جست‌وجوی استاد و پژوهشگر

Jinwook Leeمشاهده پروفایل

استادیار

• Optimization
• Risk Modeling
• Stochastic Optimization
• +۱۷ مورد دیگر

Jinwook Lee is an Assistant Professor in the Decision Sciences and MIS department at LeBow College of Business, Drexel University. His research focuses on optimization, risk modeling, and artificial intelligence systems, particularly applied to blockchain, supply chain, and financial decision-making. He is actively involved in funded research, including an NSF SBIR grant on blockchain-based supply chain systems. Research Interests: His expertise spans Stochastic Optimization , Mathematical Programming , Blockchain , Operations Research , and Data Privacy . He develops models for uncertainty quantification, risk assessment, and fairness in AI systems using advanced mathematical tools such as pseudo-Boolean functions and multivariate risk measures (e.g., MVaR, MCVaR). His work bridges theoretical rigor with practical applications in finance, supply chains, and forecasting. The recent articles reflect a strong trend toward integrating blockchain and AI with privacy-preserving forecasting , equity metrics , and secure supply chains . His publications span top journals in operations research and management science, indicating a consistent and impactful research trajectory in quantitative decision sciences. Scientific Awards: NSF SBIR Grant (Award #2112078), Co-PI, Mar 2022 Advising and Grants: While direct student names are not listed, his collaborative work with Matthew J. Schneider and András Prékopa suggests active mentorship and research collaboration. He leads a funded project on blockchain-based tracking and distribution with $255,786 from the National Science Foundation, indicating strong grant-writing capability and research leadership. Labs and Research Teams: Though no formal lab name is provided, his research activities suggest leadership in a group focused on blockchain, AI, and risk modeling, likely involving interdisciplinary collaboration across business analytics, computer science, and operations research.

• Distributed Systems
• Data Management
• Dependability
• +۴ مورد دیگر

Rui Oliveira is an Associate Professor at Universidade do Minho in Portugal, affiliated with the Distributed Systems Group. His research focuses on dependable distributed systems, fault-tolerant database systems, and large-scale distributed systems. He teaches Distributed Systems across undergraduate, master's, and doctoral programs. Research Interests His work spans epidemic communication protocols, data management in cloud environments, and high-performance transactional middleware. Recent contributions include frameworks for privacy-preserving machine learning (SOTERIA), distributed tracing (CAT), and fault-diagnosis tools for I/O behavior analysis. Key Contributions Developed the Loom disaggregated database system, the TADA distributed agreement toolkit, and the DATAFLASKS epidemic storage substrate. His work emphasizes practical implementations of scalable and resilient distributed systems. Labs/Teams Affiliated with HASLab (Hardware and Software Systems Laboratory), focusing on distributed systems research. Collaborates with industry on cloud computing and cybersecurity challenges.

Ala Sarah Alaqraمشاهده پروفایل

دانشیار

• Cybersecurity
• Privacy Engineering
• Human-Computer Interaction (HCI)
• +۷ مورد دیگر

Ala Sarah Alaqra is an Associate Professor (Docent) in Information Systems Research at Karlstad University, Sweden. She holds dual roles as a researcher and educator, contributing significantly to the fields of privacy engineering, cybersecurity, and human-computer interaction. Her work bridges technical innovation with user-centric design principles, addressing challenges in healthcare systems, cloud computing, and digital transformation. **Research Projects:** INNOPATH (2025–current): Innovating policy frameworks for innovation commons. INCREDILAB (2024–current): Enhancing climate resilience through digital education. Cross Border Cyber Capacity (CBCC) (2023–current): Strengthening cybersecurity collaboration between Sweden and Norway. PAPAYA (2018–2020): Developing privacy-preserving analytics platforms for cloud services under Horizon2020. PRISMACLOUD (2015–2018): Securing privacy in cloud services funded by Horizon2020. **Teaching:** Responsible for 15+ courses across bachelor’s, master’s, and thesis levels, including: Bachelor’s courses: Interaction Design, Web Analytics, User-Centered Prototyping. Master’s courses: AI in Law, AR/VR Research, Experimental Multimedia Methods. Thesis supervision in information systems and technology. **Research Focus:** Prioritizes user-centric solutions for privacy challenges in cloud computing, eHealth systems, and crisis management. Her work emphasizes practical implementations of cryptographic technologies, digital well-being during pandemics, and stakeholder collaboration in tech adoption. **Grants & Collaborations:** Secured funding from VINNOVA, EU Horizon2020, and Interreg initiatives. Collaborates with municipalities, healthcare providers, and tech firms to translate research into actionable solutions. **Labs/Teams:** Engaged in the INCREDILAB initiative and cross-border cybersecurity projects. Leads interdisciplinary teams addressing privacy-technology interface challenges.

Jusak Jusakمشاهده پروفایل

مدرس ارشد

• Internet of Things (IoT)
• Telehealthcare
• Blockchain Technology
• +۳ مورد دیگر

Dr. Jusak Jusak is a Senior Lecturer in Internet of Things (IoT) at the School of Science and Technology, James Cook University Singapore. He holds a PhD in Electrical Engineering from RMIT University and a BEng in Electrical Engineering from Brawijaya University, Indonesia. His research focuses on IoT applications in telehealthcare, blockchain for healthcare IoT, and lightweight machine learning for constrained devices. Dr. Jusak has held academic positions at Dinamika University (Surabaya, Indonesia) and Massey University (New Zealand). He has secured grants including a Cross-Campus Collaboration Grant for blockchain development and a Teaching Innovation Grant for the VetheRBlocks project. He is a Guest Editor for the Electronics journal's special issue on wireless communication security. His research interests include IoT device development for medical signal analysis, secure cloud-based healthcare systems, and hybrid-channel communication for rural healthcare. He has published over 20 peer-reviewed articles and received awards such as the ICITAS 2018 Best Paper Award and a 2022 Futuristic Technology Award for IoT-based water quality monitoring. Dr. Jusak's work emphasizes practical IoT solutions for healthcare challenges, integrating machine learning and blockchain to enhance data security and accessibility in distributed networks.

• Responsible AI
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Mr. Boming Xia is a Research Fellow at the Responsible AI Research Centre (RAIR) and the Australian Institute for Machine Learning (AIML), University of Adelaide. He holds a position within the Faculty of Sciences, Engineering and Technology. His research focuses on advancing responsible and safe AI through a software engineering lens, particularly exploring the integration of system-level practices with machine learning approaches. His work emphasizes ethical AI development, including frameworks for risk assessment, ESG (Environmental, Social, Governance) integration, and blockchain-based accountability mechanisms. Key areas of interest include AI safety, privacy protection in generative AI, and the design of transparent software supply chains using tools like SBOM (Software Bill of Materials). Xia's contributions span empirical studies on SBOM adoption, AI system evaluation frameworks, and tools like CDCAT for cross-document entity and event coreference. His research bridges theoretical AI foundations with practical software engineering methodologies to ensure AI systems are both innovative and ethically grounded.

Jorge Bernal Bernabéمشاهده پروفایل

دانشیار

• Cybersecurity
• Privacy Management
• Trust Management
• +۷ مورد دیگر

Jorge Bernal Bernabé is an Associate Professor at the Department of Communications and Information Engineering (DIIC) of the University of Murcia (UMU). He focuses on security, trust, and privacy management in distributed systems , particularly in Internet of Things (IoT), Software-Defined Networking (SDN), Network Function Virtualization (NFV) , and blockchain technologies . His work spans European projects like Resilmesh, RIGOUROUS, ERATOSTHENES, and INSPIRE-5Gplus. His research emphasizes Automated security policy enforcement Privacy-preserving credentials Federated learning for anomaly detection Network slicing for 6G security Cyber situational awareness Scientific awards include Best PhD Thesis Award (2015) AXA Postdoctoral Scholarship (2019) ANECA Associate Professor accreditation (2020) Best Paper Award (2009) He serves on editorial boards for journals like Frontiers in Blockchain (Blockchains Security and Privacy section), Electronics (Networks Section), and Security and Communication Networks . As a program committee member, he co-chaired SecSoft 2025 and organized workshops like ECoSP 2018.

Zahidul Islamمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Cyber Security
• +۴ مورد دیگر

Zahidul Islam is a Professor of Computer Science and Associate Dean (Research) at Charles Sturt University's Faculty of Business, Justice and Behavioural Sciences (FOBJBS). With a PhD in Computer Science from the University of Newcastle and over 150 peer-reviewed publications, his research focuses on data mining, cybersecurity, and AI applications across health, agriculture, and social systems. BSc in Civil Engineering (RUET, Bangladesh) Graduate Diploma in Information Science (UNSW, Australia) PhD in Computer Science (University of Newcastle, Australia) His research spans advanced domains like privacy-preserving data mining , federated learning , and cross-domain security threats . Recent work addresses AI applications in agri-food supply chains, malware detection across platforms, and collaborative learning frameworks for medical data. Key scientific achievements include: Cyber Security Researcher of the Year 2021 (AISA) Innovation Award 2017 (NSW Agency for Clinical Innovation) 2024 Excellence Award (CSU) Stanford University - Elsevier World Top 2% Scientists (2024) $11M+ in competitive research funding

• Internet of Things
• Wireless Sensor Networks
• Network Security
• +۳ مورد دیگر

Craig Thomson is a Lecturer at the School of Computing Engineering and the Built Environment , Edinburgh Napier University. His research focuses on Internet of Things , Wireless Sensor Networks , and Network Security with specific emphasis on energy efficiency and protocol optimization. Key Publications: 2025: Predictive QoS balancing in IoT using deep learning 2023: VANET routing protocol safety analysis 2021: Thesis on MAC layer duty cycling algorithms His work demonstrates trends in mobile sink node optimization , RPL protocol security , and energy-aware network design . He supervises PhD students in areas related to digital twins and smartphone privacy systems . All findings are supported by Edinburgh Napier University funding.

• Database Systems
• Data Mining
• Query Optimization
• +۳ مورد دیگر

Jeffrey F. Naughton is a Professor at the University of Wisconsin and works at Google Inc in Madison, WI, USA. His academic career spans decades with significant contributions to database systems, data mining, and privacy-preserving analytics. Research Interests : Distributed query processing and optimization Machine learning integration with relational databases Differential privacy in data analysis Scalable data warehousing systems Energy-efficient database architectures Temporal data management Article Trends : Over the past decade, Naughton's publications demonstrate a trajectory from foundational database optimization to modern challenges in scalable analytics and privacy-preserving techniques. Key themes include query execution prediction, workload summarization, and system design for big data environments. Scientific Awards : ACM Software System Award (2008) - Recognizing his contributions to database software systems Collaborative Network : He has collaborated with leading researchers including AnHai Doan (entity matching), Somesh Jha (privacy), and Stratis Viglas (query optimization), producing impactful work in SIGMOD, VLDB, and ICDE venues.

• Digital Forensics
• Cybersecurity
• Blockchain Technology
• +۳ مورد دیگر

Dr. Sean Mckeown is a Lecturer at Edinburgh Napier University's School of Computing Engineering and the Built Environment , specializing in digital forensics, cybersecurity, and AI applications. His research focuses on: Perceptual hashing techniques for forensic analysis Blockchain-based evidence management Incident response tools for Unix systems Forensic challenges with wearables and streaming platforms AI-generated media detection His recent publications examine: Privacy-preserving evidence management using Hyperledger Fabric Adaptive DCT-DWT watermarking for JPEG disk images GenAI dataset diversity in image inpainting detection Spatial encoding in perceptual hashes beyond Hamming distance He supervises postgraduate researchers including Matthew Thomson, and collaborates with colleagues like Bill Buchanan and Gordon Russell on projects involving digital evidence integrity, contraband detection, and forensic tool development.

• Edge Computing
• Federated Learning
• Internet of Things
• +۵ مورد دیگر

Sasu Tarkoma is a Professor at University of Helsinki specializing in next-generation computing systems with over two decades of research experience. His work bridges theoretical computer science with practical applications in smart cities, environmental monitoring, and industrial systems. His research interests focus on edge computing infrastructure , federated learning architectures , and AI-driven environmental monitoring systems . Tarkoma's work addresses critical challenges in distributed intelligence, particularly in resource-constrained environments where privacy, energy efficiency, and real-time processing are paramount. Recent work explores the integration of large language models with edge systems and novel approaches to 6G network architectures. The publication record reveals a strategic evolution from foundational mobile computing research to cutting-edge work at the intersection of AI, networking, and sustainability. His recent articles demonstrate particular strength in solving practical implementation challenges for federated learning in industrial settings and developing energy-efficient approaches to AI deployment in constrained environments. Tarkoma maintains an extensive collaborative network across European institutions, frequently partnering with researchers from Aalto University, University of Oulu, and international partners in Asia. His work shows increasing emphasis on environmental applications, particularly air quality monitoring systems using UAVs and mobile sensors.

Denis Darquennesمشاهده پروفایل

پژوهشگر

• Coordination Languages
• Data Protection
• Tuple-based Systems
• +۴ مورد دیگر

Denis Darquennes is a computer science researcher affiliated with University of Namur, specializing in coordination languages and data protection systems. His academic career spans over three decades with significant contributions to tuple-based coordination models and data protection frameworks. His research focuses on the theoretical foundations of coordination languages, particularly examining multiplicities, density metrics, and expressiveness in tuple-space systems. Darquennes has developed formal methods for analyzing coordination patterns and has contributed to important data protection frameworks through European research initiatives. His publication record shows consistent research output from 1989 through 2020, with recent work emphasizing the expressiveness of data-based coordination languages and density metrics in distributed systems. The research demonstrates a clear progression from theoretical foundations to practical applications in data protection. Darquennes has been actively involved in major European research projects including COE-Profilage, RW-Euser, CE-Miauce, and SPFJUS-Phenix, where he contributed expertise in data protection and coordination language design. His work bridges theoretical computer science with practical privacy concerns in emerging technologies. His educational background includes doctoral research completed in 2017 on multiplicities in coordination languages, supervised by Jean-Marie Jacquet and Isabelle Linden at University of Namur, following master's level work on ant colony algorithms in 2005.

Melek Önenمشاهده پروفایل

استاد

• Applied Cryptography
• Cloud Security
• Security and Privacy for Big Data and Machine Learning
• +۳ مورد دیگر

Melek Önen is a Full Professor at EURECOM's School of Digital Security, specializing in applied cryptography, cloud security, and privacy-preserving technologies. Her work focuses on cybersecurity challenges in federated learning, big data, and IoT systems. She leads research on secure aggregation techniques, privacy-preserving machine learning, and cryptographic protocols for real-world applications like healthcare. Key research interests include federated learning security, homomorphic encryption, and privacy-aware data analytics. She has developed frameworks such as PAPAYA for privacy-preserving data analytics and UPRISE-IoT for IoT privacy solutions. Her projects often address regulatory compliance (e.g., GDPR) in cloud and distributed systems. Recent research trends show a strong focus on mitigating adversarial attacks in distributed systems, optimizing secure computation efficiency, and balancing privacy with functionality in machine learning. Notable contributions include secure biometric authentication protocols ( Nomadic ), fault-tolerant federated learning systems, and privacy-preserving image analysis methods. Önen collaborates on EU-funded initiatives like TREDISEC (trustworthy cloud security) and PAPAYA , emphasizing practical implementations. She actively contributes to open-source tools like Fed-BioMed for federated healthcare AI. Her work bridges theoretical cryptography with real-world deployment challenges.

ZHANG Jiahengمشاهده پروفایل

استادیار

• Cryptography
• Security
• Artificial Intelligence
• +۷ مورد دیگر

ZHANG Jiaheng is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His work bridges cryptography, artificial intelligence, and system security, with a focus on scalable and privacy-preserving technologies. He teaches CS3235 – Computer Security and leads research in zero-knowledge proofs, LLM safety, and trustworthy AI. Research Interests: His research spans Cryptography , Security , Machine Learning & AI , Privacy , and Algorithms & Theory . He specializes in making zero-knowledge proofs practical at scale and securing large language models against jailbreaking, backdoors, and privacy leaks. His recent projects include zkGPT, BatchZK, and Guardreasoner, highlighting his dual focus on theoretical foundations and real-world applications. The recent publications show a strong trend toward scalable zero-knowledge systems and AI security , particularly in verifying and protecting LLMs. These works integrate cryptographic rigor with modern AI challenges, reflecting a cohesive research vision at the frontier of trustworthy computing. Scientific Contributions: Developed scalable collaborative zk-SNARKs for efficient proof generation. Pioneered techniques for secure LLM inference and jailbreak detection. Advanced GPU-accelerated and distributed zero-knowledge proof systems. Advising & Grants: While specific students and grants are not listed, his active publication record in top-tier venues suggests ongoing research supervision and external funding in cybersecurity and AI. He is likely involved in advising PhD and Master’s students in cryptography and AI security. Labs & Teams: He is part of the NUS School of Computing research ecosystem, potentially affiliated with cybersecurity or AI labs, contributing to Singapore’s leadership in privacy-preserving technologies.

Ayfer Ozgurمشاهده پروفایل

دانشیار

• Information Theory
• Wireless Communication
• Networks
• +۷ مورد دیگر

Ayfer Ozgur is an Associate Professor of Electrical Engineering at Stanford University and a Chambers Faculty Scholar in the School of Engineering. She holds a Ph.D. from EPFL (2009) and B.S./M.S. degrees from Middle East Technical University. Her research focuses on information theory, wireless communication, distributed estimation, and machine learning, with notable contributions to federated learning, privacy-aware systems, and energy-efficient networks. Education: Ph.D., EPFL (2009); M.S., Middle East Technical University (2005); B.S. in Electrical Engineering and Physics (2001). Research Interests: Information Theory and Geometry Wireless Networks and Communication Federated Learning and Privacy Energy Harvesting Systems Distributed Estimation and Learning Publications Trends: Ozgur's work emphasizes theoretical foundations of decentralized data science, privacy-utility trade-offs, and geometric approaches to information theory. Recent articles explore over-the-air statistical estimation, federated learning with secure aggregation, and optimal compression under privacy constraints. Awards: Recipient of the IEEE CTTC Early Achievement Award (2018), Okawa Foundation Award (2018), and NSF Career Award (2013). Advising & Grants: Supervises doctoral and master's students in distributed systems and machine learning. Current NSF projects include 'Foundations of Decentralized Data Science' and 'Geometric Network Information Theory.' Labs/Teams: Leads a research group with active members in theoretical and applied communication systems, collaborating with industry partners like Google and META.