جست‌وجوی استاد و پژوهشگر

• German Linguistics
• Grammatical Variation
• Text Linguistics
• +۳ مورد دیگر

Vita Kraft is a Research Fellow at the Institute of German Philology , University of Würzburg, focusing on German linguistics and language variation. Since 2021, she has contributed to empirical studies on revising speech activity and grammatical variation, with her dissertation work titled "Revidierende Sprachtätigkeit kompetenter Sprecher. Eine empirische Untersuchung". PhD in German Linguistics (ongoing, based on dissertation theme) M.A. in German as a Foreign Language (University of Würzburg, 2020) Magister in Philology (German/English, Kyiv National Linguistic University, 2008) Master in International Financial Management (Kyiv National Hetman Vyhovsky University, 2011) Her research explores revision mechanisms in language, dialectal variation, and digital humanities applications through projects like the Würzburg Dialect Database . She teaches courses on German language structure, orthography, and variation, and contributes to the organization of academic events like the "Was prägt die deutsche Sprache" conference. Scientific Recognition Eberhard Schöck Scholarship

Y. Annie Liuمشاهده پروفایل

استاد

• Languages
• Algorithms
• Program Design and Optimizations
• +۴ مورد دیگر

Annie Liu is a Professor of Computer Science at Stony Brook University. Her research spans programming languages, algorithms, and distributed systems, with a focus on formal methods and system assurance. She earned her Ph.D. and M.S. from Cornell University, M.Eng. from Tsinghua University, and B.S. from Peking University, all in Computer Science. Research highlights: Distributed algorithms, logic programming, program analysis, security, and domain-specific language design. Recent publications integrate logic rules with AI, blockchain consensus, and incremental computation. Awards include the SUNY Chancellor's Award for Excellence in Scholarship and a Best Student Paper Award. Teaches courses in programming, algorithms, and distributed systems (e.g., CSE 526, CSE 626). Leads the Design and Analysis Research Laboratory, focusing on optimizing compilers, real-time systems, and big data analysis.

• Labor Economics
• Development Economics
• Human Capital
• +۹ مورد دیگر

Elena Pastorino is an Assistant Professor of Economics at the University of Minnesota and simultaneously holds multiple research appointments at Stanford University including Research Fellow at the Hoover Institution, Faculty Research Fellow in the Economics Department, and Social Science Research Scholar at the Stanford Institute for Economic Policy Research (SIEPR). Education: B.A. in Economics, Bocconi University (Italy), 1998 Ph.D. in Economics, University of Pennsylvania, 2005 Research Focus: Elena specializes in labor economics, development economics, and macroeconomics, with particular emphasis on understanding the determinants of individual wages, job mobility, consumption patterns, and aggregate employment dynamics. Her work examines how human capital, borrowing constraints, and firm market power influence labor market outcomes across both developed and developing economies. Research Themes: Her recent research explores the distributional impacts of minimum wage policies, the role of big data and artificial intelligence in dynamic pricing strategies, and the mechanisms driving wage growth and persistent income inequality. She has conducted extensive analysis on the effectiveness of social insurance programs and government policies in labor and output markets, with applications ranging from US labor markets to poverty alleviation programs in Latin America. Grants and Funding: Elena has secured significant research funding from prestigious institutions including the Banque de France, National Science Foundation, and Stanford's Human-Centered AI Institute, supporting her groundbreaking work on unemployment fluctuations, big data economics, and AI's impact on market performance. Collaborations: She maintains active research collaborations with leading economists including Jeremy Lise, Luigi Pistaferri, Erik Hurst, Patrick Kehoe, and Thomas Winberry, organizing major conferences such as the SITE conference at Stanford.

• 5G/6G Networks
• Software-Defined Networking (SDN)
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Syed Muhammad Raza is a Lecturer in Autonomous Systems & Connectivity at the University of Glasgow's James Watt School of Engineering, Department of Electronic and Electrical Engineering. His research bridges theoretical networking concepts with practical implementations in next-generation communication systems. His research focuses on 5G/6G networks , software-defined networking (SDN) , and IoT systems , with particular emphasis on mobility management, network traffic prediction, and autonomous connectivity solutions. His work integrates machine learning techniques like generative adversarial networks and temporal convolutional networks to solve complex networking challenges in wireless and mobile environments. Analysis of his 15 most recent publications reveals a strong trend toward AI-driven network optimization , with significant contributions in handover protocols for B5G/6G systems, anomaly detection in IoT data streams, and spatiotemporal modeling for traffic prediction. His research consistently addresses real-world implementation challenges in next-generation networking architectures. Dr. Raza maintains an active publication record with multiple high-impact journal articles in IEEE Transactions and other leading venues, demonstrating sustained research productivity since 2014. His collaborative work spans international institutions with frequent co-authorship patterns particularly with researchers at Chung-Ang University. His technical leadership is evident in novel protocol designs including VEAD for anomaly detection, iPaaS for intelligent paging, and HP-SFC for service function chaining protection. The practical implementations of his research are validated through experimental evaluations on real network testbeds and IoT gateways.

Surbhi Goelمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Surbhi Goel is the Magerman Term Assistant Professor at the University of Pennsylvania 's Department of Computer and Information Science , affiliated with the School of Engineering and Applied Science . She contributes to foundational machine learning research through her roles in the ASSET Center for AI systems and the Warren Center for network sciences. Her work bridges theoretical computer science and practical deep learning advancements. Education: PhD in Computer Science (University of Texas at Austin), Bachelors (IIT Delhi) Former roles: Postdoctoral researcher at Microsoft NYC, Graduate fellowships including Simons-Berkeley and JP Morgan AI Research focuses on deep learning theory , transformer architecture analysis , and optimization mysteries , with publications exploring neural tangent kernels, in-context learning, and adversarial robustness. Recent projects examine self-supervised learning, diffusion models, and Markov chain dynamics in language models. Key awards include the Bert Kay Dissertation Award (2020), Rising Stars in ML (2019), and multiple competitive fellowships. She leads courses like CIS 7000: Foundations of Modern ML and CIS 5200: Machine Learning , emphasizing theoretical foundations and empirical validation.

Enjie Liuمشاهده پروفایل

استاد

• Network Applications
• Healthcare Technology
• Big Data Management
• +۴ مورد دیگر

Dr. Enjie Liu is a Professor of Network Applications at the University of Bedfordshire's School of Computer Science and Technology. She holds leadership roles as Portfolio Leader for the Undergraduate Computer Science and Technology Foundation and IRAC Postgraduate Research Lead. Her expertise spans networked applications in healthcare, big data, IoT, and wireless protocols. With a BSc from Northwest University and a PhD in Telecommunications from Queen Mary, University of London, she has over 100 peer-reviewed publications and led major EU/EPSRC projects like CARRE and Femtocell. Her research addresses healthcare monitoring, network management, and vehicular communication challenges. Enjie's work integrates medical knowledge retrieval, disease prediction, and efficient resource allocation in IoT/VANET systems. She has pioneered projects like MyHealthAvatar for patient-centric health visualization and co-developed reliable MAC protocols for safety-critical vehicular networks. Her industrial experience includes roles in China and the UK prior to joining Bedfordshire in 2003. Education BSc in Computer Science, Northwest University, China PhD in Telecommunications, Queen Mary, University of London Research Interests Dr. Liu focuses on: Healthcare IoT systems and telemedicine applications Big data analytics for disease progression modeling Wireless network optimization in vehicular and femtocell environments Secure and reliable communication protocols for critical infrastructure Her work bridges clinical needs with advanced networking technologies through projects like CARRE (EU FP7), which developed interactive visualization tools for health monitoring. Key Contributions Developed adaptive MAC protocols for VANET safety applications Designed spectrum allocation strategies for cognitive radio IoT systems Advanced femtocell network interference mitigation techniques Labs & Teams Affiliated with the Institute for Research in Applicable Computing (IRAC) and Centre for Computer Graphics and Visualisation (CCGV) . Collaborates across disciplines in health tech and telecommunications.

Azadeh Esfandyariمشاهده پروفایل

مدرس

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Dr. Azadeh Esfandyari is a Lecturer in Computer Science at the University of Bedfordshire's School of Computer Science and Technology. She holds a PhD in Computer Science from the University of Milan and both bachelor's and master's degrees in Software Engineering. Her academic background includes experience as an assistant professor prior to joining the University of Bedfordshire. Her research spans several key areas of computer science including Data Mining, Machine Learning, Artificial Intelligence, Software Engineering, and Social Network Analysis. She has significant expertise in analyzing online human behavior and network/graph mining techniques. Dr. Esfandyari has published extensively with research appearing in journals such as the Journal of Information Science and conferences including the ACM Symposium on Applied Computing. Her publications demonstrate consistent focus on social network dynamics, web service security, industrial optimization algorithms, and service-oriented architectures. Before entering academia, she worked in London's tech industry, applying her expertise in software development and data science to deliver solutions across various domains. She has collaborated as an investigator on multiple interdisciplinary research projects.

• Computer Networks
• Networked Systems
• Network Resilience
• +۳ مورد دیگر

Charalampos Rotsos is a Senior Lecturer in Computer Networks and Networked Systems at Lancaster University's School of Computing and Communications . He leads research in network resilience, programmability, and virtualization. Current projects: DSI: DVAC (Data-driven Visualisation and Aggregation of Cyber vulnerabilities) Research groups: SCC (Networking), i-DID Research Focus : His work spans intent-based networking, middlebox resilience, software-defined networking (SDN), network virtualization (NFV), and cybersecurity. Recent studies include semantic intent translation frameworks (SWIFT), virtualization-driven resilience (REMEDIATE), and state mirroring for middleboxes (Katoptron). Collaborative Efforts : He contributes to projects like NG-CDI (Future Network Intelligence) and develops tools for emulation automation (NES), network policy inference (Reasonet), and high-speed network evaluation (OFLOPS-SUME). PhD Supervision : Mentors students including Paul Alcock, Revika Anand, and Alexander Jung in networked systems research. Collaborates with PhD students in the SCC (Networking) group. Contact : Office D14, InfoLab21. Email: c.rotsos@lancaster.ac.uk

Jun Wuمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Transfer Learning/Domain Adaptation
• Graph Learning
• +۴ مورد دیگر

Jun Wu is an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign. His research focuses on trustworthy machine learning, transfer learning/domain adaptation, and graph learning, with applications in agriculture, bioinformatics, e-commerce, and legal analytics. His work has been published in top-tier venues like NeurIPS, ICML, KDD, and TKDE. Education: Ph.D. in Computer Science, University of Illinois Urbana-Champaign. Prior to MSU, his academic trajectory included significant contributions to machine learning theory and applications. Research Interests: Trustworthy AI mechanisms under distribution shifts Graph-based transfer learning frameworks Adversarial robustness in federated learning Domain adaptation for heterogeneous data Publications reflect a strong emphasis on foundational machine learning challenges, particularly in ensuring reliability and adaptability across domains. Recent work addresses robustness in large language models, federated learning resilience, and graph-structured data analysis. No awards or grants explicitly listed in the provided text. Active in advising doctoral students but none listed here.

Wang Huaxiongمشاهده پروفایل

استاد

• Cryptography
• Cybersecurity
• Coding Theory
• +۳ مورد دیگر

Professor Wang Huaxiong is a distinguished academic and researcher in the School of Physical and Mathematical Sciences at Nanyang Technological University (NTU), Singapore, where he holds a professorship in the Division of Mathematical Sciences. He also has a courtesy appointment at the College of Computing & Data Science and leads key research centers like the National Centre for Research in Digital Trust (NCDT) and the Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS). His research focuses on cryptography, cybersecurity, coding theory, combinatorics, and theoretical computer science, with over 250 publications in top-tier journals and conferences. Education: PhD in Mathematics (1996), University of Haifa, Israel PhD in Computer Science (2001), University of Wollongong, Australia Professional Experience: Professor at NTU (2006–present) Former Head of Division of Mathematical Sciences (2013–2015) Prior faculty positions at Macquarie University and University of Wollongong His research interests span cryptography fundamentals, including post-quantum cryptography (code-based and lattice-based schemes), secure multiparty computation, homomorphic encryption, and privacy-preserving technologies. He has supervised over 50 PhD students and postdoctoral researchers, and serves on numerous editorial and program committees, including ASIACRYPT and PKC. Notable achievements include the 2004 Chris Wallace Award for mid-career computer scientists and an invited plenary talk at ASIACRYPT 2017. Key scientific contributions include advancements in lattice-based group signatures, zero-knowledge proofs, and privacy-preserving protocols. He has developed practical cryptographic schemes for real-world applications, such as secure cloud storage and vehicular networks, and his work frequently addresses challenges in post-quantum security and cryptographic efficiency. Professor Wang leads interdisciplinary initiatives, including privacy-preserving technologies for industrial IoT and blockchain systems. His research teams collaborate internationally, addressing cutting-edge problems in security and cryptography.

• Cybersecurity
• Quantum Computing
• Energy Efficiency in Computing
• +۴ مورد دیگر

Haider Ali is a Lecturer in Computer Science at the College of Science and Engineering. His research focuses on cybersecurity, quantum computing, energy-efficient computing, and optimization algorithms, particularly in IoT and real-time systems. Research interests include quantum encryption protocols, zero-day exploit detection in web platforms like WordPress, energy-aware scheduling in embedded systems, and metaheuristic algorithms for wireless sensor networks. His work bridges theoretical advancements with practical applications in edge computing, cyber-physical systems, and healthcare IoT. Notable contributions include developing novel scheduling techniques for MPSoCs, energy-optimized data aggregation in IoT, and frameworks for detecting cyber threats. Articles span reputable journals like IEEE Access and Future Internet, highlighting interdisciplinary approaches to modern computing challenges. No awards or grants are explicitly documented. Advising activity details are not provided in the text.

Suman Banerjeeمشاهده پروفایل

استاد

• Networking
• Wireless Networking
• Mobile Computing
• +۳ مورد دیگر

Suman Banerjee is the David J. DeWitt Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. He leads the WIsconsin WIreless and NetworkinG Systems (WiNGS) Laboratory, focusing on wireless and mobile networking, distributed systems, and network security. Education: Ph.D. (Computer Science, 2003) from University of Maryland; M.S. (1999) and B.Tech. (1996) from University of Maryland and IIT Kanpur, respectively. Research Interests: Mobile and wireless networking systems, overlay systems, traffic classification, and network security. His WiNGS lab explores topics like interference detection (Airshark), enterprise WLAN optimization (FLUID), and vehicular networking (WiRover). Awards: ACM SIGMOBILE Rockstar Award (2013), NSF Career Award (2008), ACM MobiCom Best Paper (2009), and Wisconsin Governor’s Business Plan Competition Grand Prize (2011). Grants & Activities: Over $4M in NSF and industry grants since 2005. Served as Program Chair for ACM MobiCom, IEEE SECON, and ACM CellNet. Editor-in-Chief of Mobile Computing and Communications Review. Labs & Teams: WiNGS Lab develops systems like WiRover for vehicular connectivity and Airshark for non-WiFi interference detection. Collaborates with industry partners like Cisco, Microsoft, and Intel.

Saeed Sametمشاهده پروفایل

استاد

• Cybersecurity
• Privacy-Preserving Data Mining
• Federated Learning
• +۵ مورد دیگر

Dr. Saeed Samet is a Professor in the School of Computer Science at the University of Windsor. His research focuses on cybersecurity, privacy-preserving data mining, federated learning, blockchain technology, and healthcare informatics. He has led projects such as the privacy-preserving personal health record system (P3HR) and the decentralized electronic health records (DEHR) model leveraging blockchain. Samet actively engages in interdisciplinary collaborations, including developing frameworks like SEERa for community prediction and RNBFT for scalable Byzantine consensus. He advises students like Saghi Khani on topics such as social isolation detection algorithms. Notably, his work addresses adversarial attacks in machine learning and federated learning security. Samet has contributed to initiatives like the International Collegiate Programming Contest and the WE-Spark Health Institute grants. Education While formal academic credentials are not explicitly detailed in the text, Samet holds a professorship, indicating advanced qualifications in computer science or related fields. His extensive publications suggest doctoral-level expertise. Research Interests Samet’s research spans cybersecurity mechanisms, privacy-preserving methodologies (e.g., homomorphic encryption, federated learning), blockchain applications in healthcare, and machine learning robustness. Recent work emphasizes scalable consensus algorithms (RNBFT), adversarial defense in federated learning, and frameworks for decentralized data evaluation. His projects often integrate real-world challenges, such as combating fake news via generative AI and enhancing pandemic response through blockchain-based patient tracking. Awards & Grants Recipient of a WE-Spark Health Institute grant for innovative research in Windsor-Essex (2021). His work on social isolation algorithms (2020) and privacy-preserving statistical analysis (2019) underscores sustained funding support for impactful projects. Advising & Grants Advises Saghi Khani on machine learning algorithms for social isolation detection. Collaborates on grants involving interdisciplinary teams, such as the $287,000 WE-Spark Health Institute award. His research often bridges academia and industry, addressing practical challenges in healthcare and cybersecurity. Labs & Teams Leads initiatives in the School of Computer Science’s research groups, focusing on privacy-preserving systems and distributed learning. Collaborates with organizations like the WE-Spark Health Institute and the University of Windsor’s interdisciplinary teams on projects such as the DEHR blockchain model and federated learning frameworks.

SALAH UD DIN Muhammadمشاهده پروفایل

پژوهشگر ارشد

• Wireless Communications
• Information-Centric Networking (ICN/NDN)
• Edge Computing
• +۷ مورد دیگر

Dr. SALAH UD DIN Muhammad is a Research Fellow at the Communication System Department of EURECOM, France. He holds a Ph.D. in Wireless Communications from Hongik University, South Korea, under Professor Byung-Seo Kim. His research focuses on integrating Artificial Intelligence with emerging networking paradigms, including 6G, Edge Computing, and Information-Centric Networking (ICN/NDN). He has expertise in vehicular networks, IoT, and sensor networks, addressing challenges like latency reduction and resource optimization. Education: Ph.D. in Wireless Communications, Hongik University, South Korea (2019-2023) M.Sc. in Wireless Communications, COMSATS University, Pakistan B.Sc. in Software Development, Dr. AQ Khan Institute, Pakistan Research Interests: His work spans AI-driven network protocols, microservices architecture for edge computing, and efficient data management in vehicular/iot systems. He has contributed to projects like CFEC (ultra-low latency framework) and INF-NDN IoT (intelligent naming for IoT). Awards & Grants: ACM SIGCOMM Travel Grant (2022) Hongik University Research Frontier Grant (2021) Best Research of the Year Award (2021-2022) NDN Hackathon Awards (2019, 2021) Professional Contributions: He has served as Technical Program Co-Chair for IEEE ICC 2023 and reviewer for journals like IEEE IoT Journal and Transactions on Services Computing. His research has led to 34+ publications and 3 patents in networking and edge computing domains. Labs & Teams: Active member of the Broadband Convergence Network (BCN) Lab at Hongik University and currently affiliated with EURECOM's Communication Systems group.

• Maritime Informatics
• Autonomous Systems
• Big Data Analytics
• +۴ مورد دیگر

Associate Professor Dimitris Zisis leads the Smart Move Laboratory at the University of the Aegean's Department of Product & Systems Design Engineering, focusing on autonomous vessels and marine robotics. His research spans maritime informatics, trajectory analytics, and Big Data applications. He has published over 80 works with 3,900+ citations, including a top-0.1% cited paper on cloud computing security. As a Horizon Europe National Representative for Cluster 4 (Digital, Industry, Space), he advises EU digital policy. He previously led BDVA's Data Science/AI Task Force and participated in EU High-Level Expert Groups on B2G data sharing. He chairs the IEEE's maritime systems committees and edits Elsevier's Future Generation Computer Systems journal. Projects: Horizon 2020 VesselAI, INFORE, BigDataOcean Awards: Top 2% scientist in Information Systems (2019-2021) Memberships: IEEE Senior Member, Hellenic Institute of Marine Technology Fellow His lab develops advanced maritime systems, including the Accu-Waves platform for port safety and the Ro-Boat Race for autonomous vessel innovation. Recent work emphasizes federated learning for vessel location forecasting, scalable maritime event detection, and digital twin applications.