جست‌وجوی استاد و پژوهشگر

• Web Security
• Identity Management
• System Security
• +۳ مورد دیگر

Dr. Wanpeng Li is a Lecturer in Cyber Security within the Department of Computer Science at the University of Liverpool. Prior to this role, he held lecturer positions at the University of Aberdeen and Manchester Metropolitan University, and worked as a postdoctoral researcher at City, University of London. His research focuses on critical areas in cyber security including web security, identity management, authentication mechanisms, and malware detection using machine learning. Research Trends: His recent publications span both cyber security and mathematical modeling domains. Key security themes include automated vulnerability detection, federated learning attacks, and privacy-preserving protocols for vehicular networks and IIoT systems. Parallel studies in grey system models explore energy consumption forecasting and environmental impact analysis using fractional calculus and neural network integrations. Advising: Dr. Li actively accepts PhD students in cyber security-related fields.

Sebastian Angelمشاهده پروفایل

دانشیار

• Systems
• Security
• Privacy
• +۴ مورد دیگر

Sebastian Angel is an Associate Professor and Chair of Undergraduate Curriculum in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in systems, security, privacy, and networking, with a focus on privacy-preserving systems, accountability in online services, consistency in distributed systems, and next-generation operating systems. Research Areas: Systems, Security, Privacy, Networking, Distributed Systems, Operating Systems Labs & Teams: Distributed Systems Laboratory, Security and Privacy Laboratory, Warren Center for Network and Data Sciences Education: Ph.D. in Computer Science from the University of Texas at Austin (2018), ACM SIGOPS Dennis M. Ritchie Dissertation Award, Bert Kay Best Dissertation Award Recent Publications (2025–2022) span serverless computing, zero-knowledge proofs, distributed transactions, privacy-preserving ad tech, and verifiable execution. His 2025 work includes serverless workflow optimization and structural logic verification, while 2024 focuses on caching frameworks and stateful serverless. Earlier 2023–2022 projects include secure federated learning, confidential cloud services, and private information retrieval. Scientific Awards: NSF CAREER Award (2021) JPMorgan Faculty Award (2021) ACM SIGOPS Dennis M. Ritchie Dissertation Award (2018) Bert Kay Best Dissertation Award (2018) ACM SIGMOD Research Highlights Award (2024) VLDB 2024 Best Paper Award Nominee Advising: Current PhD Students: Elizabeth Margolin, Jess Woods, Yuxuan Zhang Current Masters Students: Felix Adena, Sydnie Shea Cohen Alumni: Eleftherios Ioannidis (2025), Yiping Ma (2025), Haoran Zhang (2024), Ke Zhong (2024), Selin Butun (2025), Martin Sander (2025), Seungmin Han (2024), Andrew Beams (2022), Yifeng Mao (2021), Varad Deshpande (2020) Current Courses: CIS 4510/5510: Computer and Network Security (Fall 2025). He also organizes conferences like S&P, OSDI, SOSP, PETS, and EuroSys.

• Federated Machine Learning
• Data Privacy
• Healthcare Applications
• +۳ مورد دیگر

Dominik Fay is a Researcher at the Division of Decision and Control Systems within Kungliga Tekniska Högskolan (KTH). His work focuses on federated machine learning, data privacy, and their applications in healthcare. He is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) through their industrial PhD program in collaboration with Elekta, a healthcare technology company. Education: MSc in Computer Science from KTH (2019) BSc in Applied Computer Science from Heidelberg University (2017) His research primarily addresses privacy challenges in distributed machine learning environments. Key contributions include methods for locally differentially private federated learning, dynamic privacy allocation, and privacy amplification techniques tailored for healthcare applications. His work also explores the intersection of machine learning and medical imaging, particularly in segmentation tasks requiring stringent privacy guarantees. Dominik's publications reflect a strong emphasis on data privacy, federated learning, and healthcare applications. Recent articles focus on correlated noise in federated learning, privacy allocation for composite objectives, and privacy-preserving medical image segmentation. Earlier works extend into smart grid privacy, metabolomics data analysis, and scalable privacy-preserving algorithms. Grants and Collaborations: Supported by WASP Industrial PhD Program Collaboration with Elekta, a leader in healthcare technology He is part of Mikael Johansson's research group, which specializes in decision and control systems, and his work aligns with broader efforts in privacy-preserving AI and machine learning for sensitive healthcare data.

Raymond Chengمشاهده پروفایل

پژوهشگر

• Security
• Privacy
• Distributed Systems
• +۵ مورد دیگر

Raymond Cheng is a Researcher and blockchain lecturer with a focus on security, privacy, and distributed systems. He earned his Ph.D. in Computer Science from the University of Washington and holds degrees in Physics, Electrical Engineering, and Computer Science from MIT. His research spans blockchain scalability, privacy-preserving protocols, and decentralized infrastructure. Key projects include Kariba Labs , MASQ Foundation , and Open Source Observer , where he works on tools for public goods funding, secure smart contracts, and open-source analytics. Raymond's publications highlight advancements in blockchain-based databases (FalconDB), confidential smart contracts (Ekiden), and privacy-preserving messaging (Talek). His work often intersects with internet freedom and secure, scalable systems. He co-founded multiple organizations, including the Decentralization Institute and Public Goods Foundation , and has been featured in Forbes , Wired , and MIT Technology Review for his contributions to privacy and blockchain innovation.

• Edge-Cloud Computing
• Resource Optimization
• Applied Machine Learning
• +۵ مورد دیگر

Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He holds a PhD in Computer Science from the University of Bradford (2010), and M.Eng/B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology. His research specializes in edge-cloud computing, federated learning, and AI-driven optimization for networks and IoT systems. Research Interests: Hu's work spans resource optimization, applied machine learning (particularly in distributed settings), network security, blockchain integration, and intelligent systems for electric vehicles and Industry 4.0. His recent projects focus on federated edge AI, 6G-enabled industrial IoT, and real-time federated learning via hardware-algorithm co-design. Publications: His 150+ papers emphasize federated learning, edge computing, and reinforcement learning applications. Recent works (2020–2025) show a strong trend toward optimizing AI at the network edge, with themes like digital twins, blockchain security, and EV-integrated systems dominating. Awards & Recognition: Best Paper Awards: IEEE SOSE'16, IUCC'14 Outstanding Service & Leadership Awards for IEEE conferences Top 4% contributor to EPSRC Peer Review Fellow of the Higher Education Academy (HEA) Grants & Projects: Secured €4.7M+ funding from EU Horizon, EPSRC, and Royal Society for projects including: SAILING (Secure AI for Smart Internet-of-Energy, €3.6M) REFINE (Real-time Air Quality Monitoring with UAVs, €897K) SustainAIRA6G (Energy-Efficient AI for 6G Networks, £118K) Advising: Supervised 12 PhD students to completion; currently mentoring 7 students in federated learning, edge computing, and AIoT.

Robert Hsuمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Cloud Computing
• Big Data Analytics
• +۴ مورد دیگر

Robert Hsu is Chair Professor and Dean of the College of Information and Electrical Engineering at Asia University, Taiwan, and holds concurrent appointments as Professor at National Chung Cheng University. He serves as President of the Taiwan Association of Cloud Computing and Research Consultant at China Medical University Hospital. His research spans parallel and distributed computing , cloud and edge systems , AI , and medical applications , with over 350 publications in top venues like IEEE TPDS, IEEE TSC, and ACM TOMM. Editor-in-Chief of International Journal of Grid and High Performance Computing Founding Editor-in-Chief of International Journal of Big Data Intelligence Advisory roles in 10+ journals including IEEE Transactions on Cloud Computing His research focuses on cloud-edge collaboration , AI for medical imaging , IoT security , and big data analytics . Key projects include federated learning frameworks for multi-institutional healthcare, optimization techniques for UAV-assisted MEC, and blockchain-based security solutions. His 15 most recent articles highlight advancements in malware detection, resource orchestration in edge environments, and lightweight AI models for retail and rural applications. Scientific accolades include: Stanford University's World's Top 2% Scientists (2020-2023) Best Paper Awards at IEEE ICEIB 2023 and SysCom 2021 Over 25 grants from Ministry of Science and Technology and Ministry of Education Leadership roles as IEEE TCCLD Chair and Steering Committee member He has supervised 30+ PhD/Master's students , including Shih-Chang Chen (2010), Tai-Lung Chen (2010), and Nithin Melala Eshwarappa (2020). His laboratory focuses on edge computing , cloud systems , and AI-driven solutions for healthcare and urban infrastructure.

Ahmet Coskunمشاهده پروفایل

استادیار

• Spatial Biology
• Single Cell Biotechnology
• Spatial Omics
• +۹ مورد دیگر

Dr. Ahmet Coskun is an Assistant Professor of Biomedical Engineering at Georgia Institute of Technology and Emory University, where he holds the Bernie-Marcus Early-Career Professorship. He directs the Single Cell Biotechnology and Spatial Omics Laboratory, an interdisciplinary program focused on multiparameter imaging of single cells within their spatial context. His work bridges the fields of bioengineering, computational biology, and systems biology to address fundamental challenges in cancers, immunology, and pediatric diseases. Dr. Coskun received his PhD from the University of California, Los Angeles (UCLA) working with Aydogan Ozcan. He completed postdoctoral training at the California Institute of Technology with Long Cai and served as an Instructor at Stanford University with Garry Nolan. His educational background has provided him with a strong foundation in both engineering principles and biological systems. Dr. Coskun's research lies at the nexus of multiplex bioimaging, microfluidic biodynamics, and big data biocomputation. His laboratory pursues three main research thrusts: spatial genomics (using seqFISH and correlation FISH methods), spatial proteomics (using CODEX technology combined with super-resolution imaging), and spatial metabolomics (using computational and isotope barcoding approaches with MIBI). His team develops machine learning algorithms to analyze the resulting high-dimensional imaging datasets, creating image-based 'omic technologies to reveal the spatial nature of biological systems. Their work has significant implications for understanding therapeutic response variability and cellular organization in health and disease. NSF CAREER Award 2024 NIH R35 MIRA Award 2023 BMES-CMBE Rising Star Award 2023 American Lung Association Innovation Award 2022 Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award NIH K25 Award Burroughs Wellcome Fund CASI Award Dr. Coskun leads an interdisciplinary research team comprising PhD students from Bioengineering, Electrical and Computer Engineering, Mechanical Engineering, and Biomedical Engineering programs. His lab has been supported by numerous federal and private grants, including funding from multiple NIH institutes (NIA, NIAID, NCI, NIDCR, OD, and ORIP), Wellcome LEAP, Burroughs Wellcome Fund, NSF CMaT, American Cancer Society IRG, Multi-cellular engineered living systems (M-CELS), and Regenerative Medicine Center. In addition to his research, Dr. Coskun leads outreach programs through BioCrowd Studio, which engages K12 and undergraduate students through interactive virtual media and distributed biokits. The Single Cell Biotechnology and Spatial Omics Laboratory is strategically positioned at the forefront of spatial biology research. The lab benefits from advanced technologies including super-resolution microscopy, imaging mass spectrometry, combinatorial molecular barcoding, and machine learning to enhance the information capacity of cellular data. The team's innovative approaches to spatial multi-omics profiling have positioned them as leaders in understanding cellular heterogeneity and organization within tissues.

Aryan Pasikhaniمشاهده پروفایل

مدرس

• Intrusion Detection and Prevention Systems
• Reinforcement Learning
• Machine Learning
• +۹ مورد دیگر

Dr. Aryan Pasikhani is a Lecturer in Cybersecurity at the University of Sheffield , affiliated with the Security of Advanced Systems research group . Holding a PhD from the same institution, his research focuses on Intrusion Detection Systems , Reinforcement Learning , and Quantum Computing applications in security. Specializes in securing Embedded Systems and Internet of Things architectures Active in Adversarial Machine Learning and Privacy-Preserving Technologies Peer-reviewer for IEEE Internet of Things Journal and IEEE Transactions on Industrial Informatics His recent work explores 6LoWPAN security through reinforcement learning and Federated Learning frameworks for privacy-preserving data analysis. Awards include Fellow of the Higher Education Academy . Current grants involve real-time ransomware detection (CipherGrit) and automated threat modeling for AI systems. He supervises four PhD students and teaches Security of Control and Embedded Systems .

Salvatore Ramponeمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Salvatore Rampone is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of Università degli Studi del Sannio (Unisannio). His research focuses on Machine Learning , Internet of Things (IoT) , and Cybersecurity , with applications in intrusion detection, bioinformatics, and financial systems. Research Interests : Privacy-preserving Machine Learning Hybrid Cloud-IoT Architectures Genetic Programming Neural Network Applications Data Processing in Distributed Environments Recent Publication Trends : His work spans federated learning for IoT security ( 2025 ), DNA splicing site prediction ( 2020 ), meningitis classification ( 2019 ), RFID-based traceability systems ( 2015 ), and geological data analysis using neural networks ( 1995-1996 ). Key technologies include Apache Spark , MLlib , and Wavelet Transform . Teaching Activities : He teaches courses like Applied Machine Learning and Principles of Computer Sciences across multiple degree programs including Statistical and Actuarial Sciences and Economics and Management .

• Dependability Engineering
• Performability Analysis
• Cloud Computing
• +۷ مورد دیگر

Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.

Ruozhou Yuمشاهده پروفایل

استادیار

• Computer Networks
• Distributed Systems
• Cybersecurity
• +۱۰ مورد دیگر

Ruozhou Yu is an Assistant Professor in the Department of Computer Science and a Courtesy Assistant Professor in the Department of Electrical and Computer Engineering at NC State University. His research focuses on computer networks, distributed systems, and cybersecurity , with applications to quantum networking, blockchain, IoT, cloud/edge computing, and machine learning . He earned his PhD in Computer Science from Arizona State University (2019) and his BS from Beijing University of Posts and Telecommunications (2013). Education: PhD, Computer Science, Arizona State University, 2019 BS, Computer Science, Beijing University of Posts and Telecommunications, 2013 Yu's research spans quantum internet (high-fidelity entanglement distribution, satellite-assisted quantum networks), blockchain technologies (payment channel networks, smart contracts, layer-2 security), and edge computing (resource provisioning, SLA verification, market design). He also explores machine learning in distributed systems (LLM fine-tuning on graphs) and network security (data delay attacks, Byzantine-robust federated learning). His recent publications (2024–2025) emphasize quantum networking (LACE, QuESat), edge computing SLAs (VeriEdge, WolfPack), and blockchain security (Thor, Fence). Articles like AdaOrb (PerCom 2025) and Physics-Informed Scheduling (RTAS 2025) highlight cross-domain innovations. Awards & Recognition: NSF CAREER Award (2021) IEEE TNSE Excellent Editor Award (2024) IEEE INFOCOM Distinguished TPC Member (2024, 2022, 2020) Yu supervises PhD and Master's students in quantum networking (Huayue Gu), blockchain (Xiaojian Wang), and edge computing (Zhouyu Li). He serves as Associate Editor for IEEE Transactions on Network Science and Engineering and Area Editor for Elsevier Computer Networks .

Aruna Prasad Seneviratneمشاهده پروفایل

استاد

• Cybersecurity and privacy
• Artificial intelligence
• Communications engineering
• +۵ مورد دیگر

Professor Aruna Prasad Seneviratne serves as the Foundation Professor of Telecommunications at the University of New South Wales (Australia), where he holds the prestigious Mahanakorn Chair of Telecommunications. He is currently the Research Director for the Cyber Physical Systems Research Program within Data61, following the merger of NICTA with CSIRO. Previously, he directed the Australian Technology Park Laboratory of NICTA and led their Networked Systems research activities. Professor Seneviratne's research focuses on physical analytics - technologies enabling applications to interact intelligently and securely with their environment in real time. His recent work includes behavioral biometrics, wearable device optimization, and IoT system verification. His extensive publication record spans cybersecurity, artificial intelligence, communications engineering, and mobile technologies, with particular emphasis on integrated communications and sensing systems. His scholarly contributions include over 180 refereed technical papers and book chapters, reflecting his leadership in telecommunications and networked systems research. Professor Seneviratne's work demonstrates a consistent trajectory toward developing practical solutions for next-generation digital services and security frameworks. His scientific recognition includes prestigious fellowships at British Telecom and Telecom Australia Research Labs, underscoring his industry impact alongside academic contributions. Professor Seneviratne has supervised 30 PhD dissertations throughout his career, mentoring the next generation of telecommunications researchers. His leadership extends to directing major research initiatives at NICTA and Data61, where he has guided the development of new technologies for establishing trust, energy-efficient content storage, search, and distribution within digital economies. His laboratory work centers on the Cyber Physical Systems Research Program at Data61, where his team develops innovative approaches to secure and intelligent interaction between digital systems and physical environments.

Daniel Leidnerمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۵ مورد دیگر

Daniel Leidner is a Cooperation Professor at the University of Bremen and a researcher at the German Aerospace Center (DLR) where he has been contributing to the Institute of Robotics and Mechatronics since 2011. He earned his doctorate in Artificial Intelligence and Robotics from the University of Bremen in 2017. Since 2017, he has led the Semantic Planning Group and the Fault-Tolerant Autonomy Architectures group at DLR, focusing on advanced task planning for autonomous robotic systems and enhancing the reliability of robotic operations in dynamic environments. Leidner's research interests span multiple areas in robotics and artificial intelligence. His work emphasizes developing robust and resilient robotic systems capable of autonomous operation in complex environments. He specializes in creating systems that can not only handle predictable scenarios but also flexibly respond to unforeseen events. His ERC Starting Grant project RECOVER.ME aims to equip robots with metacognitive abilities to autonomously manage hardware malfunctions by integrating formal reasoning with Vision-Language Models. This innovative approach enhances the resilience and efficiency of space robots, reducing the need for manual intervention during missions and leveraging insights from cognitive psychology for improved problem-solving capabilities. Leidner's research portfolio includes significant projects such as RECOVER.ME, FUTURO, EASE, OPERA, Smile2gether, Surface Avatar, and CoViPa. His publications demonstrate a strong focus on autonomous task planning, human-robot interaction, fault tolerance, and metacognitive capabilities in robotic systems. Recent publications highlight advancements in space teleoperation, assistive robotics, and cognitive reasoning for resilient robotic systems. ERC Starting Grant (2024) for project RECOVER.ME Georges Giralt PhD Award (Best European PhD Thesis in Robotics) Helmholtz Doctoral Prize MIT Technology Review Innovator under 35 Award Leidner has served as an advisor to the German Federal Government from October 2023 to July 2024, where he played a crucial role in developing a national strategy for AI-based robotics. His leadership in the Semantic Planning Group and Fault-Tolerant Autonomy Architectures group at DLR demonstrates his significant contributions to advancing robotic capabilities in challenging environments. His work bridges theoretical advances in cognitive robotics with practical applications in space exploration, healthcare, and industrial automation.

• Internet of Things (IoT)
• Fog Computing
• Edge Computing
• +۱۶ مورد دیگر

Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.

Vlad-Costin Andreiمشاهده پروفایل

پژوهشگر

• Integrated Sensing and Communications (ISAC)
• Digital Twinning
• Neuromorphic Computing
• +۵ مورد دیگر

Vlad-Costin Andrei is a Researcher at the Chair of Theoretical Information Technology , Technical University of Munich (TUM), specializing in wireless communication systems and digital twinning. He joined the ACES Lab (TUM's Chair of Theoretical Information Technology) in late 2021 after 3.5 years in the aerospace and defense industry. Research Focus: Joint Communications and Sensing (6G), Neuromorphic PHY Layer, Digital Twins, MIMO-OFDM Resilience Projects: 6G-life, 6G Future Lab Affiliation: ACES Lab, TUM His work bridges theoretical foundations with practical implementations, including demonstrations of digital twinning platforms and sensing-assisted receivers. Recent publications emphasize anti-jamming frameworks, federated learning over wireless networks, and trajectory optimization for UAV-enabled ISAC systems. Scientific Awards: Best Paper Award, IEEE Symposium on Joint Communications and Sensing (2023) His research is supported by third-party grants such as BMBF's 6G-life, DFG's Gottfried Wilhelm Leibniz Prize, and multiple collaborative projects.