جست‌وجوی استاد و پژوهشگر

Gaurav Sharmaمشاهده پروفایل

استاد

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +۳ مورد دیگر

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

• Cryptography
• Computer Security
• Privacy
• +۳ مورد دیگر

Gene Tsudik is the Peter & Lois Griffin Professor at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences. His research focuses on cryptography, computer security, privacy, and applied cryptographic protocols. He has authored over 300 publications in top-tier venues, addressing challenges in secure communication, key management, privacy-preserving technologies, and network security. Key research areas include group key agreement, secure data possession, privacy-preserving protocols, and mobile agent systems. His work has influenced foundational aspects of computational grid security, cloud storage integrity, and post-quantum cryptography. Collaborations include prominent institutions like KAIST, University of Surrey, and NYIT. His publications span topics from theoretical cryptographic proofs to practical system implementations, reflecting a balance between academic rigor and real-world applicability.

Xiaoyu Xiaمشاهده پروفایل

مدرس

• System Privacy and Security
• AI Privacy
• Parallel and Distributed Computing
• +۴ مورد دیگر

Dr. Xiaoyu Xia is a Lecturer (equivalent to Assistant Professor in North America) in Cybersecurity & Software Systems at RMIT University's School of Computing Technologies. He received his PhD with the prestigious Alfred Deakin Medal from Deakin University, Australia, and has established himself as a leading researcher in distributed systems and cybersecurity with over 50 peer-reviewed publications in top-tier venues including IEEE S&P, ACM WWW, and IEEE Transactions. Dr. Xia's research spans critical areas at the intersection of computing and security: System Privacy and Security Distributed Systems and Edge Computing AI Privacy and Machine Learning Systems Sustainable Computing Cybersecurity and Privacy-Preserving Technologies His recent work demonstrates a clear trajectory toward developing practical privacy-preserving frameworks for emerging technologies, particularly in edge computing environments and large language models. Dr. Xia has made significant contributions to machine unlearning, secure data management in distributed systems, and energy-efficient edge computing solutions that balance performance with sustainability concerns. Dr. Xia has received notable recognition for his scholarly impact: World's Top 2% Scientists by Stanford University (2022-2024) Alfred Deakin Medal for PhD research excellence (2021) Teaching Excellence Award from Swinburne University of Technology (2021) As an active researcher, Dr. Xia currently leads multiple funded projects including an ARC Discovery Project grant worth over $500,000 for developing privacy-aware intelligent digital twins for secure critical infrastructures. He is open to supervising motivated PhD students with interests in system security and privacy, and distributed ML systems. Dr. Xia serves the academic community through editorial roles as Associate Editor for IEEE Transactions on Dependable and Secure Computing and as a Review Board Member for IEEE Transactions on Parallel and Distributed Systems, and regularly participates in program committees for major conferences including ACM WWW and IEEE ICDCS.

• Resilience in 5G
• Distributed Artificial Intelligence (Edge AI)
• Information assessment
• +۳ مورد دیگر

Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)

Paul de Vriezeمشاهده پروفایل

عضو هیئت علمی

• Enterprise Information Systems
• Business Process Management
• Digital Twins
• +۱۲ مورد دیگر

Dr. Paul de Vrieze is a Principal Academic in Web Systems and Technologies at Bournemouth University's Faculty of Science and Technology, Department of Computing and Informatics. He is the coordinator of the EU H2020 FIRST project and leads research in enterprise information systems, digital twins, and Industry 4.0. His work contributes to the UN Sustainable Development Goals, particularly Quality Education, Decent Work and Economic Growth, Industry Innovation and Infrastructure, and Reduced Inequalities. Education: PhD in Information Systems, Radboud University Nijmegen, 2006 MSc in Information Systems Management, Tilburg University, 2002 His research focuses on the design and implementation of complex enterprise information systems, especially in supporting and automating business processes, with a strong emphasis on cross-organizational and collaborative systems. He investigates resilience, interoperability, and service-oriented architectures in virtual factories and digital twins. His recent work applies these concepts in industrial contexts, particularly under Industry 4.0. The 15 most recent publications show a strong trend toward digital twin technologies, predictive maintenance, federated simulation, and compliance in collaborative networks. These works are deeply rooted in computer science and industrial engineering, with applications in smart manufacturing, supply chain resilience, and sustainable production. Scientific Awards and Recognitions: Panel Member, Guardian round table on cloud computing, 2012 Dr. de Vrieze has supervised multiple PhD students to completion, including Olaolu Sofela, John-Paul Kasse, Oluwetoyin Fakorede, Go Sang, and Rushan Arshad. He has secured grants from the European Commission (H2020, Horizon Europe), National Natural Science Foundation of China, British Council, and internal Fusion Funding. His current major grant is the Sustainable Production of High Entropy Alloys from Secondary Metals (Horizon Europe MSCA, starting 2026). Labs and Research Teams: He is central to the EU H2020 FIRST project team, which focuses on virtual factories and interoperation supporting business innovation. He collaborates extensively with Prof. Liang Xu and researchers across Europe and China. He is also involved in the Service Computing Research group at Bournemouth University.

• Artificial Intelligence
• Constraint Programming
• Computational Social Choice
• +۶ مورد دیگر

Toby Walsh is a Scientia Professor of Artificial Intelligence at the University of New South Wales (UNSW), holding multiple prestigious positions including Laureate Fellow, research group leader at Data61, adjunct professor at QUT, external Professor at Uppsala University, honorary fellow at Edinburgh University, and Associate Member of the Australian Human Rights Institute at UNSW. His research spans the breadth of artificial intelligence with particular focus on constraint programming, computational social choice, and fair division. Professor Walsh has made significant contributions to the theoretical foundations of AI, especially in areas related to preference representation, voting systems, and mechanism design. His work bridges theoretical computer science with practical applications in social choice and resource allocation problems. In recent years, he has expanded his research to address pressing ethical issues in AI, including privacy concerns, facial recognition technology, and AI's impact on intellectual property law. Walsh's publication record demonstrates consistent high-impact contributions across decades, with recent work showing increasing attention to societal implications of AI technologies. His research portfolio includes both theoretical advances in constraint satisfaction and practical applications in fair division algorithms, reflecting his ability to connect foundational research with real-world problems. NSW Premier's Prize for Excellence in Engineering and ICT Humboldt Award Research Excellence Award of the Association for Constraint Programming IJCAI Donald E. Walker Distinguished Service Award Professor Walsh has served the academic community extensively through editorial roles as Editor-in-Chief of the Journal of Artificial Intelligence Research and AI Communications, and on the editorial boards of the Journal of the ACM, Journal of Automated Reasoning, and Constraints journal. He has been actively involved in major AI conferences as Program and Conference Chair for Constraint Programming, Automated Reasoning, and Artificial Intelligence venues. His leadership extends to professional organizations where he has served as Secretary of the Association for Constraint Programming and Editor of CP News. As research group leader at Data61, Walsh oversees a team focused on advancing AI research with practical applications. His group has contributed significantly to constraint programming and computational social choice, developing algorithms and theoretical frameworks that have influenced both academic research and practical implementations in resource allocation systems.

• Neuroscience
• Physics of Complex Systems
• Computational Neuroscience
• +۵ مورد دیگر

Prof. Viola Priesemann is a physicist and neuroscientist at the Max Planck Institute for Dynamics and Self-organization and University of Göttingen (W3 Professorship, 20% position). Her research spans self-organization in adaptive networks across neural and societal scales, with applications to neural criticality , information processing , and COVID-19 pandemic dynamics . She leads an independent Max Planck Research Group since 2017 and has held affiliations with Caltech , MPI Brain Research , and Bernstein Center Göttingen . Her research combines statistical physics , information theory , and non-equilibrium systems to study: Self-organized criticality in brain networks Subsampling theory for large-scale systems Neural basis of curiosity and learning Test-trace-isolation strategies for epidemics Information spread in social networks Design of energy-efficient AI architectures Key scientific awards include: Communitas Award (Max Planck Society, 2021) Science award of Lower-Saxony (2021) Medaille für naturwissenschaftliche Publizistik (DPG, 2021) Young Scientist Award (DPG, 2024) Her work on cross-scale information flow has influenced both neuroscience and public health policy , including advisory roles in the German government's COVID-19 expert panel .

• Distributed Computing
• Federated Learning
• Cloud Computing
• +۱۲ مورد دیگر

Salman Zubair Toor is a researcher at Uppsala University, Sweden, specializing in distributed computing, federated learning, and cloud/edge infrastructure optimization. His work spans resource scheduling, data streaming, and secure anomaly detection.

Rafael Pereira Piresمشاهده پروفایل

مدرس

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

• Logistics Operations
• Smart Manufacturing
• Industrial Engineering
• +۶ مورد دیگر

Maharshi Dhada is a Research Fellow at Darwin College, University of Cambridge, focusing on logistics operations and smart manufacturing. His work bridges industrial engineering with advanced statistical modeling. University: University of Cambridge College: Darwin College Rank: Research Fellow His research integrates hierarchical statistical models and machine learning for predictive maintenance in engineering fleets, smart logistics , and public health applications like SARS-CoV-2 policy analysis. Recent publications highlight collaborations in radio access networks , road condition prediction , and low-cost monitoring solutions . Key article trends show a focus on Weibull models , multi-agent systems , and federated learning for cross-system knowledge transfer. His work spans applications from truck fleet survival analysis to genomic epidemiology .

Tor-Morten Grønliمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Software Architecture
• Machine Learning
• +۴ مورد دیگر

Tor-Morten Grønli serves as Professor at the Department of Technology, School of Economics, Innovation and Technology, Kristiania University College (Norway). He is also a Visiting Research Scholar at Copenhagen Business School's Department of Information Technology Management and an affiliate of the Center of Business Data Analytics (cbsDBA). Education PhD in Computer Science from Brunel University, London (2011) Master of Technology (with distinction) from Brunel University, London (2007) Research Focus Grønli leads research in context-aware systems, mobile/pervasive computing, and Internet of Things (IoT). He founded/directs the Mobile Technology Lab at Kristiania and has co-authored 70+ publications. Core expertise includes: IoT architecture and applications Machine learning for transport systems Mobile computing frameworks Blockchain-security integration Edge-cloud computing paradigms Publication Trends Recent works (2023-2025) demonstrate strong focus on converging IoT, blockchain, and AI technologies, particularly for intelligent transport and healthcare systems. Dominant themes include federated learning implementations, privacy-preserving architectures, and sustainable edge computing solutions, with increasing emphasis on real-world applications in medical diagnostics and public infrastructure. Professional Activities Founder/Director of Mobile Technology Lab General Chair: Norwegian Conference on ICT Co-organizer: International Conference on Mobile Web Editorial Board: International Journal of Pervasive Computing, Journal of Online Information Review, Computers & Electrical Engineering TPC Member for IEEE BigData, Percom, HICSS, COMPSAC Guest Editor for special issues in Future Generation Computer Systems

Hao Yeمشاهده پروفایل

استادیار

• Machine Learning
• Wireless Communications
• AI Applications
• +۴ مورد دیگر

Hao Ye is an Assistant Professor in Electrical and Computer Engineering at the University of California, Santa Cruz (UCSC). Previously, he worked as a Machine Learning Researcher at Qualcomm AI Research before transitioning to academia. His research focuses on integrating machine learning methodology into communication networks, particularly for applications involving edge devices such as autonomous vehicles. PhD in Electrical and Computer Engineering, Georgia Institute of Technology His research interests center on wireless communications, AI-driven network optimization, and signal processing for vehicular networks. He explores deep learning applications in channel estimation, resource allocation, and end-to-end communication systems, emphasizing adaptability in unreliable environments. Key trends in his publications include: Advancing Massive MIMO systems with data-driven approaches Developing graph neural networks for wireless resource management Integrating reinforcement learning into vehicular network spectrum sharing Proposing pilot-free end-to-end communication frameworks using deep learning Investigating federated learning in decentralized wireless architectures Applying meta-learning to networked AI systems Scientific contributions include being invited as an Editor for IEEE Transactions on Wireless Communications in 2024. He actively mentors PhD students and collaborates with researchers like Le Liang, Geoffrey Ye Li, and Shi Jin. Recent team announcements include welcoming Jing Qiao in September 2024, reflecting ongoing growth in his research group focused on machine learning and wireless systems.

• Artificial Intelligence
• Distributed Computing
• Energy Communities
• +۳ مورد دیگر

Rafael Duarte Pereira da Silva is a Research Fellow at the Research Group on Engineering and Intelligent Computing for Innovation and Development (GECAD) within the School of Engineering of the Polytechnic Institute of Porto. He is currently pursuing his doctoral degree in Artificial Intelligence and Intelligent Systems Engineering while developing AI-driven solutions for energy communities and smart buildings. His academic background includes: Master's degree in Artificial Intelligence Engineering (2025) from the Polytechnic Institute of Porto, with a dissertation on "Distributed Intelligent Management of Citizen Communities" (grade: 19/20). Bachelor's degree in Computer Engineering (2022) from the Polytechnic Institute of Porto, featuring a green computing project for smart building energy management. Currently enrolled in a doctoral program in Artificial Intelligence and Intelligent Systems Engineering (since 2025). Rafael's research spans Artificial Intelligence, Distributed Computing, and Energy Communities, focusing on human-centric intelligent systems for resource optimization. He develops virtual energy communities using IoT devices and AI models to enable dynamic interaction with community members while managing distributed resources. His work emphasizes sustainability through green computing principles and integration of renewable energy sources in smart infrastructure. His 2024-2025 publications reveal a cohesive research trajectory centered on the Caravels framework for decentralized energy management. Key themes include container-based infrastructure for community resource sharing, peer-to-peer energy trading with storage optimization, and AI-driven demand response systems. His work bridges theoretical AI models with practical IoT implementations, particularly in energy forecasting and HVAC control, demonstrating significant contributions to sustainable consumption in smart buildings and community-scale energy systems. Advising Co-supervised three undergraduate thesis projects at the Polytechnic Institute of Porto on IoT notification systems, Linux-based single-board computer configuration, and open-source IoT devices for smart buildings. Grants and Projects Sa4CPS: Secure situational awareness for critical cyber-physical systems (COMPETE2030-FEDER and ITEA) NGS: New Generation Storage (PRR initiative) TIoCPS: Trustworthy and Smart Communities of Cyber-Physical Systems (P2020 and ITEA programs) As an active GECAD member, Rafael contributes to server infrastructure management and organizes scientific events, including public demonstrations that secured the group's "Excellent" FCT evaluation rating. He has coordinated annual team energizing events for two consecutive years and participated in developing virtual energy community systems that showcase GECAD's research impact in distributed computing and IoT integration.

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

Krzysztof J. Kochutمشاهده پروفایل

استاد

• Knowledge Graphs
• Ontologies
• Semantic Web
• +۷ مورد دیگر

Krzysztof J. Kochut is a Professor in the School of Computing at the University of Georgia. His career at UGA spans from Limited Term Assistant Professor (1987–1988) to his current professorial role since 2001. He holds a Ph.D. in Computer Science from Louisiana State University (1987) and an M.S. from the University of Warsaw (1982). Education: Ph.D., Computer Science, Louisiana State University (1987) M.S., Computer Science, University of Warsaw (1982) His research focuses on knowledge graphs, ontologies, semantic web technologies, bioinformatics, distributed systems, and computational intelligence. He has developed frameworks for knowledge graph embedding, entity summarization, and semantic resource federation. Recent publications highlight knowledge graph mining, random walk-based embeddings, and relation prediction using large language models. Funded by NIH and NSF, his work addresses challenges in biomedical data integration and dark proteome annotation. Scientific Awards: Student Career Success Influencer Award 2024 Student Career Success Influencer Award 2022 Outstanding Faculty Service Award 2016 Best Paper Award 2015 Professor Kochut has received grants for projects like Annotating dark ion-channel functions using knowledge graph mining (NIH, 2021–2025) and Integrated Technology Resource for Biomedical Glycomics (NIH, 2002–2008). He leads the development of tools such as GTXplorer for glycosyltransferase analysis and participates in semantic web initiatives.