جست‌وجوی استاد و پژوهشگر

Bradford L. Chamberlainمشاهده پروفایل

استاد مدعو

• Parallel Computing
• Programming Languages
• High-Performance Computing
• +۳ مورد دیگر

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. With over two decades of experience in high-performance computing, he has made significant contributions to parallel programming languages, particularly as the technical lead of the Chapel programming language project since 2006. His educational background includes: Ph.D. in Computer Science & Engineering from the University of Washington (2001) M.S. in Computer Science & Engineering from the University of Washington (1995) B.S. in Computer Science from Stanford University (1992) Chamberlain's research focuses on improving programmer productivity for high-performance computing through innovative language design, compiler techniques, and runtime systems. His work centers around the Chapel programming language, which aims to provide a multiresolution programming model that allows developers to express parallelism at varying levels of abstraction while maintaining performance across diverse architectures from laptops to supercomputers. His research spans parallel language design, compiler optimization, data distribution strategies, locality management, and performance portability. Chapel builds on his earlier work with the ZPL language, where he developed region-based approaches for sparse parallel computing. His publication record over the past fifteen years demonstrates a consistent focus on practical approaches to parallel programming, with recent work emphasizing data locality, heterogeneous architectures, and performance portability. His articles show an evolution from foundational language design concepts to increasingly sophisticated implementations addressing real-world HPC challenges, particularly in the areas of domain mapping, iterator abstractions, and memory management for large-scale systems. As a Distinguished Technologist at HPE, Chamberlain has played a key role in growing the Chapel project from a modest effort to one involving nearly 20 full-time developers. His leadership has positioned Chapel as one of the most promising languages for addressing the challenge of productive parallel programming at scale. He has secured funding, established collaborations with academia and industry, and performed extensive outreach through talks, tutorials, and research visits. At the University of Washington, Chamberlain serves as a liaison between academia and industry, participating in student committees, teaching graduate courses like Parallel Computation, and fostering communication between the department and HPE/Cray. His teaching experience spans from undergraduate data structures to graduate seminars on parallel programming environments. He has also volunteered as a tutor for underrepresented students in computer science.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Language Design
• Performance Programming
• +۸ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on compiler technology, programming language design, and performance programming, with applications spanning hardware design, climate science, and quantum computing. He leads a research group developing innovative compiler frameworks and tools that bridge theoretical foundations with practical applications. Dr. Grosser completed his undergraduate studies in Computer Science at the University of Passau in Germany and pursued his PhD at École Normale Supérieure Paris as a Google PhD Fellow. Prior to joining Cambridge, he served as a Reader at the University of Edinburgh and held an Ambizione Fellowship at ETH Zurich. His research program centers on rethinking performance programming by re-connecting developers and compilers. He aims to make compilation more modular, predictable, automatic, and trustworthy while bringing open-source compiler innovation to increasingly diverse targets from GPUs to FPGAs and custom hardware. His work spans multiple domains including polyhedral compilation, constraint solving, quantum computing, and hardware design automation. Dr. Grosser is particularly interested in breaking down barriers between compilers and programmers by enabling their interaction through the programming language environment. His recent publications demonstrate a strong focus on compiler infrastructure development, particularly around the MLIR framework. He has pioneered work on Presburger arithmetic optimization with the FPL library, developed new intermediate representations for hardware description and quantum computing, and created tools for compiler education and prototyping like xDSL. His research shows a consistent theme of creating practical, high-performance compiler technologies that address real-world challenges across multiple domains. HiPEAC Technology Transfer Award 2021 for "Fast linear programming through transprecision computing on small and sparse data" OOPSLA 2021 Distinguished Paper Award for "FPL: Fast Presburger arithmetic through transprecision" Dr. Grosser actively mentors PhD students and postdoctoral researchers, currently supervising a team of over a dozen researchers working on various aspects of compiler technology. His group collaborates with industry partners including ARM and Xilinx, and maintains strong ties with the LLVM and MLIR open-source communities. He has secured funding for multiple research projects including work on verified compilation with Lean-MLIR, quantum compiler development, and hardware design automation. His research group operates at the intersection of multiple projects including Open-Source Electronic Design Automation, Seamless design of Smart Edge Processors, Lean-MLIR for verified compilation, FPL for fast Presburger arithmetic, and compilation frameworks for quantum computers. They maintain strong community engagement through regular Compiler Social events in Cambridge and active participation in LLVM developer meetings.

Mariusz Matuszekمشاهده پروفایل

استادیار

• High-Performance Computing
• Parallel Computing
• Energy Efficiency in Computing Systems
• +۳ مورد دیگر

Mariusz Matuszek serves as an Assistant Professor at Gdańsk University of Technology within the Department of Computer Systems Architecture, Faculty of Electronics, Telecommunications and Informatics. His research focuses on energy-efficient computing systems and high-performance parallel architectures. His primary research interests span High-Performance Computing , GPU Acceleration , and Energy Efficiency in Computing Systems . Current work examines power-capped optimization for deep neural network training, hardware/software energy measurement methodologies for CPU+GPU systems, and resource-aware problem formulations using ILP, greedy algorithms, and evolutionary approaches. His research bridges theoretical optimization with practical implementation in multi-GPU environments. Analysis of his 2022-2025 publications reveals strong emphasis on measuring and optimizing energy consumption in parallel computing systems, with particular focus on deep learning workloads. Key methodologies include professional hardware metering (Yokogawa WT-310E), Intel RAPL, and NVIDIA NVML interfaces across multi-GPU architectures. His work demonstrates significant performance-energy improvements through strategic power capping. Matuszek actively contributes to computer science education through curriculum development in parallel programming using MPI, OpenMP, and CUDA frameworks. His teaching case studies address the growing demand for HPC expertise in modern machine learning infrastructure development.

Mariusz Uchrońskiمشاهده پروفایل

استاد

• Discrete Optimization
• High-Performance Computing (HPC)
• Parallel Algorithms
• +۳ مورد دیگر

Professor Mariusz Uchroński is affiliated with Wrocław University of Science and Technology (WUST) as a faculty member in the field of discrete optimization and high-performance computing. His research focuses on advanced computational methods including parallel and distributed algorithms, quantum computing, and task scheduling optimization. Research Interests: He specializes in developing efficient algorithms for complex computational problems, with applications in high-performance computing environments and emerging quantum computing paradigms. His work addresses optimization challenges in parallel/distributed systems and task scheduling methodologies. Contact Information: Email: mariusz.uchronski@pwr.edu.pl Phone: +48 71 320 2456 Office: Z. Janiszewskiego Street 11/17, Wrocław, building C-3, room 214 Consultations: Mondays 16:00-18:00

Rahul Gomesمشاهده پروفایل

دانشیار

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Rahul Gomes, Ph.D., is an Associate Professor of Computer Science and Ramsey Research Professor (2025-2028) at the University of Wisconsin–Eau Claire within the College of Arts and Sciences. His work bridges artificial intelligence, machine learning, and computational science with applications in healthcare, geospatial analytics, and cybersecurity. He maintains active research collaborations with clinical, scientific, and community partners while mentoring undergraduate and graduate students in cutting-edge projects. His educational background includes: Ph.D. in Computer Science from North Dakota State University, Fargo M.S. in Computer Science from Sikkim Manipal University, India B.Ed. and B.S. from St. Xavier's College Kolkata, India Gomes leads research in multiple high-impact domains with a focus on developing interpretable AI systems that address real-world challenges. His biomedical informatics work spans medical imaging analysis (particularly for IVCF detection and pancreatic cancer diagnosis), single-cell RNA sequencing, and retrieval-augmented generation for clinical workflows. In geospatial analytics, he develops deep learning approaches for remote sensing and land cover classification. His cybersecurity research investigates AI-based threats and defense mechanisms, including analysis of large language model vulnerabilities. The interdisciplinary nature of his work is reflected in publications spanning computer science, medical journals, and domain-specific applications. His publication record demonstrates consistent productivity across multiple domains, with recent work showing particular strength in medical AI applications, geospatial deep learning, and cybersecurity analysis. The research shows a clear trajectory toward increasingly complex multimodal approaches, with recent papers integrating vision transformers, domain adaptation techniques, and specialized architectures for specific medical and geospatial challenges. Many publications involve undergraduate student co-authors, reflecting his commitment to research-based education. His scientific recognition includes: Ramsey Research Professorship (2025-2028) NSF REU grant as PI ($459,810) for advancing HPC undergraduate research (2025-2028) RET Site grant as Co-PI to bridge gaps in high school computing education (2024-2027) Gomes actively mentors students through research projects that often lead to publications and conference presentations. His research group develops advanced methods for medical image analysis, spatial transcriptomics, and AI-driven healthcare decision support. He has successfully secured significant grant funding that supports undergraduate research experiences, demonstrating his commitment to involving students in meaningful research. His TARCC RET Site project specifically aims to enhance computing education at the high school level through teacher training. His research lab focuses on several key projects including IVCF Filter Detection using AI (employing Swin-UNet for CT scan analysis), PDAC Diagnosis using Deep Learning, scRNA-seq Analysis for pancreatic cancer, CABG Outcomes analysis, and RAG applications in healthcare. These projects involve interdisciplinary teams of students working with technologies including PyTorch, MONAI, LangChain, and various high-performance computing resources.

Rafal P. Wojdaمشاهده پروفایل

پژوهشگر

• High-Frequency Magnetics
• Medium-Voltage Magnetic Components
• Loss Optimization in Magnetic Devices
• +۵ مورد دیگر

Rafal P. Wojda is a Research and Development Staff Member at Oak Ridge National Laboratory (ORNL), Knoxville, TN, since 2018. His professional journey includes a Senior Scientist role at ABB Corporate Research Center (2012–2018) and academic training in Electronics Engineering from Warsaw University of Technology (B.S./M.S.) and Wright State University (Ph.D., 2012). As a Fulbright Scholar (2009–2010), he specialized in microwave engineering, VLSI, and nanotechnology. Current Role: Leads research on advanced magnetic technologies for power flow control and energy-efficient hardware for exascale HPC systems at ORNL. Research Focus: High-frequency magnetics, loss optimization, insulation coordination, solid-state transformers, wireless power transfer, and partial discharge phenomena. His publications span magnetic component design, thermal management, and power conversion systems, with recent work on flexible PCB windings , digital twins for supercomputers , and additive manufacturing of copper windings . Awards include IEEE Senior Member (2020), IET Power Electronics Premium Award (2014), and Fulbright Commission Award (2009). Professional Service: IEEE East Tennessee PELS/PES Joint Chapter Secretary (2018–2022) and Chair (2022–present). Expertise: Holds multiple patents in magnetic component optimization and collaborates extensively with academia and national laboratories.

Gregory Gangerمشاهده پروفایل

استاد

• Computer Systems
• Cloud Computing
• Distributed Systems
• +۶ مورد دیگر

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

Yu Liuمشاهده پروفایل

دانشیار

• Computer Architecture
• High Performance Computing
• Real-Time Systems
• +۳ مورد دیگر

Dr. Yu Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Clarkson University, Potsdam, NY. He holds a Ph.D. from Southern Illinois University Carbondale (2011) and M.S./B.S. degrees from Sichuan University (2003/2000). Prior to academia, he worked at Canadian Nuclear Laboratories (2013–2017), IBM (2011–2013), and Motorola (2003–2007). His research focuses on computer architecture, high-performance computing, thermal simulation, and wireless sensor networks , with interdisciplinary applications in nuclear science. He has secured over $1.7M in NSF funding as PI/co-PI and published >50 peer-reviewed papers. Education: Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2011) M.S., Power System and Automation, Sichuan University (2003) B.S., Communication Engineering, Sichuan University (2000) Research Interests: Physics-based thermal simulation methodologies for multi-core CPUs/GPUs Data-driven and machine learning approaches in high-performance computing Interdisciplinary applications in nuclear science and environmental monitoring Real-time embedded systems and scratchpad memory architectures Grants & Awards: 5 NSF grants (e.g., REU Site: High Performance Computing with Engineering Applications) Best Paper Award at 2022 IEEE ITherm Conference Teaching & Advising: Undergraduate/Graduate courses: Software Engineering, High-Performance Computing, Computer Networks Advised 10+ graduate/undergraduate students in thermal simulation, HPC, and nuclear engineering projects Key Projects: Development of PODTherm-GP thermal simulation framework NSF-funded REU programs training over 50+ students

Torsten Hoeflerمشاهده پروفایل

استاد

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +۳ مورد دیگر

Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.

Andrew A. Chienمشاهده پروفایل

استاد

• Sustainable Computing
• Cloud Software
• Hardware and System Architecture
• +۷ مورد دیگر

Andrew A. Chien is the William Eckhardt Distinguished Service Professor of Computer Science at the University of Chicago and a Senior Computer Scientist at Argonne National Laboratory. He leads the Large-Scale Sustainable Systems Group and directs the CERES Center for Unstoppable Computing. His research focuses on sustainable computing, cloud software/hardware, supercomputing architectures, and energy-efficient systems. Chien holds ACM, IEEE, and AAAS Fellowships and has served on advisory boards for NSF, DARPA, and multiple universities. He previously led Intel’s Research division, founded Entropia, and held professorships at UC San Diego and UIUC. His educational contributions include courses on sustainable computing, data-intensive systems, and computer architecture for scientists. Education: Sc.D., S.M., S.B. in Computer Science/Electrical Engineering from MIT Affiliations: UChicago, Argonne, NSF CISE Advisory Committee, DARPA ISAT Study Group Key Projects: Zero-Carbon Cloud, CERES Center, Recode/UDP Architecture, Global View Resilience Chien’s research bridges computational sustainability and scalable systems, addressing energy grids, data center efficiency, and low-carbon computing. His work has been recognized through awards like the NSF Young Investigator Award (1994) and the Argonne Impact Award (2020).

Michael Georg Baderمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Seismic Simulations
• +۴ مورد دیگر

Univ.-Prof. Dr. Michael Georg Bader is a professor at the Department of Computer Science at the Technical University of Munich (TUM), leading the research group on hardware-aware algorithms and software for high-performance computing (HPC). He holds a professorship within the TUM School of Computation, Information and Technology. Bader's academic career includes a PhD from TUM (2001), followed by roles in the Elite Master’s Program in Computational Engineering and the Munich Centre of Advanced Computing. His work focuses on challenges in supercomputing platforms, scalable algorithms for simulation tasks in science/engineering, and earthquake/tsunami modeling. Education: PhD in Computer Science, TUM (2001) Research Interests: Bader's research emphasizes hardware-aware algorithms, parallel numerical methods, and large-scale simulations in geosciences. Key areas include seismic wave propagation, adaptive mesh refinement, and exascale computing. His group develops simulation frameworks like SeisSol and ExaHyPE, addressing multi-physics problems at extreme scales. Key Contributions: His team achieved the SC17 Best Paper Award for tsunami-earthquake coupling simulations. Current projects include exascale computing in solid earth (ChEESE) and mixed-precision algorithms for hyperbolic PDEs. Bader's work integrates cutting-edge HPC techniques with geophysical modeling. Scientific Awards: SC17 Best Paper Award (2017) Finalist for ACM Gordon Bell Prize (2014) PRACE ISC Award (2014) Grants & Advising: Supervised 13 doctoral students, including leading researchers in seismic simulation. Active in EU-funded projects like ChEESE-2P and ScalaMIDA. Teaches courses on high-performance computing, numerical algorithms, and scientific computing. Labs/Teams: Research group based at the Leibniz Supercomputing Center (LRZ), collaborating with global institutions in computational seismology and HPC.

• High-Performance Computing
• Parallel Computing
• Scientific Computing
• +۳ مورد دیگر

Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.

Are Raklevمشاهده پروفایل

استاد

• Theoretical Physics
• Particle Physics
• Dark Matter
• +۴ مورد دیگر

Are Raklev is a Professor at the University of Oslo's Department of Physics. His research focuses on theoretical and computational physics, particularly dark matter, particle physics at CERN's LHC, and applications of machine learning. He holds leadership roles in the GAMBIT Collaboration and its Collider Working Group. Education: PhD (University of Bergen, 2007), Postdoc (University of Cambridge, 2009), and Associate Professor at the University of Oslo (2010-2013) before becoming a full Professor in 2013. His teaching includes classical mechanics and electrodynamics (FYS3120) and supersymmetry (FYS5190/9190). Research Interests: Dark matter detection, Higgs boson studies, software for computational physics, and machine learning techniques like Gaussian processes. He leads projects like GAMBIT (a global inference tool) and PLUMBIN' (to address computational bottlenecks in high-energy physics). Recent publications span superconducting magnet design for colliders, quantum computing error correction, and nuclear isotope production. His work bridges theoretical insights with experimental applications at CERN and other facilities.

• Drug Discovery
• Computational Chemistry
• Protein-Ligand Interactions
• +۵ مورد دیگر

Michael Ashley Spies is a Professor of Drug Discovery and Experimental Therapeutics, and Biochemistry and Molecular Biology at the University of Iowa's College of Pharmacy. His research focuses on protein-ligand interactions, enzymology, and computational chemistry applied to drug discovery. He leads the Ashley Spies Laboratory which develops novel computational methods (HPC docking, molecular dynamics) and validates findings through biochemical assays. Key areas include allosteric inhibition mechanisms, antimicrobial agents, and DNA repair proteins in cancer therapy. Research highlights include pioneering work on caspase inhibitors featured in Angewandte Chemie as a 'hot paper' and ChemMedChem cover article. His group combines quantum mechanical/molecular mechanical (QM/MM) simulations with experimental validation. Awards include JSPS Fellowship and recognition for impactful publications in JACS and eLife. Expertise: Computational drug design, enzyme kinetics, free energy calculations Techniques: High-performance computing, steered molecular dynamics, QM/MM simulations Clinical focus: Antimicrobial development, cancer therapeutics targeting DNA repair pathways Recent work explores small-molecule effectors for DNA repair proteins (Burger's Medicinal Chemistry) and covalent inhibitors of bacterial enzymes. His lab actively collaborates across disciplines to translate computational insights into therapeutic leads.

• Distributed Systems
• High-Performance Computing
• Fault Tolerance
• +۵ مورد دیگر

Ozalp Babaoglu is a Full Professor at the Department of Computer Science and Engineering, University of Bologna. He has held this position since 1987, following a PhD in Computer Science from the University of California, Berkeley in 1981. His research focuses on distributed systems, High-Performance Computing (HPC), fault tolerance, self-organization, and machine learning applications in data-driven autonomics. He has led numerous European research projects like BROADCAST, CABERNET, ADAPT, BISON, and DELIS, contributing to foundational work in biology-inspired distributed algorithms and gossip-based systems. He co-founded the Bertinoro International Center for Informatics (BiCi) in 2001 and the IEEE SASO conference series in 2007. Babaoglu is a Fellow of the ACM (2002) and has received prestigious awards, including the Sakrison Memorial Award (1982) and the USENIX Lifetime Achievement Award (1993). His contributions to BSD Unix and open standards are seminal, including virtual memory extensions during his PhD. Babaoglu has served on editorial boards for ACM Transactions on Autonomous and Adaptive Systems, and previously for ACM Transactions on Computer Systems and Distributed Computing. His recent work addresses HPC fault classification, data-driven resource allocation, and energy-efficient computing in hybrid systems. He actively advises on European projects and explores cognitive paradigms in distributed systems.