscopy lab supporting academic and commercial users. Collaborations span mathematics, medicine, and engineering, with projects on DNA knotting, antimicrobial peptides, and nanomaterials for biosensing.

Wei Sunمشاهده پروفایل

استادیار

• Energy Systems Integration
• Renewable Energy
• Data Science
• +۳ مورد دیگر

Dr. Wei Sun is a Chancellor's Fellow (equivalent to Assistant Professor) in Energy Systems Integration at the University of Edinburgh's School of Engineering. His research specializes in low-carbon energy systems with high renewable penetration, utilizing data science and optimization techniques. He contributes to major initiatives like the National Centre for Energy Systems Integration (CESI) and Hydrogen’s Value in Energy Systems (HYVE). Research encompasses network integration of distributed energy resources, climate impacts on renewables, and multi-vector energy systems. Recent publications focus on hybrid energy storage, hydrogen integration, and machine learning applications for system optimization. He holds professional credentials as a Chartered Engineer (CEng) with memberships in IET and IEEE. Teaching includes Hydropower Design Projects and Renewable Energy Fundamentals. Visiting research affiliations include University College London, enhancing collaborative networks in energy systems research.

Hamed Nabizadeh Rafsanjaniمشاهده پروفایل

مدرس

• IoT
• AI in Architecture, Engineering, and Construction (AEC)
• Digital Twin Technology
• +۵ مورد دیگر

Hamed Nabizadeh Rafsanjani, Ph.D., P.E., ENV SP is a Lecturer at the University of Georgia (UGA) College of Engineering since 2018. He holds a Ph.D. and master’s degrees in Construction Engineering and Management from the University of Nebraska-Lincoln (UNL). Prior to UGA, he served as an assistant professor and visiting lecturer at various universities, earning accolades such as the Best Teacher Award and Best Researcher Award. His research focuses on IoT, AI, and Digital Twin technologies applied to Architecture, Engineering, and Construction (AEC) industries. As director of iSC-LAB, he develops IoT-based platforms to analyze building occupants’ energy-use behaviors and improve learning environments. His research interests span smart building systems, non-intrusive energy monitoring, and occupant behavior analysis. Key projects include a global occupant behavior database and an IoT-based smartphone energy assistant (iSEA). He has secured grants from agencies like the National Science Foundation (NSF), contributing to studies on energy efficiency, construction project management, and sustainability in built environments. Rafsanjani’s publications (2018–2023) emphasize AI-driven solutions for AEC, IoT applications in energy management, and adaptive learning systems. His work bridges technology and human behavior to optimize building performance and educational outcomes. Awards reflect his dual excellence in pedagogy and research, with ongoing contributions to the Sustainable Human-Building Ecosystems field.

Amir Keshmiriمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Biomedical Engineering
• Turbulence Modeling
• +۵ مورد دیگر

Dr. Amir Keshmiri is an Associate Professor/Reader in Computational Fluid Dynamics (CFD) and Head of Business Engagement & Innovation in the Faculty of Science & Engineering at the University of Manchester. He leads the ManchesterCFD research group and has founded multiple companies including Helical Ridge Graft and Affordable Health Technologies. His expertise spans CFD, biomedical engineering, turbulence modeling, and energy systems. Keshmiri holds a PhD in CFD from the University of Manchester and has secured over £2m in research funding since 2010. Education: BEng in Mechanical Engineering (2005, University of Manchester) MSc in Thermal Power & Fluids Engineering (2006, University of Manchester) PhD in Computational Fluid Dynamics (2010, University of Manchester) Professional Qualification in Entrepreneurship & Innovation (2016, LSE) Research Interests: His work focuses on CFD applications in biomedical devices, energy systems, and fluid-structure interaction. Notable contributions include breakthroughs in vascular prostheses (Helical Ridge Graft) and aerodynamic optimization of winglet designs. He also explores turbulence modeling impacts on energy sectors and hydrogen storage innovations. Key Awards: EPSRC Doctoral Prize (2010) IMechE Thomas Common Award (2013) RAEng Making a Difference Award (2019) Collaborate to Innovate Award (2017) Grants & Projects: Principal Investigator (PI) on 85% of £2m+ research grants from EPSRC, MRC, and industry. Active in projects like 'Fluids Research Group' and 'Engineering Sustainable Solar Energy.' Labs & Teams: Leads the ManchesterCFD team, a CFD research and consultancy group, and collaborates with industry partners like AECOM. His work contributes to UN Sustainable Development Goals in healthcare and energy sustainability.

• High Voltage Engineering
• Power Systems
• Partial Discharge Monitoring
• +۵ مورد دیگر

Uwe Schichler is a Professor at the Institute of High Voltage Engineering and System Management at TU Graz. His research focuses on high voltage engineering, partial discharge monitoring, and the integration of renewable energy into power systems. He leads projects on MVDC cable systems, insulation materials, and grid modernization. His work bridges academic research with industrial applications, including contributions to CIGRE standards and eco-friendly alternative gases for gas-insulated systems. Research interests include high-voltage insulation materials (e.g., XLPE cables, 3D printed insulators), DC grid technologies, and advanced diagnostics for power equipment. He explores novel sensors for dynamic line rating and machine learning applications in partial discharge classification. His team investigates innovative propulsion systems like ionic wind for aircraft and addresses environmental challenges in power systems. Recent publications emphasize standardization of high-voltage testing, DC system qualification, and corona noise reduction in overhead lines. He collaborates with industry partners on projects like MVDC battery storage systems and hybrid AC/DC grids. His work supports the energy transition through improved grid efficiency and sustainability.

Pedro Nardelliمشاهده پروفایل

استاد

• Cyber-physical Systems
• IoT in Energy Systems
• Smart Grids
• +۵ مورد دیگر

Pedro Nardelli is a Full Professor (tenured) of IoT in Energy Systems at the LUT School of Energy Systems, Lappeenranta University of Technology (Finland). He holds a double doctoral degree in electrical engineering from the University of Campinas (Brazil) and communications engineering from the University of Oulu (Finland). As a Docent in Information Processing and Communications Strategies for Energy Systems, he leads research in cyber-physical systems, smart grids, and 6G-enabled energy networks. Research Interests : His work focuses on integrating IoT, AI, and communication technologies into energy systems. Key areas include cyber-physical systems, UAV-enabled networks, sustainable energy management, and cybersecurity in critical infrastructure. He emphasizes interdisciplinary approaches to address challenges in the green-digital transition. Projects & Leadership : He is Principal Investigator for projects such as 'Energy-Conscious Operation: Network Efficiency for Wireless Sustainability' (2024–2026) and coordinates the Finnish-Brazilian AI and 5G training program. Past roles include leadership in the 'Hydrogen and Carbon Value Chains in Green Electrification' initiative (2021–2024). Publications : Recent work explores topics like hybrid optimal power flow models, UAV-IRS NOMA systems, and energy-centric analysis. His research bridges theoretical frameworks with practical applications, emphasizing sustainability and resilience in energy networks. Labs & Teams : Leads the IoT Solutions group within LUT's MORE SIM research platform, focusing on simulation-driven innovation for energy systems.

Domenico Ciuonzoمشاهده پروفایل

استادیار

• Information Fusion
• Statistical Signal Processing
• Wireless Sensor Networks
• +۹ مورد دیگر

Dr. Domenico Ciuonzo is an Assistant Professor at the University of Naples Federico II , Department of Electrical Engineering and Information Technologies. His research focuses on Information Fusion , Statistical Signal Processing , Wireless Sensor Networks , Network Analytics , Artificial Intelligence , and Wireless Communications . He actively explores applications in deep learning , encrypted traffic classification , and IoT security . His recent work emphasizes explainable AI , quantum machine learning , and class incremental learning for network intrusion detection. Over 30+ publications span topics like decision fusion in MIMO systems , mobile app traffic analysis , and cybersecurity for IoT . He is also involved in dataset development (e.g., MIRAGE-APP× ACT-2024) and algorithmic frameworks for encrypted traffic classification . Key research trends include: Optimizing holographic communication systems and reconfigurable intelligent surfaces for wireless networks Developing attack-adaptive intrusion detection systems resilient to emerging threats Advancing quantum computing applications in network monitoring and prediction His research integrates statistical signal processing with modern AI techniques , addressing challenges in data privacy , network security , and resource-constrained environments .

Xiaonan Guoمشاهده پروفایل

استادیار

• Cyber-physical Systems Security
• Mobile Device Security
• IoT and Wearable Technologies
• +۳ مورد دیگر

Xiaonan Guo is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. His research focuses on security and privacy in cyber-physical systems, mobile device security, IoT, mobile healthcare, and machine learning applications in mobile computing. He holds a PhD in Computer Science from the Hong Kong University of Science and Technology. His work spans innovative applications of mmWave technology for authentication, health monitoring, and activity recognition, alongside contributions to privacy-preserving systems and mobile deep learning optimization. Recent research emphasizes contactless human concentration monitoring, secure mobile DNN execution, and universal adversarial attacks against mmWave-based systems. Xiaonan’s publications reflect a strong focus on mobile and IoT-driven healthcare solutions, wearable device security, and cross-technology localization. His articles often bridge theoretical advancements with real-world applications in smart healthcare systems and pervasive computing environments.

Yue Guoمشاهده پروفایل

استاد

• Energy Storage Systems
• Automotive Engineering
• Lithium-Ion Batteries
• +۴ مورد دیگر

Yue Guo is a Professor of Battery Systems at Coventry University, affiliated with the Institute of Future Transport and Cities and the Centre for E-Mobility and Clean Growth. He holds a BEng in Electrical Engineering and Automation from Harbin Institute of Technology (China), an MSc in Management of Information Technology from the University of Nottingham, a PhD in Model-Based Design for Automotive Electronic Systems from the University of Warwick, and an MBA in Global Energy Industry from Warwick Business School. His research focuses on energy storage systems, battery thermal management, and automotive electronics. He has collaborated extensively with the automotive industry on projects involving system-of-systems modeling, Li-ion battery testing, and low-carbon transport technologies. Notable roles include former Project Manager and Deputy Head of the Energy Innovation Centre at WMG, University of Warwick, and current Chair of Battery Systems at C-ALPS. Professional memberships include Chartered Engineer, IEEE Senior Member, and SAE International Battery Thermal Management Committee. Key research trends in his publications include predictive battery diagnostics, thermal runaway prevention, and LCA analysis of battery production. His work emphasizes real-time monitoring technologies and sustainable energy storage solutions. Projects address challenges in battery aging, environmental impacts, and integration into low-carbon vehicle systems. Awards and recognitions include his academic appointments and industry collaborations, though no specific scientific awards are listed. He has advised on numerous research initiatives and contributed to advancing battery technologies through innovative thermal management systems and model-based testing frameworks. Labs and teams: Active in the Centre for E-Mobility and Clean Growth, leading interdisciplinary projects at Coventry University's Institute of Future Transport and Cities. Collaborations span academic and industrial partners globally, focusing on next-generation energy storage solutions.

• Cyber Security
• Internet of Things
• Blockchain
• +۴ مورد دیگر

Dr. Baraq Ghaleb is an Associate Professor within the Centre for Distributed Computing, Networks and Security at Edinburgh Napier University's School of Computing Engineering and the Built Environment. He actively delivers and leads modules for both Undergraduate and Postgraduate programs in the Cyber Security and Systems Engineering subject group. Dr. Ghaleb earned his PhD from Edinburgh Napier University in June 2019, following completion of his MSc and BSc degrees. His academic journey has positioned him as an expert in cybersecurity and IoT technologies. His research focuses on investigating security vulnerabilities of Internet of Things standards and utilizing cutting-edge advancements to address these vulnerabilities. With expertise spanning Cyber Security, Internet of Things, Blockchain, and Machine Learning, Dr. Ghaleb bridges theoretical research with practical applications, particularly in securing IoT ecosystems. His recent work shows a clear progression from foundational networking research to contemporary security challenges involving blockchain, cryptography, and AI-enhanced security solutions. Dr. Ghaleb has secured significant research funding as Principal Investigator and Co-Investigator across multiple projects with a total budget of approximately £435,000. His externally funded projects include SafeNet (Carnegie Trust), Trusted Threat Sharing (Innovate UK), TruElect (Innovate UK), and LastingAsset (Innovate UK). He currently supervises numerous PhD students working on diverse security challenges: Blockchain-based Privacy-preserving Cybersecurity Intelligence Sharing (Elfatih Ahmed) Enhancing Security and Privacy of Blockchain-based Healthcare Systems (Faneela) Design of complex encryption schemes for IoT security (Shahbaz Khan) Intelligent and Privacy-Preserving Security Solutions for IoT Networks (Iain Baird) Dr. Ghaleb is affiliated with the Centre for Distributed Computing, Networking and Security and the Centre for Cybersecurity, IoT and Cyberphysical Systems, where he contributes to cutting-edge research in secure network architectures, cryptographic techniques, and privacy-preserving frameworks across multiple domains including automotive supply chains, healthcare systems, and environmental monitoring.

Prasad Tadepalliمشاهده پروفایل

استاد

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

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

Harish Sarma Krishnamoorthyمشاهده پروفایل

دانشیار

• High-density power converters
• Solid-state transformers
• Power electronics for renewable energy integration
• +۳ مورد دیگر

Dr. Harish Sarma Krishnamoorthy serves as Associate Professor in the Department of Electrical and Computer Engineering at the University of Houston, College of Engineering. He holds concurrent roles as Associate Director of the PEMSEC Consortium and Associate Editor of IEEE Transactions on Power Electronics. Ph.D. in Electrical and Computer Engineering (2015), Texas A&M University B.Tech. in Electrical and Electronics Engineering (2008), National Institute of Technology Tiruchirappalli, India His research focuses on high-density power conversion systems with particular emphasis on solid-state transformers for renewable energy integration, machine learning-driven health analytics for power electronics, and converter solutions for extreme environments in aerospace, defense, and subsea applications. Current projects include NSF CAREER-funded work on Edge Intelligence for power converters and DOE ARPA-E OPEN 2021 Mini-PulPS initiative for pulsed power systems. Recent publications highlight advancements in GaN HEMT reliability testing, resonant circuit breaker topologies, and multiport energy routers for hybrid renewable-oil & gas systems. His work spans converter design for 5G communication, NMR metrology, and electric aircraft propulsion architectures. NSF CAREER Award recipient (2023) IEEE PELS Young Professional Exceptional Service Award OTC Emerging Leader recognition (2022) GE 6-Sigma Green Belt Certification Best Presentation Awards at IEEE APEC He teaches graduate courses on power converters, modeling, and renewable energy systems while leading industry-funded research programs with applications in electric vehicles, data centers, and offshore energy systems.

• Air Transportation
• Queueing Theory
• Simulation
• +۵ مورد دیگر

John F. Shortle is a Professor and Chair in the Department of Systems Engineering and Operations Research at George Mason University (GMU), part of the Volgenau School of Engineering. He specializes in applying queueing theory and stochastic processes to aviation safety, air transportation systems, and energy systems. Shortle has led major research initiatives funded by the FAA, NASA, and the Department of Energy, focusing on improving air traffic safety through advanced simulation and risk analysis techniques. Affiliations: Center for Air Transportation Systems Research (CATSR), GMU Education: PhD (UC Berkeley), MS (UC Berkeley), BS (Harvey Mudd College) Research Interests Shortle’s work emphasizes simulation methodologies, queueing theory applications, and stochastic modeling for critical infrastructure systems. Key areas include: Collision risk analysis in air transportation Aviation safety modeling (e.g., wake turbulence, event tree analysis) Energy systems reliability (e.g., blackout analysis) Autonomous systems validation Publications & Awards He co-authored the widely used textbook Fundamentals of Queueing Theory (5th ed., Wiley, 2018) and holds over 100 peer-reviewed publications. Notable awards include the Daniel H. Wagner Prize (2000) and the Military Operations Research Journal Award (2016). Leadership & Service President, INFORMS Simulation Society (2016–2018) Board Member, Winter Simulation Conference (2023–present) Editorial roles: IEEE Transactions on Reliability , Journal of Probability and Statistical Science Teaching Teaches advanced courses in stochastic processes (OR 645), queueing theory (OR 647), and dynamic systems (SYST 320).

• Digital Signal Processing
• Parallel Computing Architectures
• Nonlinear Dynamics
• +۳ مورد دیگر

Prof. Victor Grigoras is a faculty member at the Technical University of Iași, holding the rank of Professor. He specializes in electrical engineering and computer science, focusing on signal processing, parallel architectures, and nonlinear dynamics in power systems. His research interests include smart grid technologies, renewable energy integration, and data-driven methodologies for grid optimization. He teaches courses such as 'Semnale, Circuite și Sisteme' and 'Algoritmi și Structuri Paralele de Calcul.' His research spans over 15 recent articles (2021–2025), emphasizing advancements in smart grid automation, machine learning applications in voltage quality analysis, and optimal power flow solutions for renewable integration. Notable trends include SCADA system improvements, energy storage strategies for prosumer grids, and IoT-based energy management. His work addresses challenges in grid reliability, power quality, and future urban grid resilience under high EV adoption scenarios. Prof. Grigoras has contributed to frameworks for electric vehicle charging station placement, hydropower plant optimization via data mining, and demand response mechanisms using smart metering. His methodologies often combine clustering techniques with fuzzy logic or metaheuristic algorithms to solve complex grid problems.