جست‌وجوی استاد و پژوهشگر

Eugene Gartlandمشاهده پروفایل

استاد

• Liquid crystal modeling
• Mathematical physics
• Numerical analysis
• +۳ مورد دیگر

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 Panduranganمشاهده پروفایل

استاد

• Distributed Computing
• Networks
• Big Data
• +۷ مورد دیگر

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 Perrinمشاهده پروفایل

دانشیار

• Distributed Computing
• Message-Passing Systems
• Weakly Consistent Data Structures
• +۳ مورد دیگر

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
• +۴ مورد دیگر

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
• +۶ مورد دیگر

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
• +۶ مورد دیگر

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 Alnaeliمشاهده پروفایل

دانشیار

• Software Evolution
• Software Security
• Source Code Vulnerabilities
• +۵ مورد دیگر

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 Papadakisمشاهده پروفایل

استادیار

• Distributed Services and Networks
• Parallel and Distributed Systems
• Peer Systems
• +۴ مورد دیگر

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 Góesمشاهده پروفایل

استادیار

• Strategic Management
• International Business
• Corporate Sustainability
• +۲ مورد دیگر

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 Tahaمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Parallel Algorithms
• +۷ مورد دیگر

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.

Richard A. Regueiroمشاهده پروفایل

استاد

• Computational Multiscale Mechanics
• Geomechanics
• Materials Science
• +۵ مورد دیگر

Richard A. Regueiro is a Professor in the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder. His research focuses on computational multiscale mechanics for porous and particulate materials, including geomaterials, biomaterials, and multiphase systems. He leads projects funded by DOE, ONR, and other agencies, emphasizing exascale computing and micromorphic modeling. Regueiro holds a PhD from Stanford University (1998) and has been honored with awards such as the R&D 100 Award (2000) and the Sidney Shore Award (1991). Education: Ph.D., Stanford University (1998) S.M., MIT (1993) B.S.E., University of Pennsylvania (1991) Research Interests: Computational multiscale modeling of materials at micro-to-macro scales, including granular materials, geomaterials, and biological tissues. Key areas include poromechanics, damage mechanics, and coupled thermo-mechanical processes. His work bridges particle-level mechanics with continuum models for applications in geotechnical engineering, biomedical systems, and defense. Publications: Over 100 peer-reviewed articles, with recent focus on DEM parallelism, granular localization, and micromorphic constitutive models. His 2018 work on superlinear speedup in DEM simulations exemplifies computational advancements. Grants/Awards: DOE NNSA PSAAP Center (2020–2025), ONR MURI (2011–2021), and multiple industry collaborations. Labs/Teams: Leads the Computational Micromorphic Multiphysics Group, developing codes like Tahoe and ParaEllip3d-CFD. Collaborates with Sandia National Labs, Los Alamos National Lab, and universities globally.

Martin Heidaمشاهده پروفایل

پژوهشگر

• Homogenization
• Partial Differential Equations
• Stochastic Homogenization
• +۶ مورد دیگر

Dr. Martin Heida is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin, where he has been a member of the Partial Differential Equations research group since 2015. His work focuses on homogenization theory, stochastic processes, and numerical methods for complex systems. He holds a PhD from the Universities of Heidelberg and Prague (2011), and prior to WIAS, he was a PostDoc at TU Dortmund (2011–2015) and a graduate student at the Heidelberg Graduate School of Mathematical and Computational Methods (2008–2011). Education : PhD in Mathematics and Physics, cotutelle between Heidelberg University (Prof. Willi Jäger) and Charles University Prague (Prof. Josef Málek), 2011 Diploma in Physics and Mathematics from Heidelberg University, 2008 Research Interests : His research spans stochastic homogenization, partial differential equations (PDEs), numerical analysis, and material modeling. He develops methods for multiscale problems in porous media, plasticity, and fracture mechanics, with applications to battery aging, geology, and phase separation. Grants & Projects : Third-party funded projects: SPP 2256 (Fractal and Stochastic Homogenization), Math+ project on battery aging dynamics Teaching : Recent courses include Nonlinear Analysis at TU Munich, Homogenization Theory, and Analysis for Engineers. Labs & Collaborations : Leads the HighVoronoi.jl project for high-dimensional Voronoi diagram computations and collaborates with researchers in applied mathematics and computational science.

• Computational Robotics
• Artificial Intelligence
• Path Planning
• +۳ مورد دیگر

Valentin Hartmann is affiliated with the Computational Robotics department at ETH Zürich. His research focuses on advanced robotics, motion planning, and multi-agent systems. He develops algorithms for optimal path planning, task allocation, and autonomous decision-making in complex robotic environments. His work addresses challenges in multi-robot coordination, manipulation, and scalable solutions for construction and industrial automation. Key contributions include benchmark datasets for multi-agent planning and novel methods for efficient motion and task scheduling. Recent research highlights include advancements in kinodynamic motion planning (e.g., iDb-A*, iDb-RRT), optimization of multi-arm systems, and robotic construction applications. His projects emphasize reproducibility through dedicated planning tools (Planner Developer Tools) and leverage material integration for collective building tasks.

Zhilang Zhangمشاهده پروفایل

استاد

• Computational Mechanics
• Numerical Simulation
• Advanced Manufacturing
• +۳ مورد دیگر

Dr. Zhilang Zhang is a Professor at ETH Zurich, holding the Professorship for Advanced Manufacturing within the Department of New Manufacturing Technologies. His research focuses on computational mechanics, numerical simulation methods (SPH, FEM), advanced manufacturing processes, and process modeling. He specializes in high-fidelity modeling of complex phenomena such as additive manufacturing, material sintering, and fluid-structure interactions. Key research areas include multiscale modeling, CFD-DEM coupling, and novel numerical methods for extreme mechanics problems. His work integrates advanced computational techniques with experimental validations, addressing challenges in manufacturing, materials science, and fluid dynamics. Recent projects involve operando synchrotron tomography for melt pool analysis and parallelized SPH frameworks for large-scale simulations. Notable contributions include developing the Direct FE2 method for multiscale simulations and improving hydroelastic modeling via meshless methods. He actively explores applications of physics-informed neural networks and surrogate models in engineering optimization. His research group collaborates on cutting-edge projects at the intersection of computational engineering and advanced manufacturing, with a focus on sustainable and high-performance materials processing.

Dong Ryeol Leeمشاهده پروفایل

استادیار

• Machine Learning
• Large-Scale Data Analysis
• Computational Geometry
• +۳ مورد دیگر

Dr. Dong Ryeol Lee is an Assistant Professor in the Data Science Department at Saint Peter's University. His research focuses on large-scale machine learning, geometric problems in natural science, web, and IoT applications. Previously, he worked as a research scientist on web search engines, fleet prognostics, and revenue forecasting models. He holds a Ph.D. in Computer Science from Georgia Institute of Technology (2012), M.S. in Mathematics (2011) from the same institution, and dual B.S. degrees in Mathematical Sciences and Computer Science from Carnegie Mellon University (2005). Education : B.S. Mathematical Sciences & Computer Science, Carnegie Mellon University (2005) M.S. Mathematics, Georgia Institute of Technology (2011) Ph.D. Computer Science, Georgia Institute of Technology (2012) Dr. Lee’s research bridges computational methods and real-world applications, emphasizing efficient algorithms for high-dimensional data and metric space problems. His work spans celestial body simulations, web search optimization, and predictive maintenance in IoT systems. Recent publications explore dual-tree algorithms, kernel summation frameworks, and multitree methods in astrostatistics. His articles reflect a focus on algorithm efficiency, statistical analysis, and cross-disciplinary applications. Notable trends include scalable machine learning infrastructure and geometric problem-solving in astronomy and computer vision. No scientific awards have been listed. He currently teaches DS-600 (Data Mining) and DS-630 (Machine Learning). No grants or advising records are mentioned. His academic contributions emphasize algorithmic innovation and industrial applicability without explicit lab affiliations detailed in the source text.