جستجوی استاد راهنمای دانشگاهی

• Theoretical Nanoelectronics
• Quantum Transport
• Spintronics
• +۳ مورد دیگر

Prof. Dr. Maarten Rolf Wegewijs serves as a Professor at the Peter Grünberg Institute (PGI) within Forschungszentrum Jülich, Germany, leading research in the Theoretical Nanoelectronics group (PGI-2). His work focuses on fundamental quantum phenomena in nanoscale electronic systems, contributing to Germany's national research infrastructure in quantum technologies. His primary research domains include Theoretical Nanoelectronics , Quantum Transport , and Spintronics , with applications in next-generation computing architectures. This work intersects with condensed matter physics and quantum information science, leveraging Forschungszentrum Jülich's supercomputing resources for complex simulations of nanoscale material behaviors. Based in Building 04.8, Room 267 at the Jülich campus (Wilhelm-Johnen-Straße 52428), he maintains active research operations as evidenced by the September 4, 2023 profile update. Contact is available via +49 2461/61-4017 (primary) and +49 2461/61-2620 (secondary) telephone lines, though no email address appears in institutional records.

Guido van Capelleveenمشاهده پروفایل

استادیار

• Data Analytics
• Machine Learning
• Recommender Systems
• +۲ مورد دیگر

Dr. Guido van Capelleveen is an Assistant Professor at the University of Amsterdam's Amsterdam Business School and Faculty of Economics and Business. His academic and business expertise spans information systems, business analytics, and data mining, with a focus on real-world applications in circularity and sustainability. Education: PhD in Environmental Informatics (2020), University of Twente MSc in Business Information Technology (2013), University of Twente BSc in Information Science (2011), University of Utrecht Guido's research integrates data science, machine learning, and recommender systems to advance circular economy initiatives, including industrial symbiosis, eco-industrial parks, and sustainable resource management. His recent publications emphasize circular passports, greenwashing analysis, and energy symbiosis frameworks. His work experience includes postdoctoral research at the University of Twente and industry roles as an information analyst at Topicus (Dutch software organization) and data scientist at San Diego Supercomputer Center.

Juan Carlos Pichel Camposمشاهده پروفایل

استاد

• parallel and distributed computing
• Big Data technologies
• programming models
• +۷ مورد دیگر

Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.

Bradford L. Chamberlainمشاهده پروفایل

دانشگاهی

• Parallel programming languages
• Compilers for high-performance computing
• Scalable parallel algorithms
• +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 & Engineering at the University of Washington. He is the founding technical lead of the open-source Chapel programming language, which targets productive parallel computing from laptops to supercomputers. Education: Ph.D. in Computer Science & Engineering, University of Washington, 2001 M.S. in Computer Science & Engineering, University of Washington, 1995 B.S. (with honors) in Computer Science, Stanford University, 1992 Research interests center on user productivity for high-performance computing, spanning programming languages, compilers, algorithms, and applications. His work emphasizes scalable parallel computation and the design of language abstractions—such as regions, distributions, and iterators—that enable both high productivity and high performance. Over the past two decades he has led the Chapel project from a small research effort to a thriving open-source language with nearly twenty full-time developers at HPE and numerous external collaborators. His recent publications explore locality-aware compiler optimizations, fabric-attached memory, and the expression of sophisticated parallel patterns like stencil tiling and data re-allocation. Teaching & service: He created and regularly teaches UW’s graduate parallel computation course (CSEP 524), has advised graduate students, and serves on departmental committees, fostering tight ties between the Allen School and HPE/Cray.

Dai Liuمشاهده پروفایل

پژوهشگر

• Dataset Distillation
• Deep Learning on Heterogeneous Systems
• Network Compression
• +۳ مورد دیگر

Dai Liu is a researcher at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich . His work focuses on Dataset Distillation , Deep Learning on Heterogeneous Systems , Network Compression , and Hardware Acceleration for AI , with a particular emphasis on AI optimization for edge devices and specialized hardware like Cerebras. Education : Master of Informatics in Computational Science and Engineering (Technical University of Munich), Bachelor of Engineering in Electrical and Electronic Engineering (Tel Aviv University) His teaching activities include contributions to courses on Parallel Programming Systems , Advanced Computer Architecture , and Efficient Programming of Multicore Processors and Supercomputers .

• Computer Architecture
• High-Performance Computing (HPC)
• Internet of Things (IoT)
• +۷ مورد دیگر

Vincent Bode is a researcher and teaching assistant at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich (TUM) . His work focuses on benchmarking and optimizing Data Distribution Service (DDS) middleware for Industrial IoT applications, particularly through the DDS-Perf project in collaboration with Siemens .

Isaías A. Comprés Ureñaمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Parallel Programming
• Distributed Systems
• +۳ مورد دیگر

Isaías A. Comprés Ureña serves as a Researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM), focusing on advancing exascale computing infrastructure and distributed memory systems. His research centers on High Performance Computing with specialized expertise in parallel programming standards including MPI and PMIx, distributed resource management, batch scheduling optimization, and automatic performance tuning for supercomputing environments. These areas directly support the development of next-generation HPC tools and frameworks for scientific computing workloads. Dr. Comprés actively contributes to international HPC initiatives through membership in the MPI Forum, Virtual Institute - High Productivity Supercomputing, and Eurolab4HPC collaborations, driving standardization efforts for process management and distributed memory programming models.

Michael Gerndtمشاهده پروفایل

استاد

• Cloud Computing
• Internet of Things (IoT)
• High Performance Computing (HPC)
• +۱۱ مورد دیگر

Prof. Dr. Michael Gerndt is a Full Professor at the Chair of Computer Architecture and Parallel Systems , School of Informatics, Technische Universität München (TUM). His research focuses on cloud and IoT systems, high-performance computing (HPC), and performance analysis tools for parallel/distributed systems. Current projects: SEANERGYS , PlasmaPEPS , OpenCUBE , and MUNIQC-ATOMS Past projects: AutoTune , READEX , InvasIC , and CrossGrid Research Highlights: Developed the Periscope Tuning Framework and iOMP (OpenMP extension for invasive computing) Pioneering work in performance analysis, energy efficiency, and resource management in HPC and cloud systems Focus on AI-driven cloud operations, quantum-HPC integration, and hardware accelerators for machine learning Scientific Awards: Outstanding Paper Award, IEEE SC2 Symposium (2017) Teaching Contributions: Lectures: Parallel Programming Systems , Advanced Computer Architecture , Cloud Computing Lab courses: Efficient Programming of Multicore Processors , IoT Sensor Nodes Seminars: Hardware Accelerators for AI , Quantum Computing Integration Education & Career: PhD in Computer Science (1989, University of Bonn), postdoc at University of Vienna (1990-1991), habilitation at TUM (1998), Professor at TUM since 2000.

• Machine Learning
• Contact/Symplectic Geometry
• Computational Geometry
• +۶ مورد دیگر

Maia Fraser is an Associate Professor at the University of Ottawa , cross-appointed to the School of Electrical Engineering and Computer Science and affiliated with the Brain and Mind Research Institute . Her research bridges Machine Learning , Symplectic Geometry , and Computational Geometry , with interdisciplinary collaborations in Neuroscience and Mathematical Biology . PhD in Computer Science (University of Chicago, 2013) PhD in Mathematics (Stanford University, 1994) Postdoctoral work: University of Toronto Industry experience: Supercomputing Systems AG (Zurich) Her recent work explores AI safety through the lens of living systems, combining mathematical principles with societal implications. She examines temporal abstraction in continual learning, reinforcement learning limits, and geometric constraints in AI. Publications span topology , biological navigation , and neural time scales . Scientific contributions include: Co-organizing Fields Medal Symposium (2022) Co-moderating New Paradigms session at Fields Institute (2024) Leading INTER-MATH-AI (IMA) NSERC-CREATE program (2022) She investigates AI's impact on mathematical research and is writing a book on mathematical frameworks for AI safety .

Steven S. Lumettaمشاهده پروفایل

دانشیار

• Computer Architecture
• Parallel Computing
• Supercomputing
• +۳ مورد دیگر

Steven S. Lumetta is an Associate Professor at the University of Illinois at Urbana-Champaign , affiliated with the Department of Electrical and Computer Engineering and Computer Science under the College of Engineering. He contributed to the deployment of the Blue Waters supercomputer and serves as Principal Investigator (PI) for the CompGen Major Research Instrumentation (MRI) Center . His research focuses on: Coherence models for high-core-count accelerators like Rigel 1000-core Development of software tools for automated feedback on programming assignments Supercomputing infrastructure design Hardware/software co-design methodologies Lumetta’s work bridges theoretical research and practical systems engineering, particularly in scalable computing systems. As PI for CompGen MRI Center, he leads efforts to advance computational genomics tools and infrastructure.

David Sherrillمشاهده پروفایل

استاد

• Computational Chemistry
• Theoretical Chemistry
• Quantum Chemistry
• +۳ مورد دیگر

David Sherrill is a Regents' Professor in the School of Chemistry and Biochemistry at the Georgia Institute of Technology with a joint appointment in the College of Computing. As of January 2025, he serves as interim executive director of the Georgia Tech Institute for Data Engineering and Science (IDEaS), supporting interdisciplinary research in data science and high-performance computing across campus. His educational background includes a B.S. in Chemistry from MIT (1992), a Ph.D. in Chemistry from the University of Georgia (1996), and an NSF Postdoctoral Fellowship at UC Berkeley (1996-1999) working under Martin Head-Gordon. Sherrill's research focuses on ab initio electronic structure theory development and non-covalent interaction analysis across diverse chemical systems. His group pioneers methods like symmetry-adapted perturbation theory (SAPT) to dissect electrostatic, dispersion, and exchange-repulsion effects in drug binding, biomolecular structures, organic crystals, and organocatalysis. They specialize in generating high-accuracy quantum datasets for machine learning applications and developing the open-source Psi4 quantum chemistry software used globally. Recent publications reveal a strategic shift toward integrating quantum chemistry with machine learning —evident in tools like SparcleQC for automated protein-ligand studies and physics-aware neural networks for binding energy prediction—while maintaining rigorous benchmarking against coupled-cluster theory. The work increasingly targets crystal lattice energy prediction and anharmonic vibrational analysis with practical applications in drug design. Elected Member, International Academy of Quantum Molecular Science (2024) Herty Medal for Outstanding Contributions (2023) Fellow of AAAS, American Chemical Society, and American Physical Society National Science Foundation CAREER Award (2001) Camille and Henry Dreyfus New Faculty Award (1999) Sherrill actively mentors doctoral students including Caroline Glick (Ph.D. 2024), Philip Nelson (Ph.D. 2024), and current researchers like Izzy Berry (CRIDC poster award winner). His group receives sustained funding from NSF and ACS programs, supporting projects such as the BioFragment Database (BFDb) and CrystaLattE software for molecular crystal analysis. As associate director of IDEaS since 2016, he has secured resources for interdisciplinary computational infrastructure while advancing quantum chemistry methodology. The Sherrill Research Group operates from the Molecular Science and Engineering Building at Georgia Tech, maintaining active collaborations with national labs and industry partners. They contribute to Psi4's development while leading initiatives like the Supercomputing Conference participation and the Southeastern Regional ACS symposium honoring Sherrill's Herty Medal achievement.

• Computational Biology
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Sharlee Climer is a researcher affiliated with the University of Missouri-St. Louis , contributing to computational biology and genomics research. She was part of a seven-member team from Oak Ridge National Laboratory awarded the ACM Gordon Bell Prize (2018) in the Sustained Performance Prize category for work on combating the opioid epidemic through genetic analysis.

Anne C. Elsterمشاهده پروفایل

استاد

• High-Performance Computing
• Machine Learning
• GPU Acceleration
• +۴ مورد دیگر

Anne C. Elster is a Professor and Director of the Heterogeneous and Parallel Computing Lab (HPC-Lab) at NTNU's Department of Computer Science, with additional roles as HPC Leader at the Center for Geophysical Forecasting and Senior Research Fellow at the Oden Institute. She holds board positions at NTNU and its Faculty of Information Technology. Her research spans: High-Performance Computing : GPU acceleration, auto-tuning, and heterogeneous systems Machine Learning : Applied to optimization and computational geosciences Parallel Algorithms : For scientific computing and real-time simulations Her recent publications (2021-2024) focus on GPU auto-tuning, quantum-HPC integration, distributed systems, and ML-driven geophysical data analysis, with strong emphasis on performance optimization across architectures. Awards and honors: IEEE Computer Society Distinguished Contributor (2021) IEEE Distinguished Speaker (2019-2022) IEEE Senior Member (2000) She has supervised 100+ master's students, 15+ PhDs, and secured major grants including EU H2020 projects. Current Post Docs focus on HPC acceleration and AI applications. Her HPC-Lab collaborates with CERN, Equinor, and international universities, specializing in GPU-accelerated scientific computing and tools for performance portability.

Philipp Greteمشاهده پروفایل

پژوهشگر

• Computational Astrophysics
• Magnetohydrodynamic Turbulence
• High Performance Computing
• +۴ مورد دیگر

Dr. Philipp Grete is a postdoctoral research associate at the Hamburg Observatory (University of Hamburg), previously holding a Marie Skłodowska-Curie Fellowship at the same institution and a postdoctoral position at the Department of Physics & Astronomy, Michigan State University . His interdisciplinary research bridges astrophysics and computational methods , focusing on: Magnetohydrodynamic turbulence in astrophysical systems Performance-portable exascale simulation frameworks (Parthenon, AthenaPK) Cosmic ray transport mechanisms Anisotropic transport processes in weakly collisional plasmas Supercomputer-driven AGN feedback analysis He leads the XMAGNET project using DOE INCITE allocations on exascale systems and recently secured DFG funding for three years. His work has been recognized with the Postdoctoral Excellence in Research Award (MSU), SC23 Best Paper nomination, and CUG23 Best Paper Runner-up award.

Chen Dingمشاهده پروفایل

استاد

• Computer memory
• Locality theory
• Cache optimization
• +۵ مورد دیگر

Chen Ding is a Professor and Chair of the Computer Science Department at the University of Rochester, where he leads research in computer memory systems and locality theory. His work focuses on optimizing memory hierarchy performance across computing platforms from handheld devices to supercomputers. Ph.D. from Rice University (2000) M.S. from MTU (1996) B.S. from Beijing University (1994) Professor Ding's research centers on the scientific foundation of computer memory, particularly locality theory and its applications for minimizing data movement - the primary bottleneck in modern computing systems. His work has established that data movement, data reuse, and working set are mathematically related manifestations of the same underlying phenomenon. His research spans parallel program locality, data movement complexity, relational theory of locality, reference affinity, and whole-program locality analysis. His recent publications demonstrate a continued focus on memory system optimization, with particular emphasis on lease caching, symmetric locality modeling, and continuous-time analysis of Zipfian workloads. His work bridges theoretical foundations with practical implementations across CPU/GPU cache modeling, cache sharing optimization, and key-value memory caching systems. NSF CAREER award (2003) DOE Young Investigator Award (2002) Professor Ding has supervised numerous graduate students, including Lavaee who contributed to the "Hardness of data packing" research presented at POPL'16. His work has received consistent funding from NSF and DOE, supporting his research in memory systems and parallel programming. He has served in leadership roles including General Chair for ISMM 2020 and committee positions for major conferences like PLDI and PPoPP. His research group maintains the roclocality.org resource and develops tools like SLO (suggestions of locality optimizations) for analyzing and improving program locality. The group collaborates with industry partners including Microsoft Research, where Ding served as a Visiting Researcher, and academic institutions worldwide.