جست‌وجوی استاد و پژوهشگر

Shuo Yuمشاهده پروفایل

استادیار

• Health Analytics
• Interpretable Deep Learning
• Text Mining
• +۴ مورد دیگر

Shuo Yu is an Assistant Professor of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University, and serves as the Wetherbe Professor in Management Information Systems. He directs the MIS Doctoral Program and teaches courses such as Big Data Security, Information Security, and Data Analytics Tools. Yu holds a PhD from the University of Arizona (Management Information Systems) and a BBA from Tsinghua University (Information Management). His research focuses on data science applications in healthcare analytics, cybersecurity, and e-commerce. Key areas include interpretable deep learning, text mining, and mobile health technologies. Notable projects include SilverLink, a smart home monitoring system for senior care using wearable sensors and machine learning. Yu’s publications span journals like MIS Quarterly and IEEE Transactions, emphasizing innovations in health profiling, fall prevention, and recommendation systems. His work integrates techniques such as Hidden Markov Models, Generative Adversarial Networks, and Bayesian networks. No scientific awards are listed. He is actively involved in advising doctoral students and contributes to the Association for Information Systems (AIS).

• 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.

Melanie Toryمشاهده پروفایل

استاد

• Data Visualization
• Human-Centered Computing
• Natural Language Processing
• +۲ مورد دیگر

Melanie Tory is a Professor at Northeastern University's Khoury College of Computer Sciences, serving as Professor of the Practice and Director of Data Visualization. Her research focuses on data visualization and human-centered computing, with interdisciplinary applications in healthcare, energy systems, and natural language processing. Her recent work explores the intersection of machine learning and visualization in domains like cardiothoracic care and wind farm optimization. She also investigates conversational interfaces for data visualization, focusing on intent recognition and pragmatic language use in analytical workflows. Key projects include the HEART initiative for real-time medical analytics and schema design for dynamic visualizations. Melanie advises PhD students Carey Barry, Shani Spivak, and Timothy Yim, and contributes to visualization education through research faculty roles. She actively publishes in venues like IEEE PacificVis, addressing challenges in visual utility evaluation, vague command modifiers, and collaborative analysis.

• Aerodynamics
• Aeroacoustics
• Renewable Energy Systems
• +۴ مورد دیگر

Delibra Giovanni is an Associate Professor at Sapienza University of Rome, specializing in aerodynamics, aeroacoustics, and renewable energy systems. His research focuses on optimizing turbomachinery performance, including axial fans, wind turbines, and hydrogen storage systems. He employs advanced computational fluid dynamics (CFD) and machine learning techniques to address challenges in renewable energy integration, thermal management, and noise reduction. Key research areas include: Wind energy systems and offshore wind farm design Hydrogen storage and safety in green energy applications Aeroacoustic control in industrial fans and turbines CFD-based optimization of heat exchangers and cooling systems Recent work emphasizes the integration of photovoltaic and biomass systems in renewable energy communities, as well as experimental validation of wave energy turbines. His publications highlight innovations in fan blade design, leakage modeling, and multi-objective optimization frameworks for sustainable energy infrastructure. Collaborations involve both academic institutions and industry partners, focusing on real-world applications such as tunnel ventilation systems and Mediterranean island energy solutions. Giovanni's contributions bridge theoretical modeling with practical engineering challenges in the transition to clean energy.

Julian Gardnerمشاهده پروفایل

استاد

• Microsensors
• MEMS Technology
• Electronic Engineering
• +۵ مورد دیگر

Julian Gardner is a Professor of Electronic Engineering at the University of Warwick's School of Engineering, leading the Electrical and Electronic Engineering Discipline Stream. He holds a First in Physics from Birmingham University, a PhD in Physical Electronics from Cambridge, and a DSc in Electronic Engineering from Warwick. His 30-year career includes 5 years in industry as an R&D Engineer and extensive research in chemical microsensors, founding companies like Cambridge CMOS Sensors (sold to ams in 2016) and spin-offs Flusso Ltd and Sorex Sensor Ltd. His expertise spans MEMS-based sensors, signal processing, and AI-driven data analysis. Research interests include chemical/environmental microsensors, MEMS devices, smart systems, electronic noses/tongues, and biomedical engineering. He teaches ES434 (ASICs, MEMS, and Smart Devices) and has authored 10 books, 500+ papers, and 20+ patents. Awards include the Royal Society Mullard Award and Fellowships from IET, IEEE, and the Royal Academy of Engineering. Education: B.Sc. (Hons) Physics, University of Birmingham Ph.D. Physical Electronics, University of Cambridge D.Sc. Electronic Engineering, University of Warwick Research Highlights: Electronic noses for healthcare and environmental monitoring CMOS-integrated gas sensors MEMS thermal flow sensors AI-driven sensor data analysis Awards: Royal Society Mullard Award (2018) IET & IEEE Fellowships Grants/Projects: Development of low-cost air quality monitoring systems Smart city IoT sensor networks Prostate cancer detection via lab-on-a-chip His labs include collaborations on particle sensing, thermal modulation techniques, and biomimetic systems. Ongoing work focuses on edge AI for real-time environmental sensing and wearable health monitoring devices.

Sérgio Nunesمشاهده پروفایل

استاد

• Information Security
• Risk Management
• Cybersecurity
• +۳ مورد دیگر

Sérgio Nunes is a faculty member at Instituto Superior de Economia e Gestão (ISEG) , Universidade de Lisboa. He holds a Ph.D. in Information Systems Management (2018) and M.Sc. degrees in Information Technology and Information Security (2009). His academic career includes teaching roles in Cybersecurity , Information Technology , Big Data Analytics , and Information Systems Architecture for both undergraduate and graduate programs. Education Ph.D. in Information Systems Management (ISEG-UTL, 2018) M.Sc. in Information Security (Faculdade de Ciências da Universidade de Lisboa, 2009) B.Sc. in Computer Engineering (Universidade Autónoma de Lisboa, 2003) His research focuses on Information Security , Risk Management , and Human Factors in Cybersecurity . He has published extensively on these topics, including journal articles in Journal of Information System Security , book chapters, and conference proceedings. His 2019 systematic literature review on Information Security Risk Management has become a foundational reference in the field. Over the past decade, Nunes has supervised multiple Master's theses on topics including: Cybersecurity Risk Management , Supplier Cybersecurity Risks , CISO Function , and Security Policy Implementation . His work bridges academic research with practical application, evidenced by practitioner presentations at events like the CISO 360 Congress and InfosecWeek . He contributes to academic infrastructure through involvement in ISEG's research labs , including the Data Lab , Futures Lab , and Policy Lab . His publications reveal consistent focus areas: Risk Quantification , Security Governance , Human-Centric Cybersecurity , COBIT Frameworks , and ITIL-based Process Optimization .

Karen Butler-Purryمشاهده پروفایل

استاد

• Power System Automation
• Smart Grid Security
• Distribution Automation
• +۴ مورد دیگر

Karen Butler-Purry is a Professor of Electrical & Computer Engineering at Texas A&M University, holding the Raytheon Company Professorship. She is affiliated with the Power System Automation Laboratory and the College of Engineering. Her research focuses on intelligent systems for power distribution automation, fault diagnosis, and renewable energy integration. She completed her B.S. at Southern University (1985), M.S. at UT Austin (1987), and Ph.D. at Howard University (1994). Her work emphasizes cyber-physical systems, smart grid security, and microgrid resilience. Recent research includes AI-driven grid restoration, cybersecurity frameworks for distribution systems, and self-healing shipboard power systems. She has pioneered methods for transformer lifespan prediction and unbalanced load management in distributed networks. Publications span over three decades, with notable contributions to IEEE journals and conferences. Her educational initiatives include the Texas A&M System AGEP Alliance and LSAMP programs, advancing underrepresented minority participation in STEM academia. She leads interdisciplinary projects at the intersection of power systems, machine learning, and equity-focused academic workforce development. Labs/Teams: Principal Investigator in the Power System Automation Laboratory, collaborating with industry partners like Raytheon. Active in curriculum development for digital systems education through initiatives like the Enrichment Experiences in Engineering (E³) program.

Kaie Maennel, Agnė Brilingaitė, Linas Bukauskasمشاهده پروفایل

پژوهشگر

• Cybersecurity Education
• Cyber Defense Exercises (CDX)
• Psychological Safety in Teams
• +۴ مورد دیگر

Dr. Kaie Maennel, Dr. Agnė Brilingaitė, and Linas Bukauskas are cybersecurity education researchers who have co-authored studies on multidimensional approaches to Cyber Defense Exercises (CDX). Their work emphasizes integrating psychological safety , intrinsic motivation , and cognitive agility into cybersecurity training frameworks. They have contributed to understanding how behavioral aspects and educational theory can enhance technical skill development and team coordination during large-scale exercises like Locked Shields and Amber Mist. Key areas of focus include: Applying Self-Determination Theory (SDT) to improve participant motivation and learning outcomes. Developing adaptive performance metrics that evaluate non-technical skills such as communication and decision-making under stress. Advancing slow education techniques to scaffold technical and soft skill development in cyber operators. They have secured significant funding through the EEA Grants project ADVANCES , which aims to personalize cybersecurity competence development. Their research has been published in journals like Computers & Security and International Journal of Cyber Warfare and Terrorism , influencing pedagogical practices in hybrid cybersecurity exercises and incident response training.

• Wind turbines
• SCADA systems
• Wind power
• +۳ مورد دیگر

Francisco Jara Avila is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering Sciences within the Engineering Technology department, specializing in wind energy systems and machine learning applications. His work focuses on improving wind turbine performance monitoring and predictive capabilities. His research interests center on wind turbine analytics , where he develops advanced methodologies for power prediction, fault detection, and performance optimization. Using techniques like deep learning, Gaussian process regression, and signal processing, his work addresses critical challenges in wind farm operations including uncertainty quantification, spatial correlation analysis, and drivetrain health monitoring. His publication record shows a clear trajectory in wind energy analytics, with increasing sophistication from basic fault detection (2023) to sophisticated uncertainty-aware power prediction frameworks (2025). His work consistently bridges theoretical machine learning approaches with practical wind farm operational challenges. While no specific awards are listed in the available information, his publications in reputable journals like IOP Journal of Physics: Conference Series and Energies demonstrate recognition in the wind energy research community. As a researcher focused on technical development rather than academic mentoring, there's no indication of student supervision activities in the available information. His work appears to be conducted within the Acoustics & Vibrations Research Group, collaborating with researchers like Helsen, Verstraeten, and Nowe on wind energy analytics projects.

Andria Procopiouمشاهده پروفایل

مدرس

• Artificial Intelligence
• Cybersecurity
• Sports Analytics
• +۴ مورد دیگر

Dr. Andria Procopiou is a Lecturer in Artificial Intelligence and Cybersecurity at the University of Central Lancashire Cyprus, serving as Course Leader for the MSc Computing program and Deputy Course Leader for the MSc Cybersecurity. She holds a PhD in Computer Science (City, University of London, 2022), an MSc in Human-Computer Interaction (University College London, 2014), and a BSc in Computer Science (University of Surrey, 2013). Her research spans AI applications in sports analytics, cybersecurity for smart cities/IoT, hate speech detection, and metaverse safety. She has led modules in User Experience Design, Penetration Testing, and Cybersecurity. Research interests include AI-driven solutions for sports injury prediction, real-time DDoS detection in IoT networks, and ethical challenges in the metaverse. Her work emphasizes explainable AI (XAI) for transparency in decision-making systems. She has published extensively in journals like Wireless Communications and Mobile Computing and conferences such as IEEE CYBER and the International Conference on Human-Computer Interaction. Education: PhD Computer Science (City University London), MSc HCI (UCL), BSc Computer Science (University of Surrey) Key Roles: Course Leader (MSc Computing), Deputy Course Leader (MSc Cybersecurity) Administrative Contributions: Module leadership in AI, Cybersecurity, and Digital Forensics Her recent publications address critical issues like privacy concerns in smart campuses, gender-based hate speech in virtual spaces, and injury patterns in professional football using data analytics. She collaborates with industry on IoT security solutions and metaverse governance frameworks.

• Electrical Engineering
• Signal Processing
• Fault Detection
• +۵ مورد دیگر

Tarik Namas is a Senior Lecturer in the Department of Electrical and Electronics Engineering at the International University of Sarajevo's Faculty of Engineering and Natural Science. With over 15 years of academic experience, he has held roles including Teaching and Research Assistant (2006-2009), Technical and Industrial Instructor at Jubail Industrial College (2009-2011), and IT Assistant at Prince Sultan Military College. His expertise spans Electrical Engineering , Signal Processing , and Power Systems , with a focus on fault detection, impedance analysis, and augmented reality applications in healthcare.

Ashraf El-Hamalawiمشاهده پروفایل

دانشیار

• Numerical Modelling
• Geotechnical Engineering
• Civil Engineering
• +۵ مورد دیگر

Ashraf El-Hamalawi is an Associate Professor and Group Leader of the Geotechnical and Geomatics Group, specialising in engineering modelling and geotechnical systems. He holds a PhD from the University of Cambridge and is a Fellow of the Higher Education Academy (FHEA). His research focuses on numerical methods (FEM, BEM, DDA) for geotechnical challenges, soil-structure interaction, and infrastructure sustainability. Key areas include weather-driven deterioration of clay embankments, adaptive mesh refinement, and SCADA systems for construction monitoring. Education: BEng (Hons) in Engineering, PhD (Cambridge) Professional Affiliations: MASCE, MCGS, PE Research interests span geotechnical engineering, computational mechanics, and infrastructure asset management. He develops predictive models for slope stability, clay embankment degradation, and wear analysis in offshore structures. His work integrates advanced sensing technologies for non-destructive testing and environmental monitoring, including ammonia detection and landmine identification. Publications emphasize long-term infrastructure deterioration, numerical modelling frameworks, and sustainable retrofitting. His contributions to converged network systems and mobile collaboration tools in construction enhance industry practices.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +۵ مورد دیگر

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.

Kristan Stoddartمشاهده پروفایل

دانشیار

• Cybersecurity
• Cyberwarfare
• Cyberespionage
• +۵ مورد دیگر

Kristan Stoddart is an Associate Professor of Cyber Threats at Swansea University's School of Social Sciences, with additional affiliation at the Hillary Rodham Clinton School of Law. He serves as a member of the Cyber Threats Research Centre (CYTREC) and has previously held positions as a Reader in International Politics at Aberystwyth University, where he also served as Deputy Director of the Centre for Intelligence and International Security Studies. His research spans cybersecurity, cyberwarfare, cyberespionage, cyber terrorism, cybercrime, international security, intelligence studies, and nuclear weapons. Stoddart's work bridges historical analysis of Cold War nuclear strategy with contemporary cyber threats, creating a unique interdisciplinary approach to security studies. His expertise is particularly focused on the intersection of cyber operations with critical national infrastructure protection and geopolitical conflicts. Stoddart's publications reveal a clear evolution from historical nuclear strategy research to contemporary cyber security challenges. His recent works focus on Russian cyber campaigns against Ukraine, Chinese offensive cyberespionage, and threats to critical infrastructure. This progression demonstrates his ability to apply historical understanding of strategic competition to emerging cyber domains, with particular emphasis on the 'gray zone' between peace and war where cyber operations increasingly occur. Professional Recognition: Fellow of the Higher Education Academy Fellow of the Royal Historical Society Fellow of the Royal Society of Arts (FRSA) since 2022 Research Leadership: Stoddart has secured significant research funding including a €30,000 EU grant for 'EU Resilience Against Hybrid Warfare' (2021) and served as Principal Investigator for a £1.2 million project on 'SCADA Systems and Cyber Security Lifecycles' (2014-2017) funded by Airbus Group and the Welsh Government. He has supervised multiple PhD students working on topics including social media evidence, voluntary sector regulation, and crypto-crimes. Professional Engagement: Stoddart is an active member of the Project on Nuclear Issues at the Center for Strategic and International Studies in Washington DC. He has spoken at major international conferences including NATO, GCHQ, and US Strategic Command, and has provided expert commentary for media outlets including the BBC. His public engagement includes keynote lectures such as 'Edward Snowden and You: Negotiating the Post 9/11 Surveillance State' broadcast on C-Span.

Angela Meyerمشاهده پروفایل

استادیار

• Energy Meteorology
• Artificial Intelligence
• Atmospheric Science
• +۵ مورد دیگر

Dr. Angela Meyer is an Assistant Professor of Energy Meteorology and Artificial Intelligence at TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing since October 2023. She concurrently leads the Energy Weather & AI Lab at the Bern University of Applied Sciences (BFH), School of Engineering and Computer Science. She earned her PhD in atmospheric physics from ETH Zurich (2015) and a master’s degree in mathematics from the University of Cambridge (2009). Research Focus: Intersection of data science, atmospheric science, and renewable energy applications. Machine learning for solar and wind energy forecasting. Federated learning for privacy-preserving wind turbine condition monitoring. Satellite-based solar radiation retrieval and bias correction. Probabilistic intraday and sub-seasonal forecasting. Her research is supported by major grants from the Swiss National Science Foundation (SNSF) and Innosuisse , and she is a project partner in the Horizon Europe UrbanAIR initiative. Scientific Contributions: Over 40 peer-reviewed publications since 2015 in journals such as Applied Energy , Solar Energy , Energy and AI , and Journal of Climate . Key publications include advances in deep generative models for solar forecasting, federated learning in renewable energy, and AI-based satellite retrieval of solar radiation. Active reviewer for Applied Energy , Energies , and program committee member for ECML PKDD and LOD conferences. Research Team & Supervision: Dr. Meyer currently supervises six PhD candidates and six postdoctoral researchers across her labs at TU Delft and BFH. Her group focuses on AI-driven solutions for renewable energy reliability and resilience. Laboratories & Collaborations: Energy Weather & AI Lab – Bern University of Applied Sciences. GRS Lab – TU Delft, Department of Geoscience and Remote Sensing. Active collaborations with ETH Zurich, Siemens Smart Infrastructure, Hexagon AB, and NVIDIA. For more information, visit her personal website or ResearchGate profile .