جست‌وجوی استاد و پژوهشگر

Markus Diesmannمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuronal Network Modeling
• Supercomputing
• +۳ مورد دیگر

Prof. Markus Diesmann is a Director of the Institute for Advanced Simulation (IAS-6) and the Institute for Neuroscience and Medicine (INM-10) at Forschungszentrum Jülich. He leads the Computational Neurophysics group, focusing on understanding neuronal network dynamics through computational models and supercomputing. His work integrates theoretical neuroscience with high-performance computing to simulate brain-scale networks and explore neuromorphic computing foundations. Research interests include correlation structures in neuronal networks, simulation technologies, brain-scale modeling, and software engineering for neuroscience. He has pioneered efforts in scaling neuronal network simulations to exascale systems and improving computational efficiency. Key contributions include the NEST simulation software and studies on synaptic plasticity, sequence learning, and LFP dynamics. Publications emphasize advancements in simulation algorithms, network connectivity analysis, and bridging experimental data with computational models. His work addresses challenges in real-time cortical microcircuit simulation, neuromorphic hardware integration, and the role of noise in neural computation. Prof. Diesmann collaborates internationally on neuroscience initiatives and high-performance computing applications. He advocates for robust scientific software practices and contributes to infrastructure for open science in computational neuroscience.

• Computer Science
• Information Systems
• Formal Methods
• +۵ مورد دیگر

Keijo Tapio Heljanko is a Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Helsinki Institute for Information Technology and the EUROFusion Advanced Computing Hub. He previously held an Associate Professor position at Aalto University (2008–2018) and currently serves as Principal Investigator at HIIT. His research focuses on formal methods, parallel computing systems, machine learning applications, and GPU architecture optimization. He leads the LUMI AI Factory project (2025–2028), an EU-funded initiative for AI infrastructure development. Education details are not explicitly listed in the provided text. His work spans theoretical computer science and applied machine learning, with notable contributions to bike-sharing optimization, GPU consistency analysis, and boreal ecosystem modeling. Media highlights include collaborations with NVIDIA on AI technology centers and innovations in search engine and disinformation detection systems. Professional roles include supervising the Doctoral Programme in Computer Science and managing multi-institutional research projects. He has collaborated internationally on topics ranging from exascale computing to environmental data analysis. His research outputs total 131 publications and 9 press engagements, emphasizing both foundational and applied computational challenges.

• Laser Plasma Physics
• High-Performance Computing (HPC)
• Computational Physics
• +۳ مورد دیگر

René Widera is a researcher at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), specifically within the Laser Particle Acceleration department of the Institute of Radiation Physics. His work focuses on advancing high-performance computing (HPC) techniques for plasma simulations, particularly leveraging GPU architectures and exascale computing frameworks. He contributes to the development and optimization of the PIConGPU code, a leading particle-in-cell (PIC) simulation tool. His research integrates machine learning for real-time data analysis, parallel algorithms for HPC scalability, and cross-platform visualization strategies. Areas of expertise include laser plasma acceleration, high-energy-density physics, and the design of efficient numerical methods for large-scale simulations. He explores hardware-agnostic solutions for computational challenges, including memory access optimizations and DAG-based parallelism. Collaborations involve international HPC initiatives and open-source software projects like openPMD and alpaka . Key projects include the TWEAC initiative to overcome limitations in laser-wakefield acceleration and the development of in-situ visualization pipelines for real-time simulation insights. He also evaluates modern GPU architectures (e.g., AMD, ARM-based systems) for scientific workloads. His contributions bridge theoretical plasma physics with practical computational advancements, aiming to enable next-generation high-intensity laser experiments.

Philipp Neumannمشاهده پروفایل

استاد

• High Performance Computing
• Molecular Dynamics Simulations
• Multiscale Simulations in Fluid Dynamics
• +۵ مورد دیگر

Philipp Neumann is a Professor of Informatics with a focus on High Performance Computing and Data Science, holding a joint appointment at DESY and the University of Hamburg. As a Leading Scientist and Head of IT at DESY, he coordinates the Helmholtz Federated IT Services (HIFIS) and specializes in HPC for molecular and multiscale simulations. Diploma in Engineering Mathematics (2008), Friedrich-Alexander-University Erlangen-Nuremberg Doctorate in Scientific Computing (2013), Technical University of Munich Habilitation in Scientific Computing (2019), University of Hamburg Neumann's research spans high-performance computing, molecular dynamics, machine learning, and exascale applications. He develops tools like MaMiCo and AutoPas for multiscale and particle simulations, with applications in climate modeling, proteomics, and industrial use cases. His work emphasizes energy efficiency, fault tolerance, and automated algorithm selection. Recent publications highlight advancements in HPC software (e.g., xbat ), data harmonization (e.g., HarmonizR ), and simulation methodologies (e.g., aerodynamic lens systems, multiscale fluid dynamics). These reflect his expertise in bridging molecular and continuum-scale simulations with data-driven techniques. At DESY, Neumann leads IT infrastructure initiatives, including federated IT services for the Helmholtz Association. He contributes to projects like hpc.bw and serves on committees for international HPC conferences, demonstrating leadership in software development and computational science education.

Phil Hasnipمشاهده پروفایل

پژوهشگر ارشد

• Research Software Engineering
• Materials Modelling
• Computational Materials Science
• +۳ مورد دیگر

Dr. Phil Hasnip is an EPSRC Research Software Engineering Fellow at the University of York's School of Physics, Engineering and Technology. His work focuses on developing robust software tools for materials modeling, particularly through the CASTEP program, and promoting Research Software Engineering (RSE) skills globally. He holds a PhD from the University of Cambridge and has extensive postdoctoral experience. Research Interests: Combines computational methods with materials science to solve complex problems in energy, electronics, and advanced manufacturing. Specializes in quantum mechanical simulations, high-performance computing (HPC), and software engineering for scientific research. Key projects include optimizing CASTEP for emerging architectures and enhancing exascale readiness through the Particles at Exascale (PAX) initiative. Grants & Awards: EPSRC Fellowship (2018–2023) supports software improvements for materials modeling and RSE advocacy. Collaborates with NVIDIA, ARM, and national facilities like Diamond Light Source. Teaching: Advanced computational methods, Linux, LaTeX, and scientific communication. Labs/Teams: Leads the Research Software Engineering Group at York, collaborating with interdisciplinary teams on software frameworks like BOUT++ for fluid simulations. Future Work: Expanding software accessibility for non-specialists and advancing exascale-ready computational tools.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +۴ مورد دیگر

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

Gregor Gassnerمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Analysis
• High-Performance Computing
• +۷ مورد دیگر

Gregor Gassner is a Professor at the University of Cologne's Mathematical Institute, specializing in numerical methods for fluid dynamics and high-performance computing. He leads research in entropy-stable discontinuous Galerkin (DG) methods, focusing on robust and efficient simulations of multi-scale problems governed by conservation laws. His work includes adaptive algorithms for compressible flows, magnetohydrodynamics (MHD), and oceanography, supported by an ERC Starting Grant targeting 'un-crashable' solvers. Key research areas: Non-linear multi-scale simulations High-order DG methods Entropy stability and robustness Exascale computing (e.g., SCALEXA project) Adaptive mesh refinement Subcell limiting techniques Publications emphasize advancements in DG schemes, split-form discretizations, and applications to aerodynamics, plasma physics, and geophysical flows. His ERC grant aims to unify efficacy and robustness in numerical methods. Grants/Awards: ERC Starting Grant for developing robust numerical solvers. Labs/Teams: Core scientist at the Center for Data and Simulation Science, leading the NumSim group.

Hartwig Anztمشاهده پروفایل

دانشیار

• Algorithm Engineering
• High Performance Computing
• Numerical Linear Algebra
• +۳ مورد دیگر

Hartwig Anzt is a Research Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, and Director of the Innovative Computing Lab (ICL). He holds a Senior Research Scientist position at the Steinbuch Centre for Computing at Karlsruhe Institute of Technology (KIT), where he previously held an Early Career Award and a Junior Professorship. He earned a Diploma in Industrial Mathematics (2009) and a PhD in Applied Mathematics (2012) from KIT, with a year of studies at the University of Ottawa. His research focuses on algorithm engineering for computational mathematics, particularly numerical linear algebra, GPU computing, and mixed-precision methods. Key areas include asynchronous algorithms, communication-avoiding techniques, and sustainable software engineering for exascale computing. He leads development of the Ginkgo library, designed for high-performance numerical linear algebra on heterogeneous architectures. Anzt's work emphasizes practical software solutions for scientific computing, including sparse linear algebra, batched solvers, and FAIR-compliant research software practices. His contributions span fusion plasma simulations, cardiac electromechanics, and scalable data compression techniques for extreme-scale systems. Education: Diploma (2009), PhD (2012) in Applied Mathematics from KIT Key Roles: ICL Director, xSDK Multiprecision Project Lead Software Contributions: Ginkgo Library, JuMonC Tool, Compressed Basis GMRES

Paul DesJardinمشاهده پروفایل

استاد

• Multiphase reacting flows
• Propulsion
• Fire and combustion
• +۴ مورد دیگر

Paul DesJardin is a Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo, where he also serves as Director of the Center for Hybrid Rocket Exascale Simulation Technology (CHREST) and the Combustion and Energy Transport Laboratory (CET). He holds a PhD from Purdue University (1998), an MS from Purdue (1995), and a BS from the University at Buffalo (1993). His research focuses on multiphase reacting flows, propulsion systems, fire and combustion phenomena, computational fluid dynamics (CFD), and fluid-structure interaction. Key areas include radiative heat transfer in combustion systems, turbulent boundary layer dynamics, and advanced numerical methods for exascale computing. His work bridges theoretical models with experimental validation, particularly in hybrid rocket combustion and microgravity environments. Notable contributions include developing radiative flamelet models for solid fuel flame spread, uncertainty quantification in combustion diagnostics, and physics-informed machine learning approaches for chemistry tabulation. He has pioneered experimental techniques like two-color pyrometry for non-intrusive heat flux measurements in hybrid rocket systems. Awarded Fellow of ASME (2020) and UB’s Exceptional Scholar Award (2019), his research has been funded through grants focusing on combustion modeling, fire safety, and aerospace engineering challenges. He advises students in fluid dynamics and combustion, leading interdisciplinary teams at CHREST and CET labs.

Prashant Pandeyمشاهده پروفایل

استادیار

• Large-Scale Data Systems
• High-Performance Computing
• Computational Biology
• +۳ مورد دیگر

Prashant Pandey is an Assistant Professor at Northeastern University's Khoury College of Computer Sciences, co-directing the Data Lab and affiliated with Berkeley Lab's Performance and Algorithms Research Group. He holds a PhD in Computer Science from Stony Brook University (2018), with prior postdoc research at UC Berkeley, LBL, and Carnegie Mellon University, and a year as a Research Scientist at VMware Research. His research focuses on scalable data systems with theoretical guarantees, spanning high-performance computing, computational biology, stream processing, and storage. Key projects include designing adaptive filters, GPU-accelerated data structures, and graph containers for skewed dynamic data. Recent talks include sessions on hash tables, B-trees, and graph frameworks at conferences in Bangalore, Las Vegas, and Santiago. Teaching includes courses like Advanced Database Systems and Data Structures for Scalable Computing. His work bridges systems and algorithms, emphasizing practical applications in bioinformatics and exascale computing. Awards and grants are not explicitly listed, but his contributions include foundational papers on filters, hash tables, and genomic data indexing. Ongoing efforts involve the Data Lab's exploration of high-performance filters for GPUs and collaborative projects like the BP-tree and Vector Quotient Filters. Students under his supervision focus on database systems, learned indexes, and external-memory joins.

Bo Zhangمشاهده پروفایل

استادیار

• High-Performance Computing
• Exascale Data Management
• Edge-Cloud-HPC Continuum
• +۴ مورد دیگر

Bo Zhang serves as an Assistant Professor in the Department of Computer Science at Michigan Technological University's College of Computing. Prior to joining Michigan Tech in August 2025, he was a Post-Doctoral Research Associate at the Scientific Computing and Imaging Institute, University of Utah. His academic journey includes significant collaborations with national laboratories including Sandia, Oak Ridge, and NASA. Dr. Zhang holds a Ph.D. in Computer Science from the University of Utah (2024) where he worked under Professor Manish Parashar, and a Bachelor's degree in Telecommunication Engineering from Beijing University of Posts and Telecommunications (2018). His research focuses on addressing fundamental challenges in high-performance computing systems, particularly in the areas of GPU-accelerated architectures and extreme-scale data management. His work bridges theoretical computer science with practical implementations on leadership-class supercomputers, developing innovative solutions for data movement optimization and workflow integration across heterogeneous computing environments. Current projects include the National Data Platform (NDP) and DataSpaces framework for extreme-scale data management. Dr. Zhang's publications demonstrate consistent contributions to major HPC conferences including HiPC, Euro-Par, and CCGrid, with a strong emphasis on practical implementations that deliver significant performance improvements (up to 75% reduction in data-exchange time) while maintaining portability across diverse hardware platforms. Best Paper Candidate at Euro-Par 2023 Dr. Zhang actively mentors graduate students and seeks self-motivated Ph.D. candidates interested in HPC, cloud computing, and digital twins. His laboratory leverages multiple leadership computing facilities including Perlmutter@NERSC, Polaris@ALCF, Frontera@TACC, and Michigan Tech's Superior/Portage/DeepBlizzard systems. Current research directions include extending the DataSpaces framework for edge-cloud-HPC continuum integration and developing next-generation digital twins workflows.

• High Performance Computing
• Heterogeneous Accelerators
• FPGAs
• +۷ مورد دیگر

Dr. Charles James Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. His research focuses on high-performance computing (HPC), heterogeneous accelerators (FPGAs and GPUs), and their applications in diverse fields such as clinical physiology, electromagnetic fields, and image analysis. He collaborates extensively with the QUB Medical School on data analytics and machine learning for ICU patient monitoring. Gillan has led multiple projects, including an InterTrade Ireland-recognized Fusion project with CreVinn Ltd, which transferred FPGA programming knowledge to industry. His work spans academic and industrial partnerships, emphasizing innovation in computing systems and real-world problem-solving. His research interests include KTP projects, EPSRC funding, and H2020 initiatives. He has contributed to high-impact projects like the HPC-NI center and handheld olfactory detection systems. Awards include an InterTrade Ireland award for his Fusion project. Gillan has also engaged in outreach, training graduates in OpenCL programming and fostering industry-academia collaboration. Publications highlight advancements in neural networks for blood pressure prediction, exascale computing algorithms, and AI-driven healthcare solutions. His work bridges theoretical computing and practical applications, with a focus on edge computing architectures and transparency in food systems.

Adam P. Willardمشاهده پروفایل

استاد

• Chemical Physics
• Molecular Dynamics
• Exciton Dynamics
• +۳ مورد دیگر

Adam P. Willard is a Professor of Chemistry at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Chemistry within the School of Science. His research group focuses on understanding how molecular fluctuations drive emergent phenomena in chemical systems, particularly in areas such as exciton dynamics, interfacial water behavior, and nanoscale disorder effects. The group employs theory, computational modeling, and molecular simulations to bridge microscopic molecular behavior with macroscopic observations. Research interests include: Exciton dynamics in conjugated molecular systems and quantum dot solids Interfacial water structure and dynamics at heterogeneous substrates Electrochemical interfaces and double-layer physics Design of molecular excitonic circuits for quantum computing Recent investigations emphasize the role of nanoscale disorder in controlling material properties, with applications in organic electronics, catalysis, and biomolecular interfaces. The Willard Group has pioneered methods to quantify interfacial molecular structure using advanced simulation techniques and statistical mechanics frameworks. Current graduate students include Yuheng Wu, Jacob, Constantine, and Ayannah Lang (re-joining after a post-baccalaureate fellowship). The group’s lab website provides access to simulation tools and collaborative projects. Administrative support is provided by Thomas Tenzin. Key methodologies developed by the group include: Statistical mechanical models for exciton-phonon coupling Generalized Langevin approaches for surface vibrations Volterra kernel analysis for electrochemical systems Quantum state-space distributions for open systems Notable contributions include studies on exciton delocalization in DNA-scaffolded systems, interfacial water hydrogen bonding, and the quantum-classical entropy separation framework.

Anthony Goodacreمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing (HPC)
• Embedded Systems
• +۷ مورد دیگر

Anthony Goodacre is a Professor of Computer Architectures at the School of Computer Science, holding a part-time role while serving as Director of Technology and Systems at ARM Ltd. in Cambridge. He leads research spanning nanotechnology, hardware design, operating systems, and heterogeneous runtimes, with a focus on scalable, power-efficient systems for embedded, enterprise, and HPC applications. Research interests include: Exascale computing through EU-funded projects like EUROSERVER and ExaNoDe 3D silicon integration and system virtualization for heterogeneous architectures Quantum-classical programming languages (e.g., Quff) Power management techniques like cyclic power-gating Memory models and runtime systems for ARM-based data centers His 15 most recent publications highlight trends in FPGA acceleration, radiation-hardened MPSoC systems, and hybrid quantum-classical programming. Contributions to EU FP7/H2020 projects (e.g., EUROSERVER, ExaNEST) emphasize commercialization of ARM technology for exascale computation. Current collaborations include: KALEAO Limited (CSO/CTO since 2015) ARM Ltd. (Director since 2002) Technology Strategy Board (member since 2015) Academic partnerships in EU H2020 projects He co-supervises two PhD students and contributes to policy discussions, including parliamentary select committee evidence. His work aligns with UN SDGs for energy efficiency and sustainable computing.

Clotilde Cucinottaمشاهده پروفایل

پژوهشگر ارشد

• Computational Chemistry
• Theoretical Chemistry
• Surface Science
• +۵ مورد دیگر

Dr. Clotilde Cucinotta is an EPSRC Fellow in the Department of Chemistry at Imperial College London, where she has led independent research since 2018. Her work focuses on computational modeling of solid-liquid interfaces, electron transport phenomena, and energy conversion systems using advanced molecular dynamics and first-principles methods. Her educational background includes: Master's in Condensed Matter Physics, University of Messina (Italy) PhD from University of Modena and INFM-CNR-S3 excellence centre (Italy) Dr. Cucinotta's research integrates computational chemistry and materials science to investigate interfacial processes critical for energy technologies. She specializes in modeling electrified interfaces under operational conditions, with emphasis on platinum-water systems, 2D material reactivity, and electrochemical energy conversion. Her methodologies bridge quantum mechanical simulations with macroscopic electrochemical behavior, enabling predictive design of catalysts and energy storage materials. Current projects address CO 2 conversion, battery electrode interfaces, and corrosion mechanisms through atomistic simulations. Analysis of her 2021-2025 publications reveals consistent focus on solid-liquid electrochemical interfaces, particularly platinum-water systems under bias. Her work demonstrates methodological innovation in simulating potential-controlled interfaces while advancing fundamental understanding of capacitive response, wettability, and reaction mechanisms. Key thematic areas include electrocatalysis for sustainable fuels, nanoscale battery materials, and computational tools for realistic interface modeling. Scientific recognition includes: Prestigious EPSRC Fellowship supporting her independent research program Funded by her EPSRC Fellowship, Dr. Cucinotta leads a computational research group developing novel simulation frameworks for electrochemical interfaces. She mentors early-career researchers in computational chemistry and maintains collaborations with experimental groups at Trinity College Dublin and ETH Zurich. Her grant portfolio centers on fundamental interface science with applications in energy conversion and storage. Her research is conducted within Imperial College London's Department of Chemistry, utilizing high-performance computing infrastructure for large-scale molecular dynamics and quantum simulations. The computational laboratory focuses on developing open-boundary methods for realistic electrochemical interface modeling under operational conditions.