جست‌وجوی استاد و پژوهشگر

Keerthi Gaddameediمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Parallel Programming
• Parallel-in-time methods
• +۳ مورد دیگر

Keerthi Gaddameedi is a PhD candidate at the Department of Computer Science, Technische Universität München (TUM), affiliated with the Chair of Scientific Computing in Computer Science (SCCS). Their research focuses on High Performance Computing, Parallel Programming, and Numerics, with contributions to scalable kernel matrix approximation techniques. They actively teach courses such as Modelling and Simulation, Numerical Algorithms for HPC, and Advanced Programming. Keerthi has advised multiple student theses on topics like parallel-in-time integration and kernel matrix inversion. Education: Bachelors in Computer Science and Engineering from JNTU Hyderabad (India), Masters in Informatics from TUM. Current PhD research involves hierarchical decomposition methods for dense kernel matrices. Teaching roles span from 2022 to 2025, covering both undergraduate and graduate courses in scientific computing. Their work has been presented at venues like SIAM-PP 2024 and ISC 2024, focusing on dynamic resource management for parallel-in-time methods. Open projects include high-performance computing for time-series data and scalable linear solvers.

Tim Hansenمشاهده پروفایل

دانشیار

• Electric Power Systems
• Optimization
• High-Performance Computing
• +۵ مورد دیگر

Tim Hansen is the Harold C. Hohbach Endowed Associate Professor at the Department of Electrical Engineering and Computer Science at South Dakota State University. He holds a B.S. in Computer Engineering from Milwaukee School of Engineering (2011) and a Ph.D. in Electrical Engineering from Colorado State University (2015). B.S. in Computer Engineering, Milwaukee School of Engineering, 2011 Ph.D. in Electrical Engineering, Colorado State University, 2015 Dr. Hansen's research focuses on electric power systems and markets , with a specialization in optimization, high-performance computing, and machine learning applied to cyber-physical-social systems. His work addresses critical challenges in grid stability, including virtual inertia implementation , multi-area inertia estimation , and low-inertia microgrid control , leveraging advanced computational techniques for energy management systems. He also investigates data-driven approaches for secure electric grids in communities impacted by climate change. Recent research trends include federated learning for grid analysis, model predictive control for voltage and frequency regulation, and computationally efficient inverter modeling. His publications emphasize converter-dominated power systems , energy storage applications , and smart grid technologies across multiple spatiotemporal scales. Scientific awards include: 2024 Outstanding Researcher Award, South Dakota State University Jerome J. Lohr College of Engineering 2022 Best Paper Award for AC Optimal Power Flow research 2020 Milwaukee School of Engineering Graduate of the Last Decade 2019 IEEE-HKN Teaching Award Multiple IEEE best paper awards (2017, 2016) Dr. Hansen directs grants totaling over $2 million, including: $357,467 (DOE SETO, 2024): Transmission/Distribution Systems with Flexible Coordination $750,000 (NSF, 2023): Secure Grids for Climate-Impacted Communities $978,708 (NSF, 2022): Heterogeneous HPC Cluster Development $630,897 (DOE, 2019-2021): Dynamic Response Modeling of Converter-Dominated Systems He chairs IEEE committees including Region 4 Siouxland Section Executive Committee and co-chairs the IEEE PES Research Committee, while advising the IEEE-HKN Honor Society.

Markus Weinhardtمشاهده پروفایل

استاد

• Reconfigurable Computing
• Compiler Development
• Image Processing
• +۳ مورد دیگر

Prof. Dr.-Ing. Markus Weinhardt is a Professor at the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences. His research focuses on reconfigurable computing, compiler development, and image processing. He earned his Ph.D. from Karlsruhe Institute of Technology (2000) and held postdoctoral positions at Imperial College London (2000) and PACT XPP Technologies AG (2009). He leads the DFG-funded HiPReP project and organizes workshops like FSP 2016. Education: Ph.D. in Computer Science (Karlsruhe Institute of Technology), postdoctoral research at Imperial College London, and industry experience at PACT XPP Technologies AG. Research Interests: Reconfigurable architectures, FPGA-based acceleration, compiler optimization, and high-performance computing. Projects include HiPReP (high-performance reconfigurable processor) and HPVis (software optimization via FPGA coprocessors). Teaching: Courses on Hardware/Software Codesign, Programming, and Master’s projects in compiler design and hardware optimization. Key Contributions: Over 30 publications, including works on CHiPReP compilers, dynamic scheduling in reconfigurable arrays, and FPGA-accelerated algorithms.

• 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.

Anne C. Elsterمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Parallel Computing
• GPU Computing
• +۶ مورد دیگر

Prof. Anne C. Elster is a Professor of Computer Science at NTNU's Department of Computer & Information Science (IDI), leading the HPC-Lab. She specializes in High-Performance Computing (HPC), GPU acceleration, and heterogeneous systems. Her work spans HPC applications in medical imaging, seismic processing, and oil & gas simulations, with collaborations at CERN, NVIDIA, and Schlumberger. Elster holds a PhD in Electrical Engineering from Cornell University (1994) and is an IEEE Senior Member since 2000. She has supervised over 70 master students and numerous PhD candidates, emphasizing GPU computing. Her teaching includes courses like Parallel Computing and Compilers , with a focus on programming and problem-based learning. She leads EU projects like CLOUDLIGHTNING (2015–2018) and has organized major conferences (e.g., ISC, SC, PARA). Her HPC-Lab is a CUDA Research and Teaching Center, and she advocates for HPC infrastructure investments in Norway through policy engagement.

Frederic Boyمشاهده پروفایل

دانشیار

• Digital epidemiology
• Mental health
• Neurocontrol/Neuromarketing
• +۷ مورد دیگر

Dr. Frederic Boy is an Associate Professor at the School of Management, Swansea University , where he has worked since 2017. Previously, he conducted neuroscience research at Swansea (2012-2017) and was part of the Cardiff University Brain Research Imaging Centre (CUBRIC) post-doctoral team funded by the Wellcome Trust . His academic training spans University of Grenoble (BSc, MSc), University of Edinburgh (MSc), and CNRS (PhD). Education: University of Grenoble: BSc, MSc University of Edinburgh: MSc CNRS: PhD Research Interests include digital epidemiology (using search trends to monitor population well-being), neuromarketing (cognitive psychology applied to business), and data science for public health and organizational behavior. His work bridges neuroscience with management studies , particularly in technology adoption and decision-making. Publication Trends show interdisciplinary focus: recent studies analyze digital happiness via search data, gambling behavior during pandemics, and engineering psychology in human-centered design. Earlier work explores brain stimulation for mental health treatment and neurochemical mechanisms in cognitive control. Teaching covers artificial intelligence (MN-M605), business data analysis (MN-2071), and data management (MN-3526). He supervises PhD topics in AI for education , digital happiness , and sustainability leadership in Saudi Arabia's manufacturing sector.

Jack Marquezمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing (HPC)
• Cloud Computing
• Heterogeneous Storage Systems
• +۳ مورد دیگر

Jack Marquez is a Research Assistant Professor at the University of Tennessee, Knoxville, affiliated with the College of Engineering and the Department of Electrical Engineering and Computer Science. He is a member of the Global Computing Laboratory directed by Michela Taufer. PhD in Software Engineering (2022), Universidad Autónoma de Occidente, Colombia BS in Software Engineering (2013), Universidad Autónoma de Occidente, Colombia His research focuses on High Performance Computing (HPC) and Cloud Computing, with a particular emphasis on heterogeneous storage systems, scientific workflows, and performance optimization techniques. He has developed scalable algorithms for motif counting, dynamic simulation management, and data fabric integration, bridging computational efficiency with data-intensive science. His recent publications highlight expertise in: Optimizing HPC performance in cloud environments In-situ analysis for molecular dynamics simulations Machine learning-driven resource allocation Checkpointing and fault tolerance mechanisms Community-driven scientific data infrastructure Previously, he served as a researcher and adjunct professor at Universidad Autónoma de Occidente (2013–2016), contributing to software engineering pedagogy and industry collaboration as a software developer. He is based in the Min H. Kao Building, Room 351, and can be reached at jmarque4@utk.edu .

• Parallel Functional Programming
• Parallel Symbolic Computation
• Resource Analysis and Control
• +۴ مورد دیگر

Dr. Patrick Maier is a Lecturer in Computing Science at the University of Stirling's Division of Computing Science and Mathematics. His research focuses on parallel computing, including parallel functional programming, symbolic computation, and resource analysis. He has contributed to projects such as AJITPar (2013-2017), HPC-GAP (2009-2013), and MOBIUS (2006-2009). His work spans parallel algorithm frameworks (e.g., YewPar), high-performance computing systems, and formal methods in software verification. Research interests include parallel functional programming, symbolic computation optimization, temporal logic, and compositional verification. Notable publications cover neuromorphic vision systems, antibiotic dosing optimization, scalable combinatorial search, and fault-tolerant functional computation. Collaborations include work on distributed memory parallel Haskell (HdpH) and formal verification techniques for resource management. He is affiliated with the Data Science and Intelligent Systems, and Computational Mathematics and Optimisation research groups. His recent work emphasizes reproducible parallel search algorithms and interdisciplinary applications like computational biology and neuromorphic engineering.

Ayguade Parra, Eduardمشاهده پروفایل

استاد

• High-Performance Computing
• Parallel and Distributed Architectures
• Programming Models
• +۶ مورد دیگر

Eduard Ayguade Parra is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Faculty of Computer Science of Barcelona (FIB). He is a leading researcher in the UPC PM - Programming Models group and maintains a significant affiliation with the Barcelona Supercomputing Center (BSC-CNS), a premier national supercomputing facility. His primary research interests lie in High-Performance Computing (HPC) , with a deep focus on parallel and distributed architectures , programming models (especially task-based models like OmpSs), multicore and multiprocessor systems , and compilers for high-performance architectures . His work bridges hardware and software to optimize performance for complex computational problems. The trends in his recent publications highlight a sustained and evolving research program. He is actively advancing task-based programming for distributed memory and hybrid systems, exploring FPGA acceleration for key HPC kernels like SpMV, and innovating in memory system design, including active compute memory and hybrid memory object placement. His research also extends into applying AI techniques to hardware reliability and creating high-quality datasets for computer vision evaluation. HiPEAC Paper Award 2024 Professor Ayguade has been instrumental in securing and leading numerous competitive and non-competitive R&D+i projects, often funded by national and European programs. He has advised a significant number of doctoral students, whose theses cover topics such as task-based programming, FPGA acceleration, HPC compilers, and machine learning for systems. His collaborations are extensive, with frequent co-authorship with prominent figures at UPC and BSC-CNS, such as Jesús Labarta and Mateo Valero. His research has led to advancements in runtime systems, compiler technology, and FPGA-based acceleration. He is a core member of the UPC PM - Programming Models research group and his work is deeply integrated with the resources and mission of the Barcelona Supercomputing Center (BSC-CNS), one of Europe's leading institutions in supercomputing.

Torrey Wagnerمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +۷ مورد دیگر

Dr. Torrey Wagner serves as an Assistant Professor of Data Analytics at the Air Force Institute of Technology (AFIT), where he supports the Data Analytics graduate certificate program. He develops and teaches core courses including DASC 522 Machine Learning, SATI 532 Data Visualization & Communication, and DASC 511 Object-Oriented Programming using Python. His instructional focus centers on applying data analytics techniques to solve military problems through the CRISP-DM process framework. Dr. Wagner earned his PhD in Electrical Engineering with an Electro-optics focus from AFIT in 2010, an MS in Aerospace Systems Engineering from Loughborough University (UK) in 2004, and a BS in Electrical Engineering from the University of Minnesota in 2000. His educational background bridges electrical engineering, aerospace systems, and advanced data analytics. His research concentrates on applying predictive and generative artificial intelligence techniques to enhance Department of Defense organizational efficiency and performance. Dr. Wagner's scholarly work spans machine learning applications for defense systems, energy optimization for military operations, quantum computing advancements, and natural language processing solutions for defense contexts. He has developed specialized methodologies for military problem-solving through data analytics, with emphasis on practical implementation in operational environments. Dr. Wagner's publication record reveals a strategic evolution toward cutting-edge AI applications. Recent work demonstrates significant focus on large language models, retrieval-augmented generation systems, and quantum computing implementations. His research portfolio includes machine learning applications for aircraft engine cost prediction, satellite imagery analysis, seismic event classification, and energy system optimization for austere military environments. This work consistently bridges theoretical AI advances with tangible defense applications. As an educator and mentor, Dr. Wagner has guided dozens of students through their first publications and presentations. His students frequently co-author peer-reviewed journal articles and conference papers, primarily at the master's level with several doctoral candidates. He has contributed to AI education within military contexts through his chapter "Augmenting a Curriculum Review with Generative AI" in the AFIT Generative AI Teaching Guidebook and other educational resources. Dr. Wagner's research supports multiple AFIT initiatives focused on data analytics for defense applications. His work involves extensive collaboration with Department of Defense organizations to implement AI technologies in practical military settings, particularly in energy systems optimization, aircraft operations analytics, and decision support tools for commanders. Current projects include secure offline large language model implementations, quantum communication network resilience, and advanced data visualization techniques for military decision-making.

Martin Schulzمشاهده پروفایل

استاد

• Parallel and Distributed Architectures
• Performance Monitoring and Analysis
• Memory System Optimization
• +۴ مورد دیگر

Prof. Martin Schulz is a Full Professor and Chair for Computer Architecture and Parallel Systems at Technische Universität München (TUM), joining in 2017. He also serves on the board of directors at the Leibniz Supercomputing Centre. Previously, he worked at Lawrence Livermore National Laboratory (LLNL) and Cornell University. His research focuses on parallel architectures, quantum computing integration, HPC optimization, and fault tolerance. He has published over 250 papers and chairs the MPI Forum, a key standard in HPC. Education: Doctorate in Computer Science (TUM, 2001), Master of Science (UIUC). Research Interests: Parallel systems, performance analysis, quantum computing, memory optimization, and tool support for HPC. Awards: Gordon Bell Award (2006), R&D 100 Award (2011), Best Paper Awards at IPDPS and SC. Grants/Projects: SEANERGYS, PlasmaPEPS, OpenCUBE, and others. Active in academic service, including program chairs for ISC, EuroMPI, and IPDPS.

Elham Khaniمشاهده پروفایل

پژوهشگر

• Optical Communication
• Data Science
• Machine Learning
• +۲ مورد دیگر

Elham Khani is a researcher at the Electrical Engineering Department of Technische Universiteit Eindhoven. Her work focuses on optical communication systems, data center networking, and AI applications in semiconductor manufacturing. She is actively involved in the SmartTWO project (2019-2025), contributing to next-generation telecom technologies. Research Interests : Optical network design for metro-access systems AI-driven defect detection in microchips Scalable data center architectures with optical switching Energy-efficient network solutions supporting UN SDGs Collaborations : Her work intersects computer science (100% data center focus) and electrical engineering, with significant contributions to network architecture optimization (72%) and real-application traffic management (100%).

Bronson Messer IIمشاهده پروفایل

استاد

• Supernova Explosion Mechanisms
• Neutrino Transport
• Dense Matter Physics
• +۵ مورد دیگر

Bronson Messer II is a Distinguished Staff Scientist and Director of Science at the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL). Additionally, he holds a joint faculty appointment as a Professor in the Department of Physics & Astronomy at the University of Tennessee. His work focuses on enabling high-impact computational science through leadership-class computing resources. Messer’s research spans supernova explosion mechanisms , neutrino transport , dense matter physics , and turbulent nuclear combustion . He also applies machine learning algorithms to galaxy merger simulations and develops performance modeling frameworks for high-performance computing (HPC) architectures. Recent publications highlight his leadership in HPC, including efforts toward exascale computing (e.g., Ready for the Frontier , 2023) and supernova simulations (e.g., Chimera , 2020). He has contributed to the DOE Integrated Research Programs and the Frontier Exascale System initiatives. Scientific Awards : Department of Energy Secretary’s Honor Award (2020) Oak Ridge Postdoctoral Association Mentor of the Year (2020) R&D 100 Award – Flash-X (software category, 2022) Messer is affiliated with the ORCID network and collaborates with directorates such as the Computing and Computational Sciences Directorate , National Center for Computational Sciences , and the Physics Division at ORNL.

John R. Langeمشاهده پروفایل

دانشیار

• High Performance Computing
• Operating Systems
• Virtualization
• +۳ مورد دیگر

John R. Lange is an Associate Professor in the Computer Science Department at the University of Pittsburgh's School of Computing and Information. Currently on leave at Oak Ridge National Laboratory, he has established himself as a leading researcher in high-performance computing and systems software. His work bridges academic research and practical supercomputing applications, with extensive collaborations across national laboratories and research institutions. PhD in Computer Science from Northwestern University (2006) M.S. in Computer Science from Northwestern University (2010) B.S. in Computer Engineering from Northwestern University (2003) B.S. in Computer Science from Northwestern University (2003) Dr. Lange's research centers on High Performance Computing systems, with particular expertise in operating systems for supercomputing environments, virtualization technologies, and network optimization. His work explores how specialized operating systems can enhance performance in large-scale computing infrastructures, addressing challenges in resource management, fault tolerance, and system composition. He investigates the intersection of traditional HPC architectures with emerging virtualization techniques to create more flexible and efficient computing environments. His publication record demonstrates a consistent focus on advancing the state of the art in high-performance systems, with recent work emphasizing exascale computing challenges, network optimization for leadership-class supercomputers, and innovative approaches to system composition. His research shows an evolution from foundational virtualization techniques toward more sophisticated solutions for next-generation supercomputing architectures, with increasing emphasis on practical implementations in real-world leadership computing facilities. Best Paper Finalist at SC 2024 for network cost optimization research Best Paper Finalist at SC 2023 for Frontier exascale supercomputer architecture Best Paper Nominee at HPDC 2012 for VNET/P overlay networking As an educator, Dr. Lange has taught numerous systems courses including Introduction to Systems Software (CS 0449), Computer and Network Security (CS 1652), and Operating Systems (CS 2510 and CS 3510). His teaching spans both undergraduate and graduate levels, focusing on practical systems knowledge with hands-on projects. His research has been supported by collaborations with Oak Ridge National Laboratory and other national computing facilities, enabling his work to directly impact real-world supercomputing infrastructure. Dr. Lange is actively involved with The Prognostic Lab and maintains strong connections with the Oak Ridge Leadership Computing Facility (OLCF), where his current leave appointment allows him to work at the forefront of exascale computing research and development.

In Kee Kimمشاهده پروفایل

دانشیار

• Parallel and Distributed Computing
• Cloud Computing
• Edge Computing
• +۴ مورد دیگر

In Kee Kim is an Associate Professor in the School of Computing at the University of Georgia, holding a Ph.D. in Computer Science from the University of Virginia (2018). His research focuses on performance and resource management problems in diverse computing systems, including cloud, high-performance computing (HPC), edge, and IoT environments. Funded by agencies like NSF, DoD, and Army Research Labs, his work bridges theoretical innovation with practical system optimization. Education : Ph.D. in Computer Science (University of Virginia, 2018) University : University of Georgia School : School of Computing Department : Department of Computer Science Current research directions include: Edge AI : System-level optimization for AI inference, training, and compression at the edge Serverless Workflow Management : Cost-performance optimization in edge-cloud collaboration Reproducible Benchmarking : Workload characterization across cloud, HPC, edge, and IoT systems Recent publications highlight advancements in: Edge device scheduling for heterogeneous AI accelerators Model compression techniques for constrained environments Energy scheduling for environmental sensors Serverless computing benchmarking Scientific Awards : Best Paper Award at IEEE EDGE 2024 for 'Characterizing Deep Learning Model Compression...' His Adaptive Computing & Edge Intelligence (ACE) Lab at UGA supports student research through facilities in Boyd Graduate Studies Research Center (Rooms 817 and 819). Current and former advisees have contributed to publications at top venues including IEEE EDGE, CLOUD, and ACM Transactions.