جستجوی استاد راهنمای دانشگاهی

Muhammad Younasمشاهده پروفایل

استاد

• Cloud Computing
• Big Data
• Internet of Things
• +۲ مورد دیگر

Muhammad Younas is a Professor of Computer Science at Oxford Brookes University's School of Engineering, Computing and Mathematics. He serves as Subject Coordinator for both MSc Data Science & AI and MSc Artificial Intelligence programs. His teaching covers Web Technologies, Data Science, E-Business Systems, and Cloud Computing. His research focuses on: Cloud Computing & Big Data Internet of Things Web/Database Systems Web Information Retrieval He co-leads the Migration and Refugees Research Network and directs projects including: Cloud Service Selection, NoSQL Transaction Management, WoT Architecture, and Web Information Extraction. Recent publications (2015-2024) demonstrate strong focus on cloud systems, blockchain, IoT security, and machine learning applications, with emerging work in intelligent transportation and metaverse technologies. He supervises postgraduate students in areas including cloud computing, big data systems, IoT, and web technologies. Professional affiliations include the Applied Software Engineering & Data Analytics Group and Cloud Computing & Cybersecurity Group. Professor Younas chairs major IEEE conferences (ICWS, SCC, BigDataService) and serves on editorial boards for multiple journals including International Journal of Service-Oriented Computing and Applications and Human-centric Computing and Information Sciences.

Alex Thomoمشاهده پروفایل

استاد

• Databases
• Algorithms for Big Graphs
• Distributed Data
• +۴ مورد دیگر

Alex Thomo is a Professor in the Department of Computer Science at the University of Victoria, Canada. He holds a PhD from Concordia University (2003) and has expertise in databases, graph algorithms, and formal languages. His research focuses on database systems, big data algorithms, social network analysis, and privacy-preserving techniques. Education: B.Sc. (University of Piraeus, 1996), M.Sc. and PhD (Concordia University, 2001–2003). Research interests include distributed data processing, graph theory applications, and scalable algorithms for large networks. He has contributed to projects like the k-core decomposition algorithm and importance-based community detection in web-scale networks. Teaching includes courses on database systems, data mining, and artificial intelligence. Active in academic service as an associate editor of Social Network Analysis and Mining .

Jamal Bentaharمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Federated Learning
• +۷ مورد دیگر

Prof. Jamal Bentahar is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), part of Concordia University's Faculty of Engineering and Computer Science. His research focuses on intelligent agents, multi-agent systems, federated learning, reinforcement learning, cybersecurity, and their applications in healthcare, IoT, and autonomous systems. He leads research initiatives in AI-driven medical imaging, edge-cloud computing, and blockchain-empowered distributed systems. Key research areas include: Multi-Agent Systems: Formal verification of trust and commitments, group trust modeling, and distributed decision-making Federated Learning: On-demand client/model deployment, trust-aware optimization, and privacy-preserving frameworks Medical AI: Cardiac ultrasound analysis, CPR signal processing, and robotic telemedicine systems Edge/IoT Intelligence: UAV-enabled vehicular networks, energy-efficient LoRa gateways, and fog computing optimization His work bridges theoretical foundations (e.g., multi-valued model checking) with practical applications in healthcare robotics, smart city infrastructure, and autonomous vehicle safety. Recent publications highlight innovations in transformer-based reinforcement learning, spherical topic modeling, and explainable AI for cybersecurity. Prof. Bentahar's contributions include tools like MV-Checker for multi-valued verification and frameworks like CACTUS for cardiac ultrasound analysis. He actively publishes in top venues and serves as editor for special sections on federated learning and AI applications.

Martin Craneمشاهده پروفایل

استاد

• Computational Finance
• Complex Systems
• Time Series Analysis
• +۱ مورد دیگر

Martin Crane is Professor and Head of School at Dublin City University School of Computing. Funded Investigator in ADAPT Centre, with PhD in Mechanical Engineering from Trinity College Dublin. Research spans computational finance, complex systems modeling, and high-performance computing applications. Key research areas: Time series analysis in finance/biology Complex systems in finance, immunology, and lifelogging Wavelet methods for market analysis Parallel computing for large-scale simulations Current projects examine cryptocurrency dynamics, healthcare analytics, and brain tumor segmentation using deep learning. Leads FinTech Fusion initiative exploring financial technology applications. Authored significant works on random matrix theory filters for portfolio optimization and agent-based HIV modeling.

• E-Learning
• Virtual Worlds
• Information Systems
• +۳ مورد دیگر

Frank Kappe is a Professor at Graz University of Technology's Institute of Human-Centred Computing, specializing in e-learning systems, virtual environments, and web technologies. His research integrates educational innovation with computational solutions, focusing on scalable learning architectures and interactive platforms. His work spans digital manufacturing platforms, visual document exploration systems, and adaptive content delivery frameworks. Recent studies examine blockchain applications in education, flipped classroom models, and security vulnerabilities in learning management systems. Kappe maintains an extensive publication record exploring human-computer interaction paradigms, collaborative learning tools, and real-time communication systems for education. His technical innovations include mentoring session architectures using Reverse AJAX and intelligent curriculum assistants.

• Blockchain Technology
• Social Network Analysis
• Reputation Systems
• +۶ مورد دیگر

Associate Professor Aphrodite Tsalgatidou holds a faculty position at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. Her research focuses on blockchain technology, social network analysis, and reputation systems with applications in e-commerce, distributed systems, and business process management. Her work spans over three decades, addressing challenges in interoperability of heterogeneous services, P2P systems, and mobile commerce. Notable contributions include frameworks for secure blockchain scaling (Adrestus), real-time social tracking systems (BLACE), and reputation mechanisms for e-commerce platforms. She has also explored abstracting BPMN models for complex process comprehension. Her research integrates computer science with practical applications in distributed systems, cybersecurity, and service-oriented architectures. Recent efforts emphasize scalable blockchain solutions and AI-driven big data pipelines. Despite extensive contributions, no scientific awards are explicitly mentioned in available materials. Her advising and grant activities are not detailed in the provided data, though her prolific publication history suggests active involvement in academic collaborations. No lab affiliations or teams are specified in the text.

Tiziana Margariaمشاهده پروفایل

استاد

• Model-Driven Development
• Artificial Intelligence
• Formal Verification
• +۷ مورد دیگر

Professor Tiziana Margaria is a faculty member at the Department of Computer Science & Information Systems at the University of Limerick. She is affiliated with multiple research centers, including Lero – the Irish Software Research Centre, the Centre for Research Training in Foundations of Data Science, and the Limerick Digital Cancer Research Centre. Academic focus on Model-Driven Development, Artificial Intelligence in Healthcare, and Formal Verification 2023 recipient of the University of Limerick Team Teaching Award Active in interdisciplinary collaborations bridging Computer Science with Digital Humanities and Healthcare Develops low-code/no-code platforms for medical data analysis and smart manufacturing Her research spans AI governance , blockchain-based privacy systems , digital twins for cyber-physical systems , and education-focused software tools . Recent projects include interoperability frameworks for healthcare data and extreme model-driven approaches for historical data transcription. Notable scientific contributions include: 2025: Review on AI-driven healthcare system design 2024: Formal methods in AI validation 2022: Edge analytics in smart manufacturing 2020: Blockchain for medical privacy Her work emphasizes practical implementation of theoretical concepts through tools like Cinco de Bio and MazeGen. She actively promotes collaboration between technical and domain-specific teams in Industry 4.0 contexts.

• Digital Innovation
• Business Innovation
• Digital Marketing
• +۶ مورد دیگر

Dr. Chunjia Han is a Senior Lecturer (Associate Professor) in Business Innovation at Birkbeck Business School, University of London. He also serves as a visiting professor and PhD supervisor at institutions like Beijing Jiaotong University and Sichuan University. His research focuses on the application and impact of digital technologies—such as big data analytics, AI, and social media—in business and economic contexts, contributing to innovation management, tourism management, digital marketing, and the digital economy. Dr. Han has secured over £4 million in research grants from councils like EPSRC, ERC, and NNSFC, emphasizing international collaboration. His editorial roles include guest editorships for journals like Journal of Global Information Management and Computer Modeling in Engineering & Sciences . He actively contributes to professional committees for conferences like IEEE International Conference on Bioinformatics and Biomedicine. Dr. Han’s work bridges academia and industry, advising on digital transformation and data governance projects in Europe and China. Research interests include open innovation, user innovation, and digital innovation, with a focus on leveraging unstructured data (e.g., text, images) for business insights. His recent projects address challenges like AI-driven service evaluation, blockchain-enabled financing, and geopolitical risks in technology adoption. His articles explore themes such as digital innovation strategies, metaverse consumer behavior, and blockchain applications. Professional service roles include deputy director of the International Creative Management Committee and committee memberships in AAAI and IEEE conferences.

• Data Science
• Edge Computing
• IoT Security
• +۳ مورد دیگر

Dr. Yalong Wu is an Assistant Professor of Data Science at the University of Houston-Clear Lake's College of Science and Engineering. His research focuses on data-driven solutions for edge computing, IoT security, machine learning applications, and network optimization in distributed systems. Research domains include big data quality assessment, blockchain security frameworks, edge intelligence for video analytics, and neuromorphic computing. Recent publications demonstrate strong emphasis on practical implementations for cybersecurity and network efficiency. Publications show consistent focus on distributed systems challenges, with emerging work on adversarial machine learning, blockchain applications, and edge computing frameworks. Methodological approaches integrate statistical modeling, game theory, and optimization techniques.

Haiping Xuمشاهده پروفایل

استاد

• Software Engineering
• Distributed Computing
• Formal Methods
• +۱۰ مورد دیگر

Dr. Haiping Xu is a Professor and Chairperson of the Computer and Information Science Department at the University of Massachusetts Dartmouth. He serves as Director of the AI and Software Engineering Research (AISER) Laboratory and Concurrent Software Engineering Laboratory, while maintaining affiliations with the Center for Scientific Computing and Visualization Research (CSCVR) and Data Science Interdisciplinary Program (DSC). Education: PhD in Computer Science (2003), MS in Computer Science (1998), MS in Computer Engineering (1992), BS in Electrical Engineering (1989) Research Focus: Software Engineering, Formal Methods, Blockchain Technology, Cloud Security, Multi-Agent Systems, and Semantic Computing Leadership: Ph.D. in Computer Science & Information Systems Committee Chair (2013-present), College of Engineering Academic Council member His research has produced 15 recent publications spanning blockchain scalability, deep learning for medical coding, cloud security architectures, and real-time data analysis systems. Key subfields include adaptive learning models for sensor data streams, ontology-based search engines , and AI-driven trust management for online auctions. Scientific Recognition: IEEE Senior Member, ACM Senior Member, multiple editorial board memberships Advising: Mentored 12+ graduate students in projects like CS2Notes cloud security system and DRBD reliability modeling Grant Leadership: $457,478 from Office of Naval Research for adaptive learning models

Khondker Shajadul Hasanمشاهده پروفایل

دانشیار

• Distributed Computing
• Blockchain Security
• Internet of Things
• +۳ مورد دیگر

Khondker Shajadul Hasan is an Associate Professor of Computer Science in the College of Science and Engineering at the University of Houston–Clear Lake since Fall 2021, after serving as Assistant Professor from 2016. He teaches both undergraduate and graduate courses and directs research in distributed systems, blockchain security, GPU performance modeling, and IoT analytics. Education Ph.D. in Computer Science, University of Oklahoma, Norman, USA (2014) Dissertation: “Prediction Models for Estimating the Efficiency of Distributed Multi-core Systems” M.S. in Computer Science, Wichita State University, Kansas, USA (2001) Research Interests Dr. Hasan’s work bridges theory and practice in distributed computing , blockchain-based security frameworks , and GPU-accelerated high-performance computing . His group designs predictive models for multi-core CPU availability and energy-efficient scheduling, and explores private-blockchain solutions for securing data streams in IoT and aviation domains. Publications & Trends Across 10 refereed papers since 2017, he has advanced the state-of-the-art in secure IoT architectures, smart-farming analytics, and JVM-level concurrency optimization. His research consistently integrates machine-learning techniques with systems-level challenges, yielding practical tools for industry and academia. Awards & Honors Piper Award Semi-Finalist, University of Houston–Clear Lake (2017–2022) Distinguished Alumni Award, North South University (2017) Computer Science Advisory Board Award, University of Oklahoma (2013-14) Industry certifications from Schlumberger and InterSystems (2014-2015) Advising & Service Since 2016 he has mentored numerous graduate and undergraduate researchers and has served as faculty advisor for the Computer Club. He is an active member of editorial boards and technical program committees for IEEE and Elsevier journals/conferences worldwide. Laboratory & Collaborations Dr. Hasan directs the Parallel and Distributed Systems Lab at UHCL, collaborating with industry partners such as Schlumberger and academic groups across the USA and Asia on blockchain security, GPU performance modeling, and smart-IoT systems.

Sonya H. Y. Hsuمشاهده پروفایل

دانشیار

• Health Informatics
• Business Analytics
• Machine Learning
• +۳ مورد دیگر

Sonya H. Y. Hsu is an Associate Professor in the School of Computing and Informatics at the University of Louisiana Lafayette, with expertise in health/business informatics, machine learning, and blockchain applications. A Fulbright Specialist, she develops analytics frameworks for healthcare and business operations. Her research encompasses three primary domains: Health informatics systems for patient care management and epidemiological monitoring Blockchain implementations for enterprise security and supply chain finance Machine learning models for misinformation detection and privacy-preserving diagnostics Dr. Hsu's publications demonstrate consistent innovation in applying deep learning architectures to practical problems, including patented kernel-segregated convolution methods and BERT-based fake news detection systems. Recent work focuses on transpose convolution optimization and election prediction from social media data. She maintains active collaborations in IoMT security and has developed real-time health monitoring systems for flu surveillance. Current projects investigate hybrid machine learning approaches for customer segmentation and blockchain consensus mechanisms for enterprise applications.

Mehmet Engin Tozalمشاهده پروفایل

دانشیار

• Data Science
• Graph Theory
• Cybersecurity
• +۳ مورد دیگر

Mehmet Engin Tozal is Francis Patrick Clark/BORSF Endowed Associate Professor in the School of Computing and Informatics at the University of Louisiana at Lafayette. His research develops machine learning and graph-theoretic methods for cybersecurity, health informatics, and network analysis. Tozal has secured over $1.3M in grants from organizations including MITRE and NSF. His current projects focus on anomaly detection in blockchain transactions, predictive link analysis in computer networks, and healthcare fraud detection using knowledge graphs. He advises multiple doctoral students and teaches courses in data visualization, information assurance, and advanced informatics. His publications span network topology mapping, cybersecurity, and health informatics applications.

Yongge Wangمشاهده پروفایل

استاد

• Cryptography
• Blockchain
• Post-Quantum Cryptography
• +۶ مورد دیگر

Yongge Wang is a Professor at the Department of Software and Information Systems within the College of Computing and Informatics at the University of North Carolina at Charlotte. His research spans cryptography, blockchain, post-quantum cryptography, deep learning, and secure communications. He has contributed to industry standards such as IEEE 1363.2 (SRP5) and IEEE 1363.3 (WANG-KE), as well as IETF RFC 4050 for ECDSA with XML-Signature syntax. Research Interests : Cryptography: Focused on post-quantum schemes (e.g., RLCE, polarRLCE), garbled computation, and secure authentication protocols. Blockchain: Led work on consensus protocols (XP for asynchronous networks), DeFi mechanisms, and censorship-resistant systems. Algorithmic Randomness: Pioneered LIL testing techniques for pseudorandom generators, analyzing vulnerabilities in Debian OpenSSL. Publications : His recent articles (2016-2024) address blockchain security, code-based cryptography, garbled circuits, and quantum-resistant algorithms, with trends emphasizing decentralized systems and secure cloud computation. Grants and Funding : Secured multiple NSF and UNC Charlotte grants for cybersecurity research, including projects on privacy-preserving databases and secure storage systems. Education : Holds a PhD in Mathematics from Heidelberg University (Germany), focusing on randomness and computational complexity. Teaching : Courses include Applied Cryptography, Cloud Data Storage, and Network Security, with a strong emphasis on academic integrity and hands-on projects. Software Tools : Developed the LIL statistical testing package for pseudorandomness, available as a Java application.

Kouhyar Tavakolianمشاهده پروفایل

استاد پژوهش

• Biomedical Signal Processing
• Medical IoT
• Seismocardiography
• +۲ مورد دیگر

Kouhyar Tavakolian is Executive Director of UND's BioInnovation Zone and UND Site Director for NSF's Center to Stream Healthcare in Place. As Research Professor in Biomedical Engineering, his work integrates signal processing, wearable sensors, and clinical applications. Research focuses on noninvasive cardiology methods (seismocardiography, ballistocardiography) and medical IoT systems. He has secured $13M+ funding from NASA, NIH, and DoD. Recent publications emphasize AI-driven food safety systems, space agriculture monitoring, and federated learning for healthcare. Teaches courses in Biomedical Instrumentation, Machine Learning Applications, and Physiology for Engineers. Holds P.Eng licensure and MBA.