جست‌وجوی استاد و پژوهشگر

Nysret Musliuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.

Rosina M. Georgiadisمشاهده پروفایل

دانشیار

• Biomolecular Binding
• Surface Chemistry
• Analytical Chemistry
• +۴ مورد دیگر

Rosina M. Georgiadis is an Associate Professor in the Department of Chemistry at Boston University. Her research focuses on developing experimental tools to characterize biomolecular binding at surfaces and in solution, employing techniques such as surface plasmon resonance (SPR) spectroscopy, surface acoustic wave (SAW) sensing, and microscale thermophoresis (MST). She also investigates electronic structure at electrochemical interfaces using non-linear optics. Prof. Georgiadis is renowned for her innovative teaching methods, including the development of cloud-enabled lab courses that allow student access to instrument software from anywhere. This approach was recognized with the 2019 Gerald and Deanne Gitner Family Award for Innovation in Teaching with Technology. Her educational contributions include pioneering the CH303 advanced instrumental methods course and integrating virtual machines for lab instruction. These innovations were highlighted in Chemical & Engineering News (2017) and Analytical and Bioanalytical Chemistry (2018). She holds a B.S. in Chemistry (with Honors) from Indiana University (1982) and a Ph.D. in Physical Chemistry from the University of California, Berkeley (1988). Research interests span biomolecular dynamics, nanoparticle interactions, and surface chemistry. Her work bridges fundamental science with practical applications in diagnostics and instrumentation. She is currently scaling her cloud-based teaching model to other disciplines and institutions, emphasizing accessibility and equity in STEM education. Awards include the Gitner Award (2019) and recognition at Boston University’s Celebration of Teaching, Research, and Scholarship. Her lab, the Georgiadis Group, collaborates on interdisciplinary projects involving bioanalytical methods and educational technology.

• Distributed Machine Learning
• Edge Computing
• Federated Learning
• +۲ مورد دیگر

Christos Anagnostopoulos is Reader (Associate Professor) in Distributed Computing & Data Engineering Systems at the University of Glasgow, where he directs the MSc Information Technology and MSc Software Development programmes. His research focuses on distributed computing, federated learning, and edge intelligence for large-scale systems. Research interests span: Distributed ML/AI for edge environments Federated learning optimization Proactive data management in pervasive systems Edge-centric predictive analytics Funded projects include EU Horizon grants TERRA, COIN-3D, ELLIE, and TRACE, focusing on intelligent platforms for climate services, 3D VLSI reliability, cultural heritage technologies, and logistics optimization. Additional support comes from EPSRC, Royal Academy of Engineering, and industry partners including BMW and NXP. Recent publications (2024-2012) demonstrate innovations in edge-based task allocation, federated learning efficiency, distributed query processing, and mobile edge offloading. Article trends show consistent development of resource-aware distributed intelligence algorithms. Serves as Editor-in-Chief of Open Computer Science and Associate Editor of IEEE Access, and General Chair of IEEE ICDCS 2025. Leads the Knowledge and Data Engineering Systems research group.

• Industrial Internet of Things
• Digital Manufacturing
• Industry 4.0
• +۳ مورد دیگر

Prem Prakash Jayaraman is Professor and Director of the Factory of the Future and Digital Innovation Lab at Swinburne University, leading digital transformation initiatives for manufacturing and smart cities. His research develops IoT architectures for industrial applications with over $7.5M in external funding. Key projects include the Advanced Manufacturing Industry 4.0 Hub providing SME digital readiness assessments, 5G-enabled smart city solutions for road maintenance, and industrial IoT systems for quality control. His research spans edge computing, digital twins, and cyber-physical systems with applications in food processing, logistics, and healthcare. Professor Jayaraman has published extensively on IoT middleware, sensor sharing marketplaces, and quality-aware autonomous systems. He leads Swinburne's strategic partnerships in Industry 4.0 and contributes to national initiatives through ARC-funded research hubs on digital manufacturing.

Zomaya Albertمشاهده پروفایل

عضو هیئت علمی

• Complex systems
• Parallel and distributed computing
• Sustainable computing
• +۷ مورد دیگر

Dr. Zomaya is the Chair Professor of High Performance Computing & Networking and Founding Director of the Centre for Distributed & High Performance Computing at the University of Sydney, Australia. He has held leadership roles including Deputy Head of School and Head of School at The University of Western Australia and the University of Sydney. A Fellow of AAAS, IEEE, and IET, he leads research in distributed computing systems, sustainable computing, and edge intelligence. His research spans parallel/distributed computing, energy-efficient systems, and cloud/edge technologies. He has published over 600 papers, authored/edited 20+ books, and secured $22M+ in funding from ARC, Cisco, IBM, and others. He currently supervises 12 PhD students and has mentored 35 PhD completions. Key awards include the ACM MSWIM 2017 Fessenden Award and IEEE Technical Achievement Award (2014). He serves as Editor-in-Chief of IEEE Transactions on Sustainable Computing and has chaired over 700 conferences. His current projects focus on data analytics over cloud/edge platforms and holistic energy-aware scheduling for distributed systems.

• Operations Research
• Combinatorial Optimization
• Operational Research
• +۳ مورد دیگر

Edmund Burke is a Professor and Vice Chancellor at Bangor University. His research focuses on operations research, combinatorial optimization, and algorithm development, with notable contributions to scheduling, hyper-heuristics, and airport ground movement optimization. He has published extensively on topics such as constraint propagation, routing algorithms, and multi-objective optimization. Research interests include algorithmic approaches for real-world problems, such as airport slot allocation, maintenance crew scheduling, and timetabling. His work often integrates heuristics and metaheuristics to tackle complex optimization challenges. Recent publications explore cutting-edge techniques like memetic algorithms for ground movement and hyper-heuristics for nurse rostering. Burke has contributed to interdisciplinary projects, including collaborations with Danish railways and airport logistics systems. He actively participates in university administration, organizing alumni events and engaging in outreach activities such as exhibitions and community days. His academic leadership roles underscore his commitment to both research excellence and institutional governance.

Chase Murrayمشاهده پروفایل

دانشیار

• Operations Research
• Autonomous Vehicles
• Vehicle Routing & Logistics
• +۵ مورد دیگر

Chase Murray is an Associate Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (SUNY). He directs the SOAR Drone Facility and Optimator Lab , focusing on operations research applications for autonomous systems. His work includes drone logistics, routing optimization, and AI integration in delivery networks. Education: PhD, Industrial & Systems Engineering, University at Buffalo, 2010 ME, Industrial Engineering, Texas A&M University, 2000 BS, Industrial Engineering, Texas A&M University, 1998 Research Interests: Dr. Murray’s research emphasizes leveraging autonomous vehicles for logistics and surveillance. Key areas include: - Routing algorithms for drone-truck systems - Weather and traffic impact modeling - Integer programming for problem classification - FAA-certified drone pilot with hands-on SOAR facility testing . Publications: Over 30 peer-reviewed articles focus on drone coordination, vehicle routing, and optimization frameworks. Recent work includes graph-based ILP classification and weather-aware delivery systems. Labs & Software: SOAR : Drone test facility with 3D digital twin simulations Optimator Lab : Develops tools like VeRoViz (routing visualization) and tex2solver (LaTeX-to-code translator) Open-source projects: Ground Control Station software, ROS-enabled web interfaces Grants & Advising: Advised PhD students Ritwik Raj (traffic congestion modeling) and Lan Peng (weather-impact scheduling). Active in collaborative projects on AI-driven logistics systems.

• Mining Technology
• Electrical Engineering
• Optimization
• +۳ مورد دیگر

Dr. Hirad Assimi is a Grant-Funded Researcher A in the School of Electrical and Mechanical Engineering at the University of Adelaide, affiliated with the Faculty of Sciences, Engineering and Technology. His research focuses on multidisciplinary engineering solutions to enhance efficiency, reduce operational costs, and advance sustainable technologies. He holds a PhD from the Integrated Mining Consortium, where he addressed real-world challenges in mining technologies, particularly stockpile management. Currently, he leads the Mine Operational Vehicles Electrification (MOVE) project, funded by the Future Battery Industries Cooperative Research Centre (FBICRC), aiming to transition mining operations toward battery-supported systems. His work bridges academic research with industrial applications, emphasizing optimization, renewable integration, and electrification in mining. Research Interests: Mining Technology Optimization, Sustainable Energy Systems, Electrification of Mining Fleets, Multi-objective Optimization Algorithms, and Process Automation in Mining Operations. His publications span optimization techniques (e.g., particle swarm algorithms) and applications in mechanical engineering design. Advisory Role: Eligible to supervise Masters and PhD students as a Co-Supervisor. Grants: MOVE project funding from FBICRC. Collaboration: Active in the MOVE project team, developing decision-making tools for mining electrification.

Miguel Mosteiroمشاهده پروفایل

استاد

• Algorithms
• Distributed Computing
• Internet of Things
• +۷ مورد دیگر

Miguel Mosteiro is a Professor and Chair of the Computer Science Department at Seidenberg School of Computer Science and Information Systems, Pace University. He earned his PhD and MS in Computer Science from Rutgers University and holds a Bachelor of Engineering in Electronics from Universidad Tecnologica Nacional, Argentina. Research Interests Theoretical and experimental algorithms Distributed computing in constrained environments Machine learning and game theory applications Crowd/cloud computing and radio networks His recent publications focus on distributed algorithms for anonymous networks (2024), vehicular networks (2024), and information dissemination in wireless systems (2022-2023). He co-authored the ICALP 2018 best paper and served as keynote speaker at Fun with Algorithms 2022. Scientific Awards 2016 Faculty Research Mentor of the Year (Kean University) 2018 ICALP Best Paper Award 2022 Fun with Algorithms Keynote Speaker Grants include NSF-funded projects on IoT algorithms, crowd computing security, and anonymous network computations. He advises undergraduate and graduate students, including 2016 Undergraduate Researcher of the Year Maitri Chakraborty.

• Learning to optimize
• Optimization under uncertainty
• Efficient logistics and transport
• +۴ مورد دیگر

Prof. Dr. Kevin Tierney is a Full Professor for Decision and Operation Technologies at Bielefeld University's Faculty of Business Administration and Economics. He also serves at the Department of Management Science & Business Analytics and is affiliated with the Bielefeld Center for Data Science (BiCDaS) and Center for Uncertainty Studies (CeUS). Chair of Business Administration, Decision and Operation Technologies Member of BIGSEM Graduate School PhD (2013) - IT University of Copenhagen Sc.M. (2010) & BS (2008) - Brown & RIT Research Interests His work focuses on: Learning to Optimize: Using deep reinforcement learning to automate solution heuristics for complex problems like routing and scheduling. Optimization under Uncertainty: Developing models that incorporate probabilistic elements for decision-making in unpredictable environments. Efficient Maritime Logistics: Specializing in container shipping, terminal operations, and fleet routing with real-world constraints. Recent publications demonstrate expertise in algorithm configuration, constraint programming, and machine learning applications to logistics challenges. Scientific Recognition Distinguished Paper Award - European Conference on Artificial Intelligence (2020) Projects & Grants Principal Investigator in projects: Self-learning methods with Deep Reinforcement Learning (DFG 2026) itsowl-MOVE (Land NRW 2024) AIPlan4EU Meta-planning engine (EU H2020 2023) Academic Leadership Module responsible for: Quantitative Business Administration Data Science Production and Operations Management

Virginie Galtierمشاهده پروفایل

پژوهشگر

• Distributed Computing
• Grid Computing
• Parallel Processing
• +۵ مورد دیگر

Virginie Galtier is a Researcher at the Laboratoire Lorrain de Recherche en Informatique et ses Applications (LLLIA), affiliated with the University of Lorraine. Her work focuses on distributed computing systems, fault-tolerant architectures, and co-simulation methodologies. She has contributed to projects involving grid computing, UAV swarm systems, and hybrid co-simulation frameworks such as DACCOSIM and MECSYCO. Her research integrates interdisciplinary approaches combining computer science with fields like financial engineering, robotics, and energy systems. Key technical contributions include: Development of fault-tolerant distributed frameworks using JavaSpace Co-simulation solutions for FMI-compliant systems Resource prediction in heterogeneous active networks Recent work emphasizes application-driven research in cyber-physical systems (CPS), particularly using UAV swarms for safety-critical applications and smart space heating modeling. Galtier collaborates extensively with academia and industry on projects requiring distributed simulation platforms and parallel processing techniques. Her technical contributions span: Parallel algorithms for large-scale grid computing Middleware design for reconfigurable distributed systems Optimization of resource utilization in heterogeneous environments She actively participates in conference proceedings and maintains close ties with the MECSYCO and DACCOSIM co-simulation ecosystems. Current research directions include advancing hybrid co-simulation interoperability and exploring adaptive frameworks for dynamic computing environments.

James Harbinمشاهده پروفایل

پژوهشگر ارشد

• Real-Time Systems
• Distributed Systems
• Automated Software Engineering

James Harbin is a Research Associate in the Department of Computer Science at the University of York, UK, actively contributing to the Real-Time and Distributed Systems research group and the Automated Software Engineering research group. His office is located in CSE/016 on the Deramore Lane campus in York, with contact facilitated exclusively through the department's web-based email form to prevent spam exposure. His research spans critical domains in modern computing: real-time systems focus on timing-critical applications requiring precise scheduling and fault tolerance in embedded environments, while distributed systems research addresses coordination challenges across networked architectures. Concurrently, his work in automated software engineering develops methodologies for streamlining software development lifecycles through code generation, testing automation, and program analysis techniques. These intersecting fields drive innovation in cyber-physical systems, IoT infrastructure, and large-scale distributed services where reliability and efficiency are paramount. As a core member of both research groups, Harbin participates in the Department of Computer Science's mission to advance foundational computing research. The Real-Time and Distributed Systems group specializes in timing assurance for safety-critical applications, while the Automated Software Engineering group pioneers tools for software verification and maintenance automation. His position enables direct collaboration with faculty and researchers on projects bridging theoretical frameworks with practical implementations in resource-constrained environments.

Adam Wiermanمشاهده پروفایل

استاد

• Sustainable Computing
• Online Algorithms
• Optimization
• +۴ مورد دیگر

Adam Wierman is the Carl F. Braun Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). He holds academic roles including Professor (2012–2024), Braun Professor (2024–present), and served as Executive Officer (2015–2020), Director of Information Science and Technology (2016–2025), and Associate Director (2015–2016). His research focuses on designing sustainable and resilient networked systems through advances in machine learning, optimization, control, and economics. Key applications include data centers, electricity grids, and transportation systems. Education: B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University (2001–2007). His work bridges theoretical foundations and practical deployment, emphasizing provably efficient algorithms and market mechanisms. Recognitions include IEEE Fellow, ACM Distinguished Member, and the Northrop Grumman Prize for Excellence in Teaching. Research Interests: Sustainable Computing, Online Algorithms, Optimization, Control Systems, Network Economics, and Applied Probability. His lab develops tools for robust voltage control, carbon-aware scheduling, and scalable reinforcement learning for multi-agent systems. Recent projects include SustainGym, a reinforcement learning benchmark for sustainability tasks. Teaching: Courses include Networks: Structure & Economics, Projects in Networking, and Computer Science Education in K-14. He actively mentors graduate students and postdocs in areas like learning-augmented control and smart grid systems. Affiliations: Member of Caltech’s RSRG (Resilient Sociotechnical Systems Research Group), DOLCIT (Dynamical Learning for Optimization and Control), CSIS (Center for Social and Information Sciences), and CMI (Computation and Mathematical Sciences Institute).

Ruhai Wangمشاهده پروفایل

استاد

• Satellite Networks
• Delay-Tolerant Networks
• Space Communications
• +۳ مورد دیگر

Dr. Ruhai Wang is a Professor in the Phillip M. Drayer Department of Electrical and Computer Engineering at Lamar University. He holds a Ph.D. in Electrical/Computer Engineering from New Mexico State University and leads research in space communication networks. His office is located in the Cherry Building (Rm. 2205), and he can be contacted at rwang@lamar.edu or (409) 880-1829. Research Focus: Dr. Wang specializes in Delay-/Disruption-Tolerant Networks (DTN), satellite/space communications, cybersecurity, and wireless ad hoc networks. His work bridges theoretical modeling and practical implementations for space Internet systems, with ongoing projects focused on DTN protocol development for NASA collaborations. Publication Trends: His 15 most recent publications (2016-2020) primarily analyze protocol optimization for space communications, with emphases on Licklider Transmission Protocol (LTP), Bundle Protocol efficiency, deep-space channel modeling, and integrated terrestrial-satellite networks. Key themes include disruption resilience, transmission timing optimization, and network scalability under asymmetric conditions. Awards and Honors: Distinguished Lecturer, IEEE AESS (2025) Teaching Board Member, Ph.D. Program in STIET, University of Genova (2018-Present) Best Associate Editor, IEEE Aerospace & Electronics Systems Magazine (2015) Lamar University Presidential Faculty Fellowship (2015) IEEE ComSoc Best Tutorial Paper Nominee (2009) University Merit Award (2007) Advising and Projects: Currently mentoring doctoral candidates Alaa Sabbagh and Hacer Varol, and MSEE students Qinglin Xie and Arkun Zhuang. His primary project involves developing DTN protocols for space networks through a NASA/JPL-collaborated testbed. Grant-supported research focuses on reliable data delivery in deep-space environments. Professional Service: Senior IEEE member (AESS, ComSoc), Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Aerospace & Electronics Systems Magazine. Former TPC co-chair for IEEE ICC and organizer of international workshops on satellite communications.

• Operations Research
• Mathematical Programming
• Project Scheduling
• +۷ مورد دیگر

Francisco Felipe Ballestín González is an Associate Professor in the Department of Mathematics at the Faculty of Economics, University of Valencia. His research is centered on Operations Research, with a focus on resource-constrained project scheduling, optimization models, and heuristic algorithms. University: University of Valencia School: Faculty of Economics Department: Department of Mathematics Email: francisco.ballestin@uv.es His research interests include Operations Research, Mathematical Programming, Project Scheduling, Heuristics, Metaheuristics, and Optimization. He is a member of the OMMEE research group, dedicated to Optimization and Mathematical Models for Economics and Business. The 15 most recent publications analyzed reveal a consistent focus on project scheduling under resource constraints, with applications in production, healthcare, and logistics. His work frequently involves hybrid and evolutionary algorithms, justification techniques, and modeling of preemption and time lags. He has contributed significantly to the theoretical and practical understanding of RCPSP variants. No scientific awards are mentioned in the provided text. There is no information available about student advising or research grants. However, his extensive collaboration with researchers such as Vicente Valls, Sacramento Quintanilla, and Roel Leus indicates active research engagement. He is affiliated with the OMMEE research group, which focuses on developing mathematical models for economic and business applications, particularly in scheduling and optimization.