جستجوی استاد راهنمای دانشگاهی

Hani Ragab Hassenمشاهده پروفایل

دانشیار

• Cybersecurity
• Deep Learning Applications
• Natural Language Processing (NLP) in Cybersecurity
• +۷ مورد دیگر

Hani Ragab Hassen is an Associate Professor leading the Cybersecurity with AI Research Group at the School of Mathematical & Computer Sciences, Heriot-Watt University. He specializes in cybersecurity, particularly in applying machine learning and NLP to address challenges such as malware analysis, intrusion detection, and access control systems. His roles include Associate Director of PGR Studies, Academic Integrity Officer, and former leadership in Learning & Teaching and Research programs. Research interests focus on cybersecurity fundamentals, including deep learning for malware detection, NLP-driven vulnerability assessment, and P2P systems. He has supervised numerous PhD students, currently advising three candidates on airport cybersecurity, NLP-based vulnerability detection, and network intrusion systems. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure security. Publications span malware detection frameworks (e.g., ActDroid, Droiddissector), network security tools (NTFA), and machine learning optimization (D2TS). He has developed datasets like SI22 for DDoS analysis and contributed to open-source tools. His research emphasizes practical applications of AI in securing networks and cloud environments. Notable contributions include advancing Android malware detection via control flow graphs and text analysis, and pioneering cloud security protocols like Stick for social platforms. His interdisciplinary work bridges cybersecurity with machine learning and data science, addressing real-world threats through innovative solutions.

• High-Performance Computing
• Parallel Computing
• Data Compression
• +۴ مورد دیگر

Kento Sato is a researcher specializing in High-Performance Computing (HPC), with a focus on checkpointing systems, data compression, and parallel computing optimization. His work addresses challenges in fault tolerance, algorithmic efficiency, and resource management in large-scale computing environments. Collaborating with institutions like the Joint-Laboratory of Extreme Scale Computing, he explores trade-offs between compression efficiency and hardware costs, as well as automatic variable identification for checkpointing. His contributions span theoretical advancements and practical implementations, with notable publications in venues such as SC, IPDPS, and the International Journal of High Performance Computing Applications. Key Research Themes: Checkpointing protocols, lossy compression for scientific data, HPC workflow optimization. Long-term Collaboration: Regular co-authorship with experts like Satoshi Matsuoka and Martin Schulz. Publications consistently highlight innovations in fault tolerance mechanisms, adaptive hardware design, and scalable algorithms for machine learning and distributed systems.

• Computer Vision
• Machine Learning
• Computer Graphics
• +۵ مورد دیگر

Ziyan Wang is a researcher affiliated with Carnegie Mellon University's School of Computer Science, Department of Computer Science. Their work focuses on computer graphics, machine learning, and medical imaging, with a strong emphasis on dynamic capture and animation of human hair and heads. They hold a PhD in Computer Science from Carnegie Mellon University (2023). Research interests include 3D modeling, neural networks, reinforcement learning, and applications in medical informatics. Notable contributions include high-fidelity hair modeling using computed tomography and neural dynamic models for volumetric hair capture. Their work spans conferences like CVPR, NeurIPS, and ECCV, demonstrating expertise in both theoretical and applied aspects of computer vision and graphics. Publications highlight advancements in diffusion models, multi-agent reinforcement learning, and domain adaptation for fault diagnosis. Collaborations span institutions like the University of Washington, Max Planck Institute, and NVIDIA Research, reflecting interdisciplinary engagement. Recent work explores AI-generated content in virtual environments and cybersecurity in package management systems.

Adnan Siraj Rakinمشاهده پروفایل

استادیار

• AI Security
• Deep Learning
• Computer Vision
• +۳ مورد دیگر

Adnan Siraj Rakin is an Assistant Professor at Binghamton University's School of Computing. He holds a PhD and MS in Computer Engineering from Arizona State University (2022 and 2021) and a BS in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2016). His research focuses on AI security, including adversarial attacks on deep learning systems, model stealing, and hardware vulnerabilities. Notable contributions include defenses against bit-flip attacks, weight duplication frameworks, and RowHammer exploits. Research Interests: Adversarial Attacks (Input/Weight/Model Stealing) Deep Learning Security Hardware Vulnerabilities (e.g., FPGA/DRAM) Robust Neural Network Design Publications highlight advancements in detecting and mitigating adversarial perturbations, with work featured in CVPR, ICCV, and IEEE Security & Privacy. His recent efforts address LLM vulnerabilities and edge computing efficiency. He received the 2022-2023 Educator of the Year award from Binghamton's CS department. Current projects include developing full-stack obfuscation frameworks, secure domain adaptation, and exploring adversarial impacts on robotics systems.

Panayiotis Papadopoulosمشاهده پروفایل

پژوهشگر

• Control Theory
• Intelligent Buildings
• Power Electronics
• +۴ مورد دیگر

Panayiotis Papadopoulos is an Associate Research Scientist at The Cyprus Institute (CyI), affiliated with the Energy, Environment & Water Research Center (EEWRC). He holds a PhD in Electrical and Electronics Engineering from the University of Cyprus (2020), focusing on distributed monitoring and control frameworks for smart buildings. His research spans control theory, intelligent buildings, and power electronics, with an emphasis on fault diagnosis and energy efficiency. Key projects include the EU H2020 COLLECTiEF initiative, which aims to enhance energy flexibility in buildings through collective intelligence networks. His work integrates digital twin architectures, deep reinforcement learning, and model-based approaches to optimize HVAC systems, power electronics, and grid interactions. He has contributed to sensor fault detection in smart grids and building automation, emphasizing real-time data analysis and adaptive control strategies. Papadopoulos' contributions include over 15 peer-reviewed publications since 2013, focusing on fault-tolerant control, energy management systems, and smart building technologies. His research bridges theoretical control methodologies with practical applications in sustainable infrastructure.

Radu MARINESCUمشاهده پروفایل

استاد

• Software Engineering
• Software Quality Assurance
• Software Metrics
• +۳ مورد دیگر

Radu Marinescu is a Professor of Software Engineering at the Politehnica University of Timisoara, affiliated with the Department of Computer Science and Engineering. He leads the LOOSE Research Group and is a senior researcher at the e-Austria Institute. His primary roles include teaching graduate courses like Foundations of Software Engineering and Research Topics in Software Engineering. Marinescu's research focuses on software quality assurance, metrics, and maintenance, emphasizing object-oriented systems. He co-founded Intooitus, a spin-off company that develops quality assessment tools like inFusion and inCode. Marinescu holds a Dr. Habil. Eng. and a joint PhD ('magna cum laude') from Politehnica University of Timisoara and University of Karlsruhe. He has authored over 30 international publications and co-authored the best-selling book Object-Oriented Metrics in Practice (Springer). His awards include the IBM John W. Backus Award (2009) and an IBM Eclipse Innovation Award (2006). Marinescu has served as General Chair for ICSM 2010 and on numerous conference program committees. His research projects include developing tools like iPlasma for software analysis and inCode for real-time design flaw detection. Marinescu has advised four PhD students and participated in consultancy projects for companies like Nokia, Alcatel-Lucent, and Siemens VDO Automotive. He actively contributes to the software engineering community through editorial roles, conference organization, and evaluations for EU research programs.

James Nabityمشاهده پروفایل

دانشیار

• Environmental Engineering
• Aerospace Engineering
• Space Systems Design
• +۳ مورد دیگر

James Nabity is an Associate Professor and Associate Chair for Undergraduate Operations at the University of Colorado's College of Engineering and Applied Science, affiliated with BIOSERVE Space Technologies. His research focuses on Environmental Control & Life Support Systems (ECLSS), space habitat design, propulsion, and power systems for human spaceflight. He earned a Ph.D. in Mechanical Engineering from the University of Colorado (2007), an M.S. in Aeronautics & Astronautics from the Naval Postgraduate School (1989), and a B.S. in Mechanical Engineering from the University of Nebraska (1983). Professional experience includes roles as Principal Engineer at TDA Research (2007-2013) and Mechanical Engineer at the Naval Air Warfare Center Weapons Division (1983-1999). His work emphasizes advanced materials, thermal systems, and bioregenerative life support solutions. Research interests span CO2 capture technologies, ionic liquid membranes, and thermal control architectures. Notable achievements include the 2017 Outstanding Faculty Advisor Award and selection as a NASA astronaut candidate in 2009. His contributions bridge academic research with practical aerospace engineering challenges. Publications highlight innovations in ECLSS design, space habitat systems, and propulsion architectures for Mars missions. Labs/teams include BIOSERVE’s collaborative initiatives in space biotechnology and environmental control systems.

Alfonso Capozzoliمشاهده پروفایل

استاد

• Building Automation
• Energy Management Systems
• Data Analytics
• +۴ مورد دیگر

Alfonso Capozzoli is a Full Professor at the Department of Energy (DENERG) of Politecnico di Torino, coordinating the College of Electrical and Energy Engineering. He specializes in building energy systems, data-driven energy management, and smart building technologies. His roles include membership in the SmartData@PoliTO Lab and editorial boards of journals like ENERGY AND AI and SUSTAINABLE ENERGY, GRIDS AND NETWORKS . Education: Graduated in Mechanical Engineering from the University of Naples Federico II (Italy) and earned a PhD in Engineering of Mechanical Systems. He has conducted visiting research at the University of Wollongong, Australia. Research focuses on adaptive control strategies, fault detection in HVAC systems, and energy data analytics. He leads projects on predictive energy management, smart building envelopes, and coordinated energy management in building clusters. His work bridges building physics and data science, emphasizing AI-driven solutions for energy efficiency. Teaching includes courses on building physics, energy management systems, and architectural sustainability at Politecnico di Torino. He supervises PhD students exploring topics like AI-based control systems, energy flexibility in communities, and semantic integration in building management. Key contributions include developing the CityLearn platform for grid-interactive communities and co-founding the BAEDA Lab. He has secured funding from EU projects (e.g., EU-DREAM) and national grants, focusing on data-driven solutions for energy systems. Awards include recognition for his students (e.g., Sabrina Savino’s AIDDA 2024 award) and PoliTo Quality Awards for doctoral research. His work addresses sustainable development goals related to energy (SDG 7), cities (SDG 11), and responsible consumption (SDG 12).

• Induced Seismicity
• Geothermal Energy
• Hydro-Geomechanical Modeling
• +۳ مورد دیگر

Dr. Vanille Ritz is a postdoctoral researcher at the Swiss Seismological Service (SED), ETH Zurich, with a split position between the Modelling Group and the GeoBest team. Her expertise lies in developing hydro-geomechanical and statistical models to understand and forecast induced seismicity in geothermal systems. She focuses on projects like GeoBest, which provides seismological support for Swiss geothermal energy initiatives, and has been a key contact for cantons Vaud, Jura, and Geneva. She holds a Ph.D. from ETH Zurich (2023) and a M.Sc. in Seismology from Université de Strasbourg (2017). Research interests include induced seismicity mitigation, geothermal reservoir engineering, and real-time seismic monitoring. Her work integrates data-driven approaches with physics-based models to optimize energy production while minimizing seismic risks. Notable contributions include the 'Transient Evolution of Earthquake Size Distributions' study (2022), recognized with the SSA Student Award. Education: Ph.D., ETH Zurich (2018–2023): Modelling Induced Seismicity in Deep Geothermal Systems M.Sc., Université de Strasbourg (2015–2017): Seismology Geophysical Engineering, Ecole et Observatoire des Sciences de la Terre (2014–2017) Professional activities include leadership in projects like DEEP (2021–2024), COSEISMIQ (2018–2021), and DESTRESS (2017–2018), advancing de-risking strategies for geothermal energy. Awards include the 2023 SSA Student Presentation Award for seismic risk indicator research. Her work bridges geoscience and engineering, emphasizing collaboration with cantonal governments and energy stakeholders to ensure sustainable geothermal development. Current efforts focus on synthetic benchmark datasets and real-time forecasting tools for induced seismicity management.

Zoleikha A. Bironمشاهده پروفایل

استادیار

• Cyber Physical Systems
• Intelligent Transportation Systems
• Control Theory
• +۴ مورد دیگر

Zoleikha A. Biron is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Florida. Her research focuses on cybersecurity, resilient control, and optimization of cyber-physical systems (CPS), with applications to intelligent transportation systems, smart power grids, and renewable energy integration. She holds a Ph.D. from Clemson University (2017), and prior to joining UF in 2019, she conducted postdoctoral research at Clemson University's International Center for Automotive Research (CU-ICAR). Education: Ph.D., Electrical Engineering, Clemson University, 2017 M.S., Electrical Engineering, K.N. Toosi University of Technology, Iran B.S., Electrical Engineering, University of Tehran, Iran Research Interests: Dr. Biron specializes in secure CPS design, fault diagnosis, and control theory. Her work integrates machine learning with control systems to enhance system resilience against cyber-physical threats. Key areas include: Resilient control strategies for power grids and transportation systems Game-theoretic methods for load management in microgrids Safety-critical autonomous systems and reinforcement learning Thermal fault diagnosis in lithium-ion batteries Funding & Projects: As Principal Investigator (PI), she leads NSF- and AFRL-funded projects focusing on cyber-survivability frameworks, real-time EV integration, and multi-pronged attack mitigation. Notable grants include: "A Multi-Level Cyber-Survivability Framework" (AFRL, 2024-2025) "Cyber Agility against Multi-Pronged Attacks" (AFRL, 2024-2025) "Real-time EV Integration Framework" (NSF CRII, 2022-2024) Labs & Teams: Her research group explores interdisciplinary challenges in CPS through hardware-in-the-loop (HIL) testing with OPAL-RT simulators. Active projects include motion planning for F1/10th autonomous vehicles and battery management systems for electric vehicles.

Janise McNairمشاهده پروفایل

استاد

• Wireless Networking
• Mobile Networking
• Smart Grid Security
• +۷ مورد دیگر

Janise McNair is a Professor in the Department of Electrical & Computer Engineering at the University of Florida. Her research focuses on wireless and mobile networking, next-generation wireless systems, and medium access control protocols. She holds the Nelms Faculty Fellowship and has been recognized with awards such as the IEEE Best Paper Session and IET Featured Article honors. Her work emphasizes cybersecurity in smart grids and software-defined networks (SDN), alongside innovations in edge computing and IoT integration. Education: PhD in Electrical & Computer Engineering from Georgia Institute of Technology (2000), MS (1993) and BSEE (1991) from University of Texas at Austin. Research Interests: Dr. McNair’s expertise spans cyber-physical security, resilient smart grid architectures, and machine learning applications in network defense. She has pioneered frameworks for detecting zero-day attacks in LEO constellation networks and enhancing SDN-based security in UAV relay systems. Her contributions to multi-criteria handover in multi-RAT networks and blockchain-enabled edge computing have advanced next-generation communication systems. Publications (Highlighted Trends): Recent work emphasizes cybersecurity in smart grids and SDN, with a focus on hybrid frameworks combining data fusion and physics-based models. Her research bridges theoretical contributions (e.g., graph-based attack detection) with practical implementations, such as IoT mesh networks for refrigeration monitoring. Over 150 publications span topics from 5G edge computing to satellite networking resilience. Scientific Awards: Includes Nelms Faculty Fellow (2022), IEEE INFOCOM Distinguished Membership (2019–2021), and multiple best paper awards. Advising & Grants: A recipient of the UF Doctoral Mentoring Award (2014), she advises students on cutting-edge network security and communications. Her grants fund projects in SDN resilience, IoT integration, and tactical mobile networks. Collaborations include the Warren B. Nelms Institute for the Connected World. Labs/Teams: Active in UF’s Connected World initiatives, focusing on secure IoT and smart infrastructure systems. Her research group develops prototypes for real-world applications in construction jobsites and rural connectivity solutions.

• Machine Learning
• Computer Vision
• Structural Health Monitoring
• +۱ مورد دیگر

Senjian An is a Senior Lecturer in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University, Faculty of Science and Engineering. His research focuses on machine learning, computer vision, and structural health monitoring, with applications in 3D displacement measurement and anomaly detection. Key interests include physics-guided neural networks, unsupervised learning for time-series data, and vision-based structural analysis. His recent publications emphasize robustness in damage detection, synthetic data generation, and transformer-based models for industrial monitoring. An's work integrates deep learning with engineering challenges, advancing non-contact measurement techniques for civil infrastructure. No students or awards are noted. Collaborative projects involve vibration analysis and maritime surveillance, reflecting a strong applied focus in AI and computer vision.

Maire O'Neillمشاهده پروفایل

استاد

• Hardware Security
• Cryptography
• Approximate Computing
• +۷ مورد دیگر

Maire O'Neill is a Professor at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, affiliated with the Secure Digital Systems (SDS) group and the Institute of Electronics, Communications & Information Technology (ECIT). Her research focuses on hardware security, cryptography, and secure embedded systems. She has held significant leadership roles and pioneered work in FPGA security, approximate computing, and post-quantum cryptography. Dr. O'Neill's academic journey includes over 20 years of contributions to secure digital systems, with a particular emphasis on cryptographic hardware, side-channel analysis, and IoT security. She has led major research projects such as the EU-funded 'TruDetect' initiative for hardware Trojan detection and the 'Secure IoT Processor Platform' project. Her work bridges theoretical research with practical applications, emphasizing real-world security challenges in electronics and computing. Education: Background in electrical engineering and computer science (details not explicitly stated in the text) Research Interests: Her work spans hardware security primitives, FPGA-based cryptographic solutions, and energy-efficient computing. She explores vulnerabilities like Rowhammer attacks and side-channel leaks while developing defenses through approximate computing and machine learning techniques. Her research addresses emerging threats in IoT, 5G networks, and post-quantum cryptography. Publications Trends: Recent work emphasizes machine learning applications in security (e.g., ML-KEM accelerator designs), hardware Trojan detection, and energy-efficient approximate computing. She frequently publishes in top-tier venues like IEEE Transactions and ACM conferences, with a focus on practical implementations and FPGA demonstrators. Awards: 2007: BFIIN ITEC Platinum Award, British Female Inventor of the Year, European Union Women Innovators 2015: Fellow of the Irish Academy of Engineering 2015: INVENT Award for collaborative innovation Advising & Grants: Supervised 8 PhD students (explicitly stated). Active in securing research grants (18 projects listed), including EU and industry collaborations. Engages in academic service roles like IEEE Distinguished Lecturer and international conference organization. Labs/Teams: Leads the Secure Digital Systems (SDS) group, collaborating with global partners on hardware security and IoT initiatives. Maintains strong ties with industry through projects like NIO New Deal Cyber Bid and TruDetect.

Jieying Chenمشاهده پروفایل

استادیار

• Knowledge Representation and Reasoning
• Semantic Web
• Ontology Engineering
• +۵ مورد دیگر

Roles and Affiliations: Jieying Chen is an Assistant Professor at the Faculty of Science, Department of Artificial Intelligence at Vrije Universiteit Amsterdam (Netherlands). She is also affiliated with the Network Institute. Previously, she held roles as Senior Research Associate at the University of Oxford, Postdoctoral Researcher at the University of Oslo, and Research Associate at the University of Manchester. She has industrial experience as an IT consultant at Boston Consulting Group (Nordics). Education: PhD from Université Paris-Saclay (France), Master's in Computational Logic (TU Dresden, Germany via Erasmus Mundus scholarship). Research Interests: Focus on Knowledge Representation and Reasoning (Description Logics, Ontology Modularity), Semantic Web (Ontology Modeling), AI for Social Goods (Bias Detection with LLMs), and applications in Cyber Physical Systems. Combines KR with NLP/ML techniques for ontology alignment and knowledge extraction. Recent Trends in Work: Recent publications emphasize ontology text alignment, bias detection in governmental documents using LLMs, and knowledge-based fault diagnosis in industrial systems. Her work bridges symbolic AI with machine learning, particularly in sustainability and industrial applications. Grants and Projects: Lead WP2 in the Zorro project (NWO-funded), Co-PI on VU-UT Alliance grant for fairness in HRM via LLM bias mitigation. Collaborates on ConCur (EPSRC-funded) and SIRIUS (Norwegian R&D center). Labs/Teams: Active in the Zorro project consortium involving ASML, Philips, and TNO-ESI. Leads ontology engineering initiatives in collaboration with industry partners like Bosch and Aibel AS.

• Mechanical Engineering Asset Management
• System Reliability Engineering
• Condition-Based Maintenance
• +۸ مورد دیگر

Tieling Zhang is an Associate Professor at the University of Wollongong's School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Tokyo University of Marine Science and Technology. His research focuses on system reliability engineering, machine learning applications, energy storage systems, and pipeline integrity management. He has published over 100 peer-reviewed articles and secured over AUD 7 million in research funding. Research Interests: Statistical Data Modelling, Big Data Analytics, Bayesian Inference, System Reliability Modelling, Degradation Modelling, Wind Turbine Maintenance, Battery Management, Smart Grids. Awards: Recipient of three Best Paper Awards from international conferences. He leads the Engineering Asset Management and Systems Engineering Research Group and serves on editorial boards of journals like Information and Machines . Teaching: Contributes to Master's programs in Engineering Asset Management and Engineering Management. Supervises PhD/Master’s students on topics like battery diagnostics, UAV wildfire management, and human-robot collaboration safety. Funding Highlights: Includes grants for battery health estimation, pipeline integrity, and railway asset management. Collaborates with industry partners like the Australasian Centre for Rail Innovation and Energy Pipeline Cooperative Research Centre.