جست‌وجوی استاد و پژوهشگر

Fabio Fagnaniمشاهده پروفایل

استاد

• Dynamical systems over large-scale networks
• Distributed control and estimation
• Opinion dynamics
• +۷ مورد دیگر

Fabio Fagnani is a Full Professor of Mathematical Analysis at the Department of Mathematical Sciences (DISMA) , Politecnico di Torino , Italy. He is a leading researcher in network dynamics, distributed control, and game theory, with a strong record of supervising doctoral students and leading national and international research projects. Education & Academic Background: Full Professor of Mathematical Analysis, DISMA, Politecnico di Torino 2017 Petar Kokotovic Distinguished Professor, UCSB 2019–2020 Leverhulme Visiting Professor, Royal Holloway University of London Research Interests: His research spans dynamical systems over large-scale networks , including cooperative algorithms for distributed control, estimation, and synchronization. He investigates opinion dynamics and epidemic spreading in networks, with a focus on minority influence, leadership, and heterogeneity. He also explores game theory over networks and learning algorithms in multi-agent systems. Research Trends from Publications: Recent work focuses on epidemic modeling (SIR/SIS dynamics), opinion dynamics with stubborn agents, and control of networked systems . His publications in IEEE Transactions on Control of Network Systems , Artificial Intelligence , and IEEE Control Systems Letters reflect a deep engagement with both theoretical and applied aspects of network science, including AI-driven pathfinding, coevolutionary models, and resilience analysis. Scientific Awards: 2017 Petar Kokotovic Distinguished Professor 2019–2020 Leverhulme Visiting Professor ICAPS-2020 Best Paper Honorable Mention Award PhD Supervision & Research Leadership: He has supervised numerous PhD students including Davide Sipione , Sebastiano Messina , Martina Alutto , and Roberta Raineri . He leads the Analysis and Control of Network Systems research group and has been the Principal Investigator for projects such as SUP-CHAIN-DIS (2023–2025) and Distributed AI for future mobile networks (2020). Labs & Research Groups: He is affiliated with the Analysis and Control of Network Systems group at DISMA, Politecnico di Torino, and has collaborated with institutions such as UCSB, Royal Holloway University of London, and INRIA.

• Quality of Experience (QoE) of Internet applications
• Artificial intelligence and machine learning for communication networks
• Measurement and analytics of encrypted network traffic
• +۵ مورد دیگر

Prof. Dr. Michael Seufert is a Full Professor and Chairholder at the University of Augsburg since October 2023, leading the Chair of Networked Systems and Communication Networks within the Faculty of Applied Computer Science. Previously, he served as a Private Lecturer at the University of Würzburg and completed his Habilitation in Computer Science there in 2023. His academic journey includes a PhD in Computer Science from the University of Würzburg (2017) with a thesis on Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming, which earned him two prestigious Best Dissertation Awards. His research focuses on Quality of Experience (QoE) of Internet applications, artificial intelligence and machine learning for communication networks, measurement and analytics of encrypted network traffic, and data-driven proactive user-centric network management solutions. His work bridges theoretical foundations with practical applications in network monitoring, security, and performance optimization. Recent research has increasingly incorporated machine learning techniques to address challenges in network management, traffic analysis, and user experience assessment. Prof. Seufert's publication record shows a clear evolution from foundational work on QoE modeling and HTTP adaptive streaming toward more sophisticated applications of machine learning in network operations. His recent publications (2023-2025) demonstrate a strong emphasis on practical ML applications for network monitoring, security, and optimization, with particular attention to real-world constraints and performance requirements. The research spans multiple subfields including encrypted traffic analysis, explainable AI for network management, edge computing, and quality assessment for emerging applications. CNOM Young Professional Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) (2024) Best Dissertation Award 2018 of KuVS (Communication and Distributed Systems) special interest group Best Dissertation Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) Prof. Seufert actively supervises research projects and advises students through thesis projects and practical modules. His teaching portfolio includes Communication Systems, Management of Communication Networks, Practical Introduction to Internet-Technologies, and seminars on Networked Systems. He leads a research group focused on networked systems and communication networks, with current projects exploring machine learning applications in networking, as evidenced by the organization of the 3rd International Workshop on Machine Learning in Networking (MaLeNe 2025).

• Networking
• Machine Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

S. Boumerdassi is a researcher at the CEDRIC Laboratory of Conservatoire National des Arts et Métiers (CNAM) . He has been actively involved in interdisciplinary research spanning networking , machine learning , image encryption , and IoT systems since the early 2000s. His work has focused on security protocols, energy efficiency in data centers, and anomaly detection frameworks. Key Research Areas: Networking: VANETs, LoRaWAN, network anomalies, and edge computing. Security: Blockchain oracles, cryptographic protocols, and wireless sensor network security. Machine Learning: Applications in network optimization and sustainable agriculture. Image Encryption: Chaotic maps, DCT coefficients, and grain algorithms. Recent Publications highlight his contributions to federated learning, anomaly detection, and mobility models. Leadership and Collaboration He has co-organized conferences like Mobile, Secure, and Programmable Networking and contributed to edited volumes on machine learning and IoT. His collaborations include researchers from Springer, IEEE, and institutions across France, Italy, Spain, and Canada.

• Cryptography
• Secure Multi-Party Computation
• Blockchain
• +۳ مورد دیگر

Dr Aydin Abadi is a Research Fellow at Newcastle University specializing in cryptography and security systems. His research spans privacy-preserving technologies, blockchain architectures, and secure multi-party computation protocols, with active publication output from 2015 through 2025. His primary research domains include Cryptography, Secure Multi-Party Computation (particularly Private Set Intersection variants), Blockchain Applications, Privacy-Preserving Computation, Distributed Systems Security, and Federated Learning. He has pioneered protocols for verifiable delegated computation on outsourced datasets and developed novel approaches to Byzantine fault tolerance in distributed ledgers. Analysis of his 9 publications (2015-2025) reveals an evolutionary trajectory from foundational cryptographic protocols (2015-2019) toward applied blockchain systems and machine learning security (2023-2025). His recent work integrates cryptographic techniques with federated learning frameworks and develops commercial-grade blockchain alternatives, demonstrating consistent innovation in bridging theoretical cryptography with real-world system implementations.

D.H.J. Epemaمشاهده پروفایل

استاد

• Distributed Systems
• Resource Management
• Scheduling Algorithms
• +۳ مورد دیگر

D.H.J. Epema is a full Professor at the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), specializing in Data-Intensive Systems . His research focuses on resource management, scheduling algorithms, performance analysis, trust/reputation systems, and blockchain applications in distributed systems. Research Interests: Distributed Systems, Blockchain, IoT, and Trust Management Notable Awards: Best Paper Awards at CCGrid (2010, 2014), MASCOTS (2013), and LCN (2021) Recent Publications emphasize blockchain integration in IoT, energy trading platforms, and decentralized identity systems. His work bridges theoretical frameworks with practical implementations in large-scale systems. Editorial Roles: Editor for ACM (2013–present) and Lecture Notes in Computer Science (2009–present)

Xuyang Wuمشاهده پروفایل

دانشیار

• Distributed Optimization
• Large-scale Optimization
• Machine Learning
• +۳ مورد دیگر

Xuyang Wu serves as Associate Professor at the School of Automation and Intelligent Manufacturing (AiM), Southern University of Science and Technology (SUSTech) in Shenzhen, China. He was promoted from Assistant Professor in June 2025 after joining SUSTech in February 2024, following postdoctoral research at KTH Royal Institute of Technology (2020-2023) and a visiting position at The Chinese University of Hong Kong (2023-2024). His educational background includes: Ph.D. in Communication and Information Systems, University of Chinese Academy of Sciences (2015-2020) B.S. in Applied Mathematics, Northwestern Polytechnical University (2011-2015) Wu's research centers on distributed and large-scale optimization with applications in machine learning and networked systems. His work develops asynchronous algorithms that handle communication delays and coupling constraints, bridging control theory with modern machine learning. Key contributions include barrier function methods for safe resource allocation and delay-agnostic coordinate updates, emphasizing convergence guarantees in decentralized settings. Analysis of his 15 most recent publications (2021-2025) reveals consistent focus on asynchronous distributed optimization across top venues including IEEE Transactions on Automatic Control and ICML. His work demonstrates increasing sophistication in handling non-convex objectives and time-varying networks while maintaining theoretical convergence guarantees, with recent expansion into high-performance decentralized training frameworks. Scientific recognition includes: Best Student Paper Finalist at IEEE International Conference on Control & Automation (2019) While specific advised students and grant details aren't documented in available sources, Wu's publication record indicates active collaboration with researchers at KTH Royal Institute of Technology and The Chinese University of Hong Kong. His work shows clear progression from theoretical foundations toward practical distributed learning applications. Current research focuses on enabling violation-free optimization under constraints and developing communication-efficient decentralized training frameworks for real-world deployment scenarios.

Abigail Borronمشاهده پروفایل

دانشیار

• Agricultural Communication
• Youth Development
• Community Development
• +۴ مورد دیگر

Abigail Borron is an Associate Professor in the Department of Agricultural Leadership, Education & Communication at the University of Georgia's College of Agricultural & Environmental Sciences. Her work focuses on culture-centered communication strategies for community engagement, particularly in underserved populations, and she integrates research with extension outreach to address rural development challenges. Doctor of Philosophy in Youth Development and Agricultural Education, Purdue University (2012) Master of Science in Youth Development and Agricultural Education, Purdue University (2007) Bachelor of Arts in English, Indiana University-Purdue University, Fort Wayne (1999) Dr. Borron’s research emphasizes community resilience, rural health, and agricultural science communication. She employs frameworks like the Community Capitals Framework to assess social impacts, develop diagnostic tools, and evaluate communication strategies. Recent projects include analyzing rural stress, community coalitions during crises, and public dialogues on climate change and biotechnology. Her publications from 2020–2025 span topics such as community resource mapping, communication channel preferences, agricultural biotechnology outreach, and rural policy development. These works utilize methodologies like grounded theory, Q methodology, and participatory action research to inform extension programming and policy interventions. Dr. Borron’s collaborative efforts with colleagues like Alexa Lamm and Kevan Lamm have produced validated instruments for measuring personal agency and rural stress, contributing to both academic literature and practical extension tools. She has mentored undergraduate research, including a 2013 study on food insecurity narratives.

• Machine Learning
• Data Analytics
• Blockchain Technology
• +۷ مورد دیگر

Prof. Tahar Kechadi is a Principal Investigator at the Insight Centre for Data Analytics , specializing in Machine Learning & Statistics and Optimisation & Decision Analytics . His research spans interdisciplinary domains including agriculture, healthcare, and cybersecurity. University : Insight Centre for Data Analytics Role : Principal Investigator Ranks : Professor His current research focuses on applying machine learning to smart agriculture (e.g., crop yield prediction, data engineering), blockchain technologies (e.g., energy trading, e-voting), and privacy-preserving systems in healthcare and cybersecurity. Publications highlight advancements in multi-modal analysis , deep learning architectures , and game-theoretic clustering . Recent work explores data contamination in LLMs , privacy-aware blockchain systems , and distributed reputation management . His team develops tools for agro-climate modeling , medical diagnostics , and cloud forensic readiness . Contact: tahar.kechadi@insight-centre.org

• Applied Probability
• Stochastic Networks
• Random Graphs
• +۷ مورد دیگر

Professor A J Ganesh is a faculty member at the University of Bristol , holding the title of Professor of Applied Probability within the School of Mathematics, Statistical Science and is affiliated with the Probability, Analysis and Dynamics research group. He is also an active member of the Cabot Institute for the Environment , contributing to themes such as City Futures , Low Carbon Energy , and Natural Hazards and Disasters . Education B.Sc. – Indian Institute of Technology, Madras M.Sc. – (institution not specified) Ph.D. – Cornell University Research Interests Professor Ganesh’s work lies at the intersection of applied probability , stochastic networks , and network science . A recurring theme is understanding how randomness and local interactions give rise to global phenomena such as consensus, epidemics, or congestion. His investigations span: Consensus & Gossip Algorithms – analysing voter models and multi-agent bandits; Epidemic Processes & Rumour Spreading – quantifying thresholds, extinction times, and the impact of network topology; Random Graphs & Connectivity – soft geometric graphs, isolated nodes, and diameter questions; Queueing & Large Deviations – Cox/G/∞ queues, resource allocation, and delay-optimal scheduling; Cyber-Security & Intrusion Detection – leveraging variational autoencoders for anomaly detection; Game-Theoretic Resource Allocation – Pigouvian tolls, welfare optimality, and price of anarchy in parallel-server systems. Publication Trends Over the last decade Professor Ganesh has published extensively on collective decision-making (2025, 2024), machine-learning approaches to security (2023), and latency-sensitive communication (2022, 2019). Earlier work focused on epidemic thresholds , connectivity in random graphs , and large-deviations analysis of queues , demonstrating a consistent trajectory toward real-world applications of stochastic models. Scientific Awards & Recognition Author of the Springer Lecture Notes in Mathematics monograph Big Queues (2004) – a widely cited reference on large-deviations techniques in queueing theory. Supervision & Grants Professor Ganesh has 6 supervised works listed in institutional repositories, indicating ongoing Ph.D. or post-doctoral mentoring. While specific grant titles and amounts are not disclosed in the provided text, his sustained publication output and participation in EU and UK research networks (e.g., Horizon 2020, EPSRC) suggest active grant funding. Laboratories & Teams He collaborates closely with colleagues in the Cabot Institute for the Environment , applying probabilistic models to urban sustainability and disaster resilience. Cross-disciplinary partnerships include joint projects with engineers on connected and automated vehicles and with biologists on epidemic control strategies .

Khritankov Anton Sergeevichمشاهده پروفایل

دانشیار

• Machine Learning
• Software Engineering
• MLOps
• +۳ مورد دیگر

Khritankov Anton Sergeevich is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE) , where he started working in 2021. He holds a Candidate of Physical and Mathematical Sciences (2010) and a Master's degree (2007) and Bachelor's degree (2005) in Applied Mathematics and Physics from the Moscow Institute of Physics and Mathematics (State University) . Research Interests include machine learning , software engineering , MLOps , metamorphic testing , and feedback loops in ML systems . His work focuses on automating and ensuring reproducibility in data science, with a particular emphasis on blockchain verification and hidden feedback mechanisms that cause concept drift in AI systems. Publication Trends reveal a strong focus on MLOps platforms (2025), metamorphic testing for AI/ML systems (2024–2022), blockchain verification (2022–2021), and feedback loop modeling (2021–2020). His recent work addresses challenges in distributed computing and state registration of software developed with the AI Center project teams at HSE. Scientific Awards include a Bonus for publication in a List A journal (2024–2025) . Additional Contributions involve developing the SmartMLOps platform (2024–2025), co-authoring state-registered software frameworks (2024), and organizing roundtable discussions on reproducibility in data science (2021). He has taught Advanced Software Design to Master's students in Software Engineering since 2021.

• Software
• Intelligent informatics
• Control and system engineering
• +۴ مورد دیگر

Professor Shigeru Fujimura is affiliated with Waseda University as a faculty member of the Faculty of Science and Engineering , specifically in the Graduate School of Information, Production, and Systems . With a PhD in Engineering from Waseda University, his research focuses on intelligent informatics and system engineering , particularly in scheduling, optimization, and human-robot collaboration. His research spans multiple domains including: Deep Reinforcement Learning for combinatorial optimization Energy-efficient manufacturing systems Multi-agent collaboration frameworks Augmented Reality interfaces IoT business modeling Recent publications demonstrate expertise in graph neural networks , particle swarm optimization , and generative adversarial networks applied to industrial problems. He has received multiple awards including the Invention Encouragement Prize (2003) and IEEJ Paper Presentation Award (1994) .

Aris Filos-Ratsikasمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Artificial Intelligence
• Social Choice Theory
• +۱۱ مورد دیگر

Aris Filos-Ratsikas is a Lecturer (Assistant Professor) at the University of Edinburgh, School of Informatics, and a member of the Laboratory for Foundations of Computer Science. His research integrates theoretical computer science and economics, focusing on algorithms, computational complexity, social choice, fair division, game theory, and mechanism design. Education: PhD in Computer Science, Aarhus University, Denmark (2015) – Supervisor: Peter Bro Miltersen MSc – Advisor: Ioannis Caragiannis Research Interests: His work spans social choice theory , fair division , consensus halving , necklace splitting , PPA-completeness , distortion in voting and matching , and market algorithms . He develops new techniques combining convex optimization, topology, and query complexity to solve foundational problems in economics and computation. Recent Publication Trends: Recent papers focus on distortion minimization in social choice and matching under limited information, FIXP-completeness for fair-division problems like envy-free cake cutting, and randomized voting rules that balance explainability and efficiency. His work frequently appears in NeurIPS, FOCS, EC, AAAI, IJCAI, and SODA. Awards & Service: Fellow of the Higher Education Academy Teaching Assistant of the Year, Aarhus University (2014) Outstanding/Senior PC Member awards: AAMAS 2022, IJCAI 2018, AAAI 2017, IJCAI 2016 Program (co-)Chair: SAGT 2023; Area Chair: EC 2023; Senior PC: WINE, AAMAS, AAAI Teaching & Grants: He has designed and delivered large MSc modules (Advanced Algorithmic Techniques, Programming Fundamentals) at Liverpool, and taught Algorithmic Game Theory at Oxford. His tutorials on “Total Search Problems” and “Auctions & Markets” are regularly invited to international summer schools. He holds a fellowship recognizing excellence in higher-education teaching and has produced reflective scholarship on teaching theoretical courses. Workshops & Visits: He co-organizes the UK-wide “Indoor Games” workshop series and multiple international workshops on fairness, distortion, and total search problems. Long-term visits have included Oxford (hosted by Elias Koutsoupias) and Tsinghua University (hosted by Andrew Yao).

Daniel Hellwigمشاهده پروفایل

استاد

• Blockchain
• Supply Chain Management
• Production Management
• +۲ مورد دیگر

Dr. Daniel Hellwig serves as Professor and Chair of Production Management within the Supply Chain Management Group at WHU - Otto Beisheim School of Management in Vallendar, Germany. His academic expertise is complemented by his professional role as Principal at Kepler Cannon in New York since 2014, demonstrating a strong integration of theoretical research and practical business applications in the field of supply chain innovation. His educational background includes: M.Eng. from University of California at Berkeley (2013) B.S.E. from The Wharton School, University of Pennsylvania (2011) Dr. Hellwig's research focuses on blockchain technology applications in supply chain management, with particular emphasis on distributed ledger systems for trade finance, resource allocation, and supply chain resilience. His work bridges technical blockchain implementation with business process optimization, exploring how cryptographic techniques like fully homomorphic encryption can enable secure information sharing among competing retailers. He has developed innovative simulation frameworks such as the DISASTER token trading game to study supply chain behavior during crises, demonstrating practical applications of theoretical concepts. His publication trajectory reveals an evolution from foundational blockchain concepts to increasingly sophisticated supply chain applications. Early works established fundamental blockchain knowledge through comprehensive guides, while recent research focuses on behavioral aspects of blockchain-enabled markets and privacy-preserving technologies for supply chain collaboration. This progression demonstrates both technical depth and growing business relevance in his scholarly contributions. As an educator, Dr. Hellwig has been a consistent guest lecturer at WHU since 2018, teaching blockchain programming and introduction courses at both bachelor's and master's levels. His teaching approach emphasizes practical implementation alongside theoretical understanding, reflecting his dual expertise in academic research and professional consulting. He frequently collaborates with Goran Karlic and Professor Arnd Huchzermeier, suggesting strong research partnerships within WHU's Supply Chain Management Group.

Cyril Monnetمشاهده پروفایل

استاد

• Monetary Theory
• Financial Intermediation
• Macroeconomics
• +۲ مورد دیگر

Cyril Monnet is a full Professor at the Economic Institute of the University of Bern, Switzerland, with dual affiliations at the main university campus in Bern and the Study Center Gerzensee. His institutional roles include active participation in the World Trade Institute (WTI) and other research centers within the Economic Institute ecosystem. His research program centers on three core pillars: Monetary Theory (examining money's fundamental properties and functions) Financial Intermediation (analyzing banking structures and credit allocation) Macroeconomics (investigating systemic economic dynamics) Recent scholarly output reveals a pronounced focus on digital currency ecosystems, where he pioneers theoretical frameworks for central bank digital currencies (CBDCs), stablecoins, and distributed ledger systems. His work critically assesses privacy implications in digital payments, banking sector vulnerabilities under new monetary architectures, and optimal liquidity backstop designs. Methodologically, he integrates game-theoretic models with empirical validation, particularly in consensus mechanisms and truth-verification systems for decentralized finance. Professor Monnet's publication trajectory demonstrates deep engagement with real-world policy challenges, including Swiss National Bank liquidity operations, ECB Digital Euro implementation hurdles, and systemic risk mitigation in collateralized credit chains. His research consistently bridges theoretical rigor with actionable insights for central banking institutions globally. Based in office A316 at Schanzeneckstrasse 1, Bern, he maintains an active research presence through cyrilmonnet.ch while contributing to the University of Bern's leadership in monetary economics through the Economic Institute's collaborative framework with the Oeschger Center for Climate Change Research and Center for Regional Economic Development.

• Blockchain Technology
• Distributed Systems
• Wireless Sensor Networks
• +۳ مورد دیگر

C.U. Ileri is a researcher at Delft University of Technology's Data-Intensive Systems department within the College of Electrical Engineering, Mathematics and Computer Science. His work focuses on blockchain systems, distributed algorithms, and wireless sensor networks. Research Areas: Blockchain, Web3, Sybil attack mitigation, fault-tolerant algorithms, wireless sensor networks Key Contributions: Developed latency-based Sybil defense mechanisms, blockchain interoperability protocols (XChange), and capacitated graph algorithms for WSNs His publications span blockchain-based federated learning, zero-knowledge identity frameworks, and distributed graph optimization techniques. Recent work explores IOTA 2.0's finality guarantees and zkSSI's privacy-preserving capabilities. Collaborations include joint datasets with Q. Stokkink and J. Pouwelse, focusing on latency measurements and reputation system prototypes. Research outputs demonstrate expertise in balancing security, efficiency, and scalability in decentralized systems.