جست‌وجوی استاد و پژوهشگر

Ciaran McCreeshمشاهده پروفایل

پژوهشگر ارشد

• Graph Theory
• Combinatorial Optimization
• Constraint Programming
• +۵ مورد دیگر

Ciaran McCreesh is a Research Fellow in the School of Computing Science at the University of Glasgow. His research focuses on solving hard combinatorial problems in practice, particularly in graph theory and subgraph finding, leveraging symbolic AI techniques like constraint programming and Boolean satisfiability. He explores closing the gap between theoretical worst-case complexity and practical performance through empirical algorithmics and computational experiments. His work also addresses algorithm reliability via proof logging, parallel hardware exploitation (bit-parallelism, multi-core computing), and algorithm engineering to improve solver accessibility. Research interests: Combinatorial optimization, constraint programming, proof logging, parallel computing, algorithm reliability. Developed the Glasgow Subgraph Solver, a constraint programming-based tool for hard subgraph isomorphism problems. Publications span graph computation models, certified solvers, and algorithmic advancements. His work emphasizes practical algorithmic improvements with rigorous validation methods. Supervised students include Matthew McIlree and José Antonio Rodríguez Bacallado.

Jason Mardenمشاهده پروفایل

استاد

• Game Theory
• Control Systems
• Distributed Optimization
• +۵ مورد دیگر

Jason Marden is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). His research focuses on feedback control, game-theoretic methods for distributed systems, and applications in traffic routing, resource allocation, and sensor networks. He holds a PhD and BS in Mechanical Engineering from UCLA. Education: PhD and BS in Mechanical Engineering, University of California, Los Angeles His work bridges systems theory and game theory to address coordination challenges in large-scale decentralized systems. Key areas include strategic resource allocation, adversarial scenarios (e.g., security games), and multi-agent optimization under uncertainty. Recent contributions explore the efficacy of collaborative equilibria, the impact of information asymmetry in contests, and optimal control strategies for energy systems like industrial refrigeration. His research has been supported by grants from the National Science Foundation (NSF), Office of Naval Research (ONR), and Air Force Office of Scientific Research (AFOSR). Awards: IEEE Fellow, NSF Early CAREER Award, ONR Young Investigator Award, AFOSR Young Investigator Award, Eckman Award (AACC). His publications emphasize quantifying system performance metrics such as price of anarchy and designing robust utility functions to ensure efficient distributed outcomes. Current efforts include advancing decentralized learning algorithms and optimizing adversarial defense strategies in networked systems.

Giovanni Steaمشاهده پروفایل

دانشیار

• Quality of Service
• Wireless Networks
• Network Calculus
• +۳ مورد دیگر

Giovanni Stea is an Associate Professor at the Department of Information Engineering within the College of Engineering at the University of Pisa. His research focuses on Quality of Service, resource allocation in wireline and wireless networks, performance evaluation through simulation and analytical techniques, and traffic engineering. Dr. Stea's research spans network calculus, wireless systems, and telecommunications infrastructure. His recent work demonstrates strong emphasis on 5G/6G networks, AI integration in telecommunications, and optimization of network performance. Key trends include: Advancements in network calculus methodologies Innovations in mobile edge computing applications Development of trustworthy AI systems for next-generation networks Cross-disciplinary approaches combining telecommunications and machine learning His publications consistently address computational efficiency in network analysis and practical implementations of theoretical models in real-world telecommunications environments.

Sridhar Radhakrishnanمشاهده پروفایل

استاد

• Computer Networks
• High Performance Computing
• IoT and SDN Development
• +۵ مورد دیگر

Dr. Sridhar Radhakrishnan is a Professor and former Director of the School of Computer Science at the University of Oklahoma since 1990. He holds a PhD from Louisiana State University and multiple undergraduate degrees in Computer Science and Physics. His research focuses on network protocols, IoT/SDN development, high-performance computing, and algorithm design. He has led NSF-funded projects like the OneOklahoma Friction Free Network and TRAMS transit management system. With over 100 publications, his work bridges theoretical computer science with practical applications in critical infrastructure protection, graph compression, and adversarial network defense. Active in IEEE and ACM, he reviews for major conferences and advises on federal transportation and cybersecurity initiatives. Education: PhD, Computer Science, Louisiana State University (1990) M.S., System Sciences, Louisiana State University M.L.I.S., Library and Information Services, Louisiana State University B.S., Computer Science, University of South Alabama B.S., Physics, Vivekananda College, University of Madras Research Interests: His work spans network protocol development, RFID systems, parallel computing architectures, and resilience against weaponized disinformation. Recent breakthroughs include optimization of infrastructure networks under adversarial conditions and scalable graph compression techniques for massive datasets. Grants & Projects: NSF OneOklahoma Friction Free Network (2014-2016) Federal Transit Administration TRAMS System (2012-Current) PARA-TRANSIT Operations Technology (2005-2010) Awards & Recognition: While no formal awards listed, his sustained federal funding and high-impact publications reflect industry esteem. Lab & Teams: Leads research groups focused on IoT/SDN integration and network resilience, collaborating with federal agencies like FHWA and ODOT.

Stephen Wrightمشاهده پروفایل

استاد

• Computational Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Stephen Wright is a Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, serving as Department Chair from 2023-2025. He previously held roles at Argonne National Laboratory (1990-2001) and the University of Chicago (2000-2001). His research focuses on computational optimization, with applications in data science, machine learning, and engineering. He co-authored seminal books such as Numerical Optimization (with J. Nocedal) and Optimization for Data Analysis (with B. Recht). Wright leads the Wisconsin Institute for Discovery's research initiatives and has developed widely-used optimization software like PCx and SpaRSA . Key awards include the 2024 George B. Dantzig Prize, 2020 Khachiyan Prize, and SIAM Fellow status since 2011. He has served as editor-in-chief of the SIAM Journal on Optimization and Mathematical Programming, Series B . His teaching includes courses on nonlinear optimization (CS726) and introductory optimization (CS524). Wright’s work bridges theory and practice, emphasizing scalable algorithms and interdisciplinary applications.

• Distributed Computing
• Self-Stabilizing Algorithms
• Robotics
• +۳ مورد دیگر

Stéphane Devismes is a Professor at Université de Picardie Jules Verne and a member of the ALCO: Algorithmics and Complexity team within the MIS Lab (UR 4290). His research focuses on Distributed Computing , Self-Stabilizing Algorithms , Robotics , and Network Security . He has contributed extensively to algorithmic solutions for grid exploration by autonomous robots, leader election in dynamic networks, and certified self-stabilization frameworks. Keywords : Distributed Systems, Self-Stabilization, Robotics, Algorithmics, Complexity, Security His recent work trends include self-stabilizing unison in directed networks, infinite grid exploration by myopic robots, and certified algorithm analysis for worst-case stabilization times. Notable projects include the SASA simulator for self-stabilizing algorithms and the SR3 secure routing protocol. Devismes has received prestigious awards including the Wilkes Award 2022 and Best Student Paper Awards at ICDCN'2023 and SSS'2013. He has supervised numerous PhD and Master's theses, including those of Geoffrey Defalque and Anaïs Durand, and actively participates in international program committees and grant-funded research like the ANR SkyData project.

Thomas Courtadeمشاهده پروفایل

دانشیار

• Information Theory
• Data Science
• Network Communication
• +۵ مورد دیگر

Thomas Courtade is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined Berkeley in 2014 after a postdoctoral fellowship at Stanford University, supported by the NSF Center for Science of Information. His research focuses on information theory, data science, and their intersections with machine learning and privacy-preserving algorithms. Education: Ph.D. in Electrical Engineering, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, University of California, Los Angeles (2008) B.Sc. in Electrical Engineering, Michigan Technological University (2007, summa cum laude) Research Interests: Information Theory and its applications to network communication Privacy-preserving data analysis and differential privacy Statistical estimation under heterogeneous privacy constraints Optimization in distributed systems and market design Functional inequalities (Brascamp-Lieb, Poincaré-Korn) Machine learning with emphasis on model robustness and efficiency Awards and Fellowships: Electrical Engineering Award for Outstanding Teaching (2020) Hellman Fellow (2016) Advising and Grants: Supervised no listed students (student names not provided in text) Recipient of NSF CAREER Award (2018) Labs and Collaborations: Berkeley Laboratory for Information and System Sciences (BLISS) Center for Theoretical Foundations of Learning, Inference, and Mathematics (CLIMB)

Chen Dingمشاهده پروفایل

استاد

• Computer memory
• Locality theory
• Cache optimization
• +۵ مورد دیگر

Chen Ding is a Professor and Chair of the Computer Science Department at the University of Rochester, where he leads research in computer memory systems and locality theory. His work focuses on optimizing memory hierarchy performance across computing platforms from handheld devices to supercomputers. Ph.D. from Rice University (2000) M.S. from MTU (1996) B.S. from Beijing University (1994) Professor Ding's research centers on the scientific foundation of computer memory, particularly locality theory and its applications for minimizing data movement - the primary bottleneck in modern computing systems. His work has established that data movement, data reuse, and working set are mathematically related manifestations of the same underlying phenomenon. His research spans parallel program locality, data movement complexity, relational theory of locality, reference affinity, and whole-program locality analysis. His recent publications demonstrate a continued focus on memory system optimization, with particular emphasis on lease caching, symmetric locality modeling, and continuous-time analysis of Zipfian workloads. His work bridges theoretical foundations with practical implementations across CPU/GPU cache modeling, cache sharing optimization, and key-value memory caching systems. NSF CAREER award (2003) DOE Young Investigator Award (2002) Professor Ding has supervised numerous graduate students, including Lavaee who contributed to the "Hardness of data packing" research presented at POPL'16. His work has received consistent funding from NSF and DOE, supporting his research in memory systems and parallel programming. He has served in leadership roles including General Chair for ISMM 2020 and committee positions for major conferences like PLDI and PPoPP. His research group maintains the roclocality.org resource and develops tools like SLO (suggestions of locality optimizations) for analyzing and improving program locality. The group collaborates with industry partners including Microsoft Research, where Ding served as a Visiting Researcher, and academic institutions worldwide.

• Real-Time Systems
• Embedded Systems
• Scheduling Theory
• +۳ مورد دیگر

Ahmed Rahni serves as a Research Fellow at ISAE-ENSMA, affiliated with the Laboratory of Engineering Applications of Signaling and Systems (LIAS) within the Real Time Team. His work centers on real-time scheduling theory and response time analysis for transaction-based embedded systems. He earned his PhD in Networks and Telecommunications from the University of Poitiers in 2008 with the thesis "Contributions to the validation of real-time scheduling in the presence of transactions under fixed priorities and EDF". Rahni's research focuses on Real-Time Systems, Embedded Systems, and Scheduling Theory. His expertise includes feasibility analysis of real-time transactions, worst-case response time computation under fixed priority and EDF scheduling, and offset-aware scheduling for time-critical applications. His work addresses fundamental challenges in ensuring temporal correctness for complex real-time environments. His publication history reveals consistent contributions to real-time scheduling theory from 2006-2012, with emphasis on analytical methods for transaction processing systems. Key trends include comparative analysis of schedulability techniques, development of efficient response time computation frameworks, and extension of scheduling theory to handle transaction dependencies and offsets. No scientific awards were documented in the provided materials. The available information indicates no formal student advisement or research grants were recorded in the source text. As an active member of LIAS's Real Time Team, he contributes to the laboratory's research infrastructure spanning both ISAE-ENSMA (Chasseneuil) and ENSIP (Poitiers) locations, focusing on theoretical and applied real-time systems engineering.