جست‌وجوی استاد و پژوهشگر

Manuel Gil Perezمشاهده پروفایل

دانشیار

• Cybersecurity
• Intrusion Detection Systems
• Trust Management
• +۵ مورد دیگر

Manuel Gil Pérez is an Associate Professor in the Department of Information and Communication Engineering at the University of Murcia, Spain. As member of the Faculty of Informatics, he focuses on cybersecurity research areas including intrusion detection systems, trust management, privacy-preserving data sharing, and security operations in dynamic environments. PhD in Computer Science from University of Murcia (2015) Co-supervised by Dr. Gregorio Martínez Pérez and Dr. Antonio Skarmeta Gómez Active researcher with 70+ scientific publications His research spans multiple cutting-edge domains: 5G/6G network security and trust frameworks Decentralized federated learning systems (Fedstellar platform) Knowledge graph applications for cyber threat intelligence Device behavior fingerprinting for IoT security Healthcare data protection through blockchain and SSI Botnet detection and mitigation strategies Recent publications demonstrate trends in: Antifragile cybersecurity architectures Semantic inference for threat profiling Reputation systems in multi-stakeholder environments Edge computing security solutions Integration of AI/ML in network defense As part of the Intelligent Systems and Telematics research group, he collaborates on major EU-funded projects including 5GBarcelona, 5GZORRO, and SoftFIRE. His work addresses critical challenges in network security, trust management, and decentralized learning systems through practical implementations and benchmarking frameworks like LwHBench.

• Autonomous Systems
• Control Systems
• Robotics
• +۷ مورد دیگر

Professor Antonios Tsourdos is a leading academic in autonomous systems and control engineering at Cranfield University, where he serves as Professor of Autonomous Systems and Control and Director of the Centre for Autonomous and Cyber-physical Systems. His career spans over two decades with significant contributions to guidance, navigation, and control systems for unmanned vehicles. He has held leadership positions including Head of the Autonomous Systems Group (2007), Head of the Centre of Autonomous and Cyber-Physical Systems (2012), and Director of Research for Aerospace, Transport and Manufacturing (2015). Professor Tsourdos earned his academic qualifications from prestigious UK institutions: MEng in Electronic, Control and Systems Engineering from the University of Sheffield (1995), MSc in Systems Engineering from Cardiff University (1996), and PhD in Nonlinear Robust Flight Control Design and Analysis from Cranfield University (1999). His research focuses on autonomy for single and multiple unmanned vehicles, with particular expertise in guidance and control systems, mission planning, multi-agent coordination, and UAS traffic management. Professor Tsourdos has published extensively in top-tier journals, with over 100 publications in the last five years alone, demonstrating his active research program and leadership in the field. His work bridges theoretical control systems with practical aerospace applications. His research portfolio includes significant projects such as CHEDDAR (Communications Hub for Empowering Distributed ClouD Computing), Air Mobility Ecosystem Consortium, Project BLUEPRINT for UTM-enabled drone operations, and the UKRI Trustworthy Autonomous Systems Node in Security. These projects are funded by UKRI, ESA, Horizon Europe, and major industry partners including BAE Systems, Lockheed Martin, Boeing, Airbus, and Thales. Professor Tsourdos has received notable recognition including Fellowship in the Royal Aeronautical Society, membership in the winning team for the UK MoD Grand Challenge (2008), and the IET Innovation Award (2009). He served as chair of the IFAC Technical Committee on Aerospace Control (2017-2023) and has been involved with the BSI Future Flight Standards Hub and AIAA Advanced Aerial Mobility Certification Task Force. He serves on the editorial boards of numerous prestigious journals including Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering; Aerospace Science and Technology; and IEEE Transactions on Aerospace and Electronic Systems. His work has significant industrial impact through collaborations with major aerospace and defense companies.

Zhengdao Wangمشاهده پروفایل

استاد

• Machine Learning
• Cybersecurity
• Communications
• +۲ مورد دیگر

Zhengdao Wang is a Professor in the Department of Cyber Security Engineering at George Mason University (GMU). Previously, he held a professorship at Iowa State University (2002–2023) and served as a Program Director at the National Science Foundation’s Division of Electrical, Communications, and Cyber Systems (2020–2022). He earned a PhD in Electrical Engineering from the University of Minnesota (2002), an MS from the University of Virginia (1999), and a BEng from the University of Science and Technology of China (1996). His research focuses on machine learning, cybersecurity, communications, signal processing, and information theory. Notable contributions include advancements in reinforcement learning architectures, energy-harvesting systems, and secure communication protocols. His work has been recognized with awards such as the IEEE Signal Processing Society Best Magazine Paper Award (2003), IEEE Marconi Paper Prize (2004), and election as an IEEE Fellow (2016). Key research themes include optimizing wireless networks, designing robust learning algorithms, and exploring theoretical limits of communication systems. Recent publications (2018–2021) highlight innovations in reinforcement learning frameworks, secure key generation, and nonconvex optimization techniques. Professional contributions include editorial roles in top IEEE journals and leadership in international conferences. His interdisciplinary expertise bridges theoretical foundations and practical applications in cyber-physical systems.

• High-Performance Computing
• Energy Efficiency
• Parallel Computing
• +۳ مورد دیگر

Ananta Tiwari is a researcher specializing in High-Performance Computing (HPC), energy efficiency, and parallel system optimization. His work focuses on optimizing HPC applications, workload management, and resource allocation strategies to enhance both performance and energy efficiency. Tiwari has collaborated extensively with institutions like the University of Maryland, UC San Diego, and Lawrence Livermore National Laboratory through his research activities. Education: PhD in Computer Science, University of Maryland, College Park (2011) Research Interests: Energy-efficient HPC systems Parallel application auto-tuning frameworks Workload characterization and extrapolation Node-sharing and resource pricing models ARM architecture optimization for HPC Key Contributions: Tiwari's research spans energy optimization techniques for large-scale MPI applications, colocation strategies for HPC workloads, and binary instrumentation tools for program analysis. His work on auto-tuning frameworks and multi-objective modeling with machine learning addresses critical challenges in balancing performance, power consumption, and scalability in modern HPC environments.

• dynamics of neuronal networks
• mechanisms of neuronal information processing
• physics of machine learning
• +۱ مورد دیگر

Prof. Moritz Helias is a Professor and Group Leader of the Theory of Multi-Scale Neuronal Networks at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience department at Forschungszentrum Jülich. His research focuses on the dynamics of neuronal networks, mechanisms of neuronal information processing, and the physics of machine learning, with a particular emphasis on applying statistical physics methods to these areas. His work bridges theoretical neuroscience and machine learning, exploring how principles from statistical physics can elucidate neural computation and improve neural network models. He investigates transient recurrent dynamics, feature learning in deep networks, and renormalization group techniques to link network and neuron-level correlations. Key contributions include studies on criticality in neural networks, the role of synaptic heterogeneity, and the application of field-theoretic methods to analyze network dynamics and learning processes. His research also addresses challenges in computational neuroscience, such as modeling high-frequency neural activity and optimizing reservoir computing architectures.

Simon Musallمشاهده پروفایل

استادیار

• Neural Network Function
• Decision-making
• Multisensory Processing
• +۳ مورد دیگر

Prof. Simon Musall is an Assistant Professor of Neuromodulation at RWTH Aachen University and Head of the in-vivo Neurophysiology Lab at Forschungszentrum Jülich. His research focuses on understanding how multisensory information is integrated in neural networks to guide behavior, particularly in mice performing cognitive tasks. He employs advanced techniques like high-density electrophysiology and functional imaging to study cortical networks, pathway-specific information transfer, and neuromodulation effects. Key research interests include neural network function, decision-making mechanisms, and neuromodulation's role in information processing. His lab develops cutting-edge neurotechnology, such as flexible neural probes and organic neurohybrid systems, to advance in vivo recordings and closed-loop control. Collaborative projects integrate computational neuroscience with experimental approaches to dissect cortical circuit dynamics and their behavioral correlates. Recent work highlights contributions to spike sorting algorithms (UnitRefine), network dynamics modeling (Riemannian multi-scale decomposition), and enzyme-mediated neurohybrid interfaces. His interdisciplinary approach bridges basic neuroscience with translational tools for studying neurodevelopmental and psychiatric conditions.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Chenhui Shaoمشاهده پروفایل

دانشیار

• Smart Manufacturing
• Machine Learning
• Big Data Analytics in Manufacturing
• +۸ مورد دیگر

Chenhui Shao is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. He holds a Ph.D. in Mechanical Engineering from the University of Michigan (2016), with prior degrees in Statistics, Industrial and Operations Engineering, and Automation. His academic roles include serving as an Associate Editor for multiple journals and organizing conferences such as the ASME International Manufacturing Science and Engineering Conference. Education: Ph.D., Mechanical Engineering, University of Michigan, 2016 (Advisors: S. Jack Hu and Jionghua Jin) M.A., Statistics, University of Michigan, 2013 M.S.E., Industrial and Operations Engineering, University of Michigan, 2013 B.E., Automation, University of Science and Technology of China, 2009 Research Interests: Smart Manufacturing Machine Learning in Manufacturing Big Data Analytics and Materials Joining Manufacturing Systems Control and Automation Applications in Dynamics, Energy, Environment, and Healthcare Recent Research Trends: His work focuses on federated learning for distributed manufacturing, physics-informed machine learning for process optimization, and sensor fusion for quality prediction. Key areas include ultrasonic metal welding, additive manufacturing, and two-photon lithography. Awards: 2022 Dean’s Award for Excellence in Research 2021 ASME Chao and Trigger Young Manufacturing Engineer Award 2020 NSF CAREER Award Multiple teaching excellence recognitions from UIUC students Advising & Grants: Advised over 10 Ph.D. and Master’s students, with notable projects funded by NSF, NCSA, and industry partnerships like CRRC Corporation. Collaborates on interdisciplinary initiatives such as the Center for Advanced Research in Drying (CARD) and the NSF nanoMFG Node. Labs & Teams: Leads the Automation and Digital Manufacturing Lab (ADML), focusing on machine learning, control systems, and sustainable manufacturing. Active in national and international conferences, organizing sessions on topics like additive manufacturing and smart manufacturing networks.

• Civil Engineering
• Materials Science
• Construction Technology
• +۵ مورد دیگر

Dr. Ciaran McNally is an Associate Professor in the School of Civil Engineering at University College Dublin (UCD). He holds a PhD in Civil Engineering from UCD (2001) and a Professional Diploma in Teaching & Learning (2015). His research focuses on advanced materials, including concrete durability, asphalt technology, fiber-reinforced polymers (FRP), and digital design methods like 3D printing and BIM. He has secured over €2 million in research funding, leading EU projects such as the Marie Curie Initial Training Network TEAM (€3.2M). Education: BE, PhD (Civil Engineering, UCD); Prof Dip University Teaching & Learning (UCD) Affiliations: Irish Concrete Society (Treasurer), Engineering Graduates Association Committees: College Executive Board, Safety Committee (School/College), UCD Covid-19 Consultative Committee His research interests span low-carbon concrete, eco-engineering for marine structures, and infrastructure asset management. Notable contributions include studies on reclaimed asphalt in pavements, bio-inspired optimization, and durability of GGBS blends. He coordinates modules like Advanced Materials and Group Design Projects, emphasizing practical engineering and sustainability. Recent articles highlight innovations in 3D-printed concrete, low-carbon materials, and steel stud behavior. McNally’s work bridges academic research with industry applications, promoting sustainable construction practices and lifecycle thinking.

• Plastic Surgery
• Reconstructive Surgery
• Microsurgery
• +۳ مورد دیگر

Dr. David A Daar is an Assistant Professor of Clinical Surgery at the University of Southern California (USC), specializing in reconstructive plastic surgery and microsurgery. He serves as co-director of the USC Lymphedema Center, focusing on advanced microsurgical techniques like lymphovenous bypass and lymph node transfer. Dr. Daar completed his undergraduate studies at USC, earned his medical degree at UC Irvine (with emphasis on Latino healthcare disparities), and underwent plastic surgery residency at NYU Langone Health, followed by a microsurgery fellowship at USC. His clinical expertise includes breast reconstruction (implant-based and autologous techniques like DIEP flap), lower extremity reconstruction, head and neck reconstruction, and lymphedema management. His research interests span microsurgical outcomes, flap survival, and leveraging technology such as 3D printing and ultrasound imaging. He has published over 60 peer-reviewed articles, contributing to topics like postoperative complication prediction, surgical technique optimization, and disparities in reconstructive care. Dr. Daar emphasizes patient-centered care, believing that listening to patients’ goals enhances surgical success. His work bridges clinical practice with innovation, aiming to advance reconstructive surgery while addressing systemic healthcare challenges. Education: Bachelor’s degree: University of Southern California (USC) Medical Degree: UC Irvine School of Medicine (PRIME-LC Program) Training: Plastic Surgery Residency: NYU Langone Health’s Hansjörg Wyss Department of Plastic Surgery Microsurgery Fellowship: USC Labs/Teams: Co-director of the USC Lymphedema Center, part of Keck Medicine of USC.

Liang-Liang Xieمشاهده پروفایل

استاد

• Network Information Theory
• Wireless Relay Networks
• Adaptive Control
• +۶ مورد دیگر

Liang-Liang Xie is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on network information theory, wireless relay networks, adaptive control systems, and systems biology. He holds a B.S. in Mathematics from Shandong University (1995) and a Ph.D. in Control Theory from the Chinese Academy of Sciences (1999). He completed postdoctoral research at Linköping University (Sweden) and the University of Illinois at Urbana-Champaign (USA). His work spans theoretical advancements in wireless communication protocols, including relay frameworks, compression techniques, and network coding. Recent articles emphasize omnidirectional relaying, throughput optimization in ad hoc networks, and information-theoretic scaling laws. He has advised over 25 graduate students, many co-supervised with senior faculty members. Current students include Shuyue Wang and Yuxi Zhang. Teaching responsibilities include courses in signals and systems, probability theory, and network information theory. His research has contributed to foundational understanding of transport capacity, channel coding, and multi-user wireless systems.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +۴ مورد دیگر

Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework

• Graph Data Management
• RDF and Semantic Web
• Database Theory
• +۳ مورد دیگر

Maxime Jakubowski is a PostDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Databases and Artificial Intelligence research group. Based in Room HA0320 at Favoritenstrasse 9, he can be contacted at maxime.jakubowski@tuwien.ac.at and maintains an ORCID profile (0000-0002-7420-1337). His research centers on graph data management within the Semantic Web ecosystem, specializing in RDF validation through shape constraint languages like SHACL and ShEx. He investigates formal foundations, expressiveness boundaries, and practical implementations including SQL compilation and neighborhood-based graph description. Current projects include FRESH (2021–2026), KtoAPP (2018–2025), and TARGET (2024–2028), focusing on graph data theory and implementation. Recent publications (2021–2025) demonstrate consistent contributions to RDF validation standards, with 14 articles addressing shape language formalization, compilation techniques, and provenance tracking. His work bridges theoretical database concepts with industrial applications in knowledge graph validation. Dr. Jakubowski supervises bachelor theses and teaches courses including Management of Graph Data (192.161) and Project in Computer Science. His research collaborations span international projects and the Dagstuhl Seminar 24102 on Shapes in Graph Data. He is an active member of the Databases and Artificial Intelligence research group, contributing to TU Wien's leadership in graph data management and Semantic Web technologies through both theoretical research and practical tool development.

Iris Nagtegaalمشاهده پروفایل

استاد

• Gastrointestinal Pathology
• Cancer Staging and Classification
• Structured Reporting
• +۳ مورد دیگر

Iris Nagtegaal is a Professor of Gastrointestinal Pathology at Radboud University and Radboud University Medical Center in Nijmegen, Netherlands. She holds key roles including Expert Pathologist for the National Bowel Cancer Screening Program and Consultant Pathologist in Gastrointestinal Pathology. Her work integrates pathology, epidemiology, and molecular biology to improve colorectal cancer outcomes, including global standards for surgical assessment and TNM staging. Education: PhD (cum laude) in Gastrointestinal Pathology from Leiden University (2002) Fellowships at institutions including Queen Mary University (London), Baylor College of Medicine (Houston), and the Netherlands Cancer Institute Research Interests: Focus on gastrointestinal pathology, structured reporting in diagnostics, cancer staging optimization, and molecular mechanisms of colorectal cancer metastasis. Her work emphasizes translating research into clinical practice, such as standardized reporting systems and national e-learning modules for pathology. Awards: Rising Star Award (United European Gastroenterology Federations, 2012) AstraZeneca Research Award for Best Gastrointestinal PhD Thesis (2003) Editor of colorectal cancer chapter in WHO Tumor Classification Grants & Teams: Leads research groups combining molecular and epidemiological approaches. Her work includes collaborative projects linking national pathology databases to cancer registries, enhancing population-level analyses. No explicitly listed grants are provided in the texts, but her roles imply significant institutional and collaborative support. Labs/Teams: Active in the Department of Pathology at Radboud University Medical Center, focusing on gastrointestinal malignancies and translational research.

Marios Kogiasمشاهده پروفایل

استادیار

• Operating Systems
• Networking
• Distributed Systems
• +۴ مورد دیگر

Marios Kogias is an Assistant Professor in the Department of Computing at Imperial College London, conducting research at the intersection of operating systems, networking, and distributed systems with a focus on tail-tolerance, fault-tolerance, and confidentiality using emerging datacenter hardware. He holds a PhD in Computer Science from EPFL (2020) supervised by Ed Bugnion, where his thesis won the Dennis M. Ritchie Award and received an Honourable Mention for the Eurosys Roger Needham PhD Award. His undergraduate degree in Electrical and Computer Engineering is from NTUA. His research spans operating systems, networking, distributed systems, cloud computing, and confidential computing, with significant contributions to microsecond-scale computing, serverless architectures, and eBPF applications. Current work emphasizes tail-latency optimization in datacenter environments and confidential computing security. Recent publications reveal strong trends in microsecond-scale systems, confidential computing, and serverless workflows, demonstrating expertise in leveraging hardware innovations for performance and security guarantees across distributed environments. His scientific awards include: Dennis M. Ritchie Award (2021) Honourable Mention for the Eurosys Roger Needham PhD Award (2021) IBM Fellowship Microsoft Swiss JRC Fellowship Distinguished Artifact Award (ASPLOS 2021) Best Student Paper Award (Eurosys 2020) Dr. Kogias actively mentors PhD students including Konstantinos Prasopoulos (co-advised with Ed Bugnion), Scofield Liu, Farzad Mohammadi, and Adel Sefiane. He teaches Networked Systems, Computer Networks and Distributed Systems, and Object-Oriented Design at Imperial, supported by fellowships from IBM and Microsoft during his doctoral research. His collaborative work spans EPFL, Microsoft Research Cambridge, and Imperial College London, focusing on systems architecture for next-generation datacenter applications.