Find Academic Supervisors

• Databases and Information Systems
• Big Data
• Data Management
• +3 more

Dr.-Ing. Julia Othlinghaus-Wulhorst is a Researcher in the Department of Databases and Information Systems at the University of Würzburg, Faculty of Computer Science and Mathematics. She specializes in databases, data engineering, and Big Data. Her work focuses on query processing, data quality, and cloud resource optimization in distributed systems. Education: Diplom-Informatikerin (FH) (FH Diploma in Computer Science). Research Interests: Databases and Information Systems, Big Data, Data Management, Data Engineering, Data Science, Data Quality. She explores scalable database systems, data quality frameworks, and cloud-based resource allocation. Recent Work Trends: Her recent articles address large-scale distributed databases, data quality in Big Data environments, and cloud resource optimization. These reflect a focus on improving system efficiency and data reliability in modern computing landscapes. Awards: Best Paper Award at ICDE 2023 Best Paper Award at ICDE 2022 Outstanding Reviewer Award at ICDE 2022 Advising & Grants: Advises PhD students in data systems and participates in DFG-funded research projects. Active in collaborative initiatives on database optimization and Big Data analytics. Labs & Teams: Member of the Information Systems Lab and Big Data Analytics Group at the University of Würzburg.

Zev WoodstockView profile

Assistant Professor

• Optimization
• Nonlinear Analysis
• Signal Processing
• +9 more

Zev Woodstock is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU). His research focuses on nonsmooth optimization, splitting algorithms, machine learning, and monotone operator theory, with applications in data science, signal/image/audio processing, and large-scale optimization. He holds a PhD in Mathematics (2021) and MS in Applied Mathematics (2018) from North Carolina State University, and a BS in Mathematics (2016) from JMU. Previously, he was a postdoctoral researcher at the Interactive Optimization and Learning Lab at TU Berlin and Zuse Institute Berlin. Education PhD in Mathematics, North Carolina State University, 2021 MS in Applied Mathematics, North Carolina State University, 2018 BS in Mathematics, James Madison University, 2016 Research Interests Dr. Woodstock’s work spans optimization theory and applications, including convex/nonconvex optimization, fixed point algorithms, and signal recovery. He develops methods for solving nonlinear inverse problems using firmly nonexpansive operators, with contributions to variational inequality models, projection-based techniques, and block-iterative schemes. His applied work includes audio signal restoration, large-scale optimization, and machine learning infrastructure. Key Research Trends Recent publications emphasize efficient algorithms for linear minimization, asynchronous optimization frameworks, and robust signal recovery from inconsistent nonlinear data. His work bridges theoretical operator theory with practical applications in imaging, audio processing, and distributed computing. Awards and Grants Recipient of National Science Foundation Grants DGE-1746939 and CCF-1715671 for collaborative research in approximation theory and optimization. Labs and Collaborations Current affiliations include the Department of Mathematics & Statistics at JMU. Previously, he collaborated with the Interactive Optimization and Learning Lab at TU Berlin, focusing on convex optimization and algorithmic design.

JOSE RAMON RIOS VIQUEIRAView profile

Professor

• Spatial Data Management
• Environmental Data Integration
• Geographic Information Systems (GIS)
• +6 more

Jose Ramon Rios Viñuela is a Professor at the Department of Electronics and Computing, University of Santiago de Compostela, affiliated with the Higher Technical School of Engineering. He holds a PhD from the University of A Coruña (2003) for his thesis on spatio-temporal data management. His research focuses on spatial databases, environmental data integration, smart city technologies, geographic information systems (GIS), and spatio-temporal data modeling. Key projects include TRAFAIR (traffic-air quality analysis), COGRADE research group (computer graphics and data engineering), and GeoHbbTV frameworks for geospatial interactive media. He leads efforts in distributed spatial data processing, semantic interoperability, and sensor network integration for environmental monitoring. Notable contributions include frameworks like TAQE (traffic-air quality modeling), GeoSPARQL query systems, and MeteoSIX meteorological platforms. His work bridges earth sciences with public accessibility through smart environmental infrastructures, emphasizing citizen engagement and open data dissemination. Current research trends explore big data analytics for geospatial applications, federated environmental data systems, and low-cost sensor networks for urban sustainability. He collaborates on projects involving air quality forecasting (GRAL model), spatial join optimization in large data lakes, and semantic mediation of environmental observation systems. His academic contributions span over 60 peer-reviewed publications across GIS, database systems, and environmental informatics.

François OrieuxView profile

Assistant Professor

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +5 more

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.

Philippa JacksonView profile

Associate Professor

• Biological Psychology
• Nutrition and Brain Function
• Cognitive Neuroscience
• +4 more

Philippa Jackson is an Associate Professor of Biological Psychology and Director of the Brain, Performance and Nutrition Research Centre at Northumbria University. She serves as Impact Lead on the Research and Knowledge Exchange Committee in the Department of Psychology. Her research focuses on the relationship between dietary components (nutrients, whole foods, supplements) and brain function/behavior, particularly investigating omega-3 fatty acids' effects on cognition, sleep, stress, and well-being across the lifespan. She has expertise in conducting large-scale randomized controlled trials, collaborating closely with industry partners. Education: PhD in Psychology (2010), BSc (Hons) Psychology (2003). Research Interests: Bioactive lipids' neurocognitive impacts, individual differences in supplementation responses, and essential nutrients' role in sleep regulation. Current projects include investigating differential effects of omega-3 isomers (DHA vs. EPA) and developing nutritional interventions for cognitive enhancement. Student Supervision: Currently guiding PhD candidates Ben Hughes (gaming performance and Nepalese Pepper) and Jessica Greener (dietary phospholipids and brain health). Awards: None explicitly listed in provided text. Key Contributions: Over 10 peer-reviewed articles since 2021, with emphasis on plant extracts (mango leaf, coffeeberry), nutraceuticals, and marine oils' effects on human cognition and physiology. Active in translational research bridging nutritional science and clinical applications.

Eugene GartlandView profile

Professor

• Liquid crystal modeling
• Mathematical physics
• Numerical analysis
• +3 more

Eugene Gartland is a Professor in the Department of Mathematical Sciences at Kent State University, affiliated with the Advanced Materials and Liquid Crystal Institute. His research specializes in mathematical modeling of liquid crystals, focusing on electro-optical properties, stability analysis, and computational methods. Research integrates continuum mechanics, numerical analysis, and statistical physics to investigate phenomena like Fréedericksz transitions, director field instabilities, and phase behavior. Theoretical frameworks include Landau-de Gennes theory and variational methods, with applications to display technologies and soft matter systems. Publications demonstrate consistent methodological innovation in liquid crystal modeling, emphasizing numerical algorithms, bifurcation analysis, and experimental validation. Recent work explores electric-field interactions, circuit-coupled systems, and ferrofluid responses.

Gopal PanduranganView profile

Professor

• Distributed Computing
• Networks
• Big Data
• +7 more

Gopal Pandurangan is the Moores Professor in the Department of Computer Science at the University of Houston, affiliated with the Hewlett Packard Enterprise (HPE) Data Science Institute. He holds a PhD from Brown University, an MS from SUNY Albany, and a B.Tech. from IIT Madras, all in Computer Science. His research focuses on distributed computing, networks, big data, and quantum algorithms. He has advised numerous PhD students and holds IEEE Fellow and AAIA Fellow distinctions. Education: PhD (Brown University), MS (SUNY Albany), B.Tech (IIT Madras), all in Computer Science. Notable academic service includes roles as Program Committee Member for DISC, SPAA, and PODC conferences. He directs the Algorithms and Distributed Computing Lab, leading research in distributed algorithms and networks. Research interests encompass distributed algorithms, graph theory, peer-to-peer networks, social network analysis, and quantum computing applications. Awards include the University of Houston Research Excellence Award and contributions to doctoral dissertation awards. His work emphasizes fault-tolerant systems, communication efficiency, and large-scale graph processing. Professional contributions include editorial roles at Journal of Parallel and Distributed Computing and guest editing special journal issues. He teaches advanced courses like Distributed Network Algorithms and Probabilistic Methods in Algorithm Design.

Matthieu PerrinView profile

Associate Professor

• Distributed Computing
• Message-Passing Systems
• Weakly Consistent Data Structures
• +3 more

Matthieu Perrin is an Associate Professor at the University of Nantes, affiliated with the GDD team at LS2N (Laboratoire des Sciences du Numérique de Nantes). He holds a PhD from the University of Nantes (2016), advised by Claude Jard and Achour Mostéfaoui. His research focuses on distributed computing, particularly message-passing systems, weak consistency models, and distributed algorithms. He has co-advised PhD students including Grégoire Bonin and supervised undergraduate projects like Damien Maussion's work on weak consistency enforcement. Key contributions include seminal work on differentiated consistency for gossip protocols, causal broadcast algorithms, and extensions of the Herlihy wait-free hierarchy to multi-threaded systems. His publications span journals like IEEE TPDS and conferences such as EuroSys and PODC. Perrin's work bridges theoretical foundations (e.g., consistency models) with practical implementations (e.g., state-machine replication for large-scale systems). He collaborates widely, including postdoctoral work at Technion and IMDEA Software Institute.

• Distributed Systems
• Graph Processing
• Blockchain Technology
• +4 more

Ruben Mayer is a prominent researcher in distributed systems, graph processing, and blockchain technology, affiliated with the University of Stuttgart. He holds a PhD from the same institution (2018) and has authored over 100 publications in top-tier conferences and journals such as SIGMOD, VLDB, and ACM Computing Surveys. His work focuses on scalable deep learning, federated learning, and edge computing, with applications in distributed systems and privacy-preserving AI. Key research interests include optimizing distributed infrastructure for graph neural networks, exploring cross-cloud training challenges, and advancing federated learning methodologies. He has contributed to foundational studies on blockchain optimization, edge computing reliability, and ethical AI compliance with regulations like the European AI Act. Recent work highlights include WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks (2025) and A Survey on Efficient Federated Learning Methods for Foundation Model Training (2024), demonstrating his leadership in advancing scalable machine learning systems. His research bridges theoretical insights with practical system design, addressing critical challenges in modern distributed infrastructures.

• High Performance Computing
• Energy Efficiency
• Parallel Computing
• +6 more

Laura Carrington is a researcher at the University of California, San Diego, specializing in High Performance Computing (HPC) with a focus on energy efficiency, memory management, and performance optimization. She has contributed to the development of tools like PEBIL for binary instrumentation, ADAMANT for data movement analysis, and frameworks for power management in large-scale systems. Her research spans multiple domains including ARM processor evaluation, Xeon Phi vectorization, and communication reduction in graph algorithms. Key collaborations include work with Michael Laurenzano, Allan Snavely, Ananta Tiwari, and Pietro Cicotti. Laura's work addresses critical challenges in HPC such as DVFS configuration optimization, workload colocation, and energy-aware algorithm design. While no explicit academic rank is stated, her extensive publication record across 2002-2019 in top venues like SC, IPDPS, and IJHPCA establishes her as a significant contributor to HPC research. Her work has influenced practices in system-level power management, scientific application characterization, and energy-efficient computing for both CPU/DRAM domains and emerging memory technologies.

• High-Performance Computing
• Parallel Programming
• Task Parallelism
• +6 more

Stephen L. Olivier is a prominent researcher in high-performance computing at Sandia National Laboratories, with a distinguished publication record spanning nearly two decades. His work focuses on parallel programming models, performance optimization, and energy-efficient computing across diverse architectures including CPUs, GPUs, and FPGAs. Olivier has made significant contributions to OpenMP standards and Kokkos programming model development, collaborating extensively with Department of Energy national laboratories and international research teams. Olivier's research interests center on task parallelism, memory management in distributed systems, and performance portability across heterogeneous architectures. His work addresses critical challenges in exascale computing, including efficient task scheduling for unbalanced workloads, power management in large-scale systems, and optimization of communication patterns. More recently, he has expanded his research into medical imaging applications, applying high-performance computing techniques to tuberculosis detection in rural healthcare settings. Analysis of Olivier's recent publications (2021-2024) reveals a strong focus on practical performance engineering for next-generation computing platforms. His work spans traditional HPC domains while increasingly incorporating data science applications and medical imaging analysis. The research demonstrates consistent innovation in parallel programming models, particularly around OpenMP tasking and Kokkos abstractions, with growing emphasis on energy efficiency and hardware-specific optimizations for emerging architectures. Olivier has maintained a prolific research output with numerous publications in top-tier conferences including SC, IPDPS, and IWOMP. His collaborative work extends across multiple Department of Energy laboratories and international institutions, reflecting the interdisciplinary nature of modern high-performance computing research. While specific grant information isn't detailed in the publication record, his work on DOE systems suggests significant involvement in national supercomputing initiatives.

Saleh AlnaeliView profile

Associate Professor

• Software Evolution
• Software Security
• Source Code Vulnerabilities
• +5 more

Saleh Alnaeli is an Associate Professor in the Department of Mathematics, Statistics & Computer Science at the University of Wisconsin-Stout. He holds a Ph.D. in Computer Science from Kent State University (2015), an M.S. from Technical University of Ostrava (201?), and a B.S. from the University of Zawia. His research focuses on software evolution, security, and parallelization, with a strong emphasis on IoT systems and static analysis of software repositories. Dr. Alnaeli’s work bridges empirical software engineering and cybersecurity, addressing challenges in source code vulnerabilities, automatic parallelization, and multicore programming. His contributions include cloud-based tools for detecting insecure code patterns and studies on vulnerabilities in open-source and IoT systems. Professional affiliations include IEEE, ACM, and ISCA. Publications reveal a trend toward IoT security, software quality, and empirical methodologies. Recent work explores interdisciplinary applications like drone-based educational frameworks and cosmic ray detectors. No awards are explicitly listed, though his research impacts software reliability and cybersecurity. His advising record is not detailed in the provided texts, and no grants or labs are mentioned. Collaborations likely occur through conference participation and tool development for the open-source community.

Charalampos PapadakisView profile

Assistant Professor

• Distributed Services and Networks
• Parallel and Distributed Systems
• Peer Systems
• +4 more

Dr. Charalampos Papadakis is an Assistant Professor at the Department of Electrical and Informatics Engineering, Hellenic Mediterranean University. His academic journey includes a PhD in Computer Science from the University of Crete, an undergraduate degree in Computer Science from the same institution, and an MSc in Computer Engineering from the University of Patras. Research Interests: His work spans Distributed Services and Networks , Parallel and Distributed Systems , Peer Systems , Large-Scale Distributed Systems , Computer Networks , Recommender Systems , and Graph Analysis . With over 500 citations and an h-index of 8, his contributions focus on distributed systems and network technologies. Contact: (+30) 2810 379119 Email: adanar@hmu.gr Office Hours: Wednesday 11:00–13:00

Bruno Barreto de GóesView profile

Assistant Professor

• Strategic Management
• International Business
• Corporate Sustainability
• +2 more

Bruno Barreto de Góes is an Assistant Professor in the Department of Management and International Business at the University of New Haven's Pompea College of Business. He holds a Ph.D. in Business Administration from Temple University and completed postdoctoral research at Florida International University. His research focuses on strategic management in global contexts, particularly corporate sustainability diffusion in supply networks and international market expansion strategies. Recent work examines innovation ecosystems, Industry 4.0 logistics, and emerging market competitiveness. Publication trends show consistent focus on operational optimization (vehicle routing, machine scheduling) evolving toward sustainability integration and Industry 4.0 applications. His empirical work frequently employs metaheuristic algorithms and econometric analysis across manufacturing and logistics contexts.

Thiab TahaView profile

Professor

• Numerical Analysis
• Scientific Computing
• Parallel Algorithms
• +7 more

Thiab Taha is a Professor of Computer Science at the University of Georgia. He holds a Ph.D. in Mathematics from Clarkson University (1982). His research focuses on Numerical Analysis, Scientific Computing, Parallel Algorithms, and applications in Bioinformatics, Health Informatics, and Nonlinear Waves. He has held editorial roles including Senior Editor of the International Journal Mathematics and Computers in Simulation and Guest Editor for multiple special issues on Nonlinear Waves. Prof. Taha’s work spans computational methods for nonlinear evolution equations, GPU-accelerated algorithms, and deep learning applications in medical imaging and video captioning. Notable achievements include the 3rd IMACS 2011 Most Successful Papers Award and his role as Vice President of IMACS. His research also intersects with high-performance computing and big data challenges, including grants like the NSF EAGER initiative for Big Data Consulting Services (2013). Education: Ph.D. in Mathematics (Clarkson University, 1982) Key Contributions: Development of parallel numerical schemes for nonlinear PDEs, GPU-optimized algorithms, and AI-driven medical imaging solutions. Labs/Teams: Involved in computational biology projects like KINSOLVER for simulating biochemical networks and DrDRV3 for ophthalmic lesion detection. His interdisciplinary research bridges computational mathematics, AI, and real-world applications in healthcare and data science. Current efforts include advancing GAN-based meta-universe creation and energy-preserving fractional differential equation solvers.