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Mher SafaryanView profile

Researcher

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +7 more

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

• Networks and Data
• Distributed Systems
• Peer-to-Peer Storage
• +5 more

Gil UTARD is a University Professor at Université de Picardie Jules Verne (UPJV), specializing in Networks and Data. His research spans from foundational work in parallel computing and distributed systems to cutting-edge applications in medical IoT security and blockchain technology. Research Focus: Professor Utard's work has evolved significantly over three decades, beginning with fundamental research in parallel computing architectures and matrix operations. His early contributions include developing efficient out-of-core algorithms for large-scale matrix computations and pioneering work in data-parallel program validation. More recently, his research has pivoted toward critical applications in healthcare technology, specifically securing medical Internet of Things (IoT) devices through innovative blockchain implementations and advanced cryptographic protocols. Research Areas: Distributed Systems and Peer-to-Peer Networks Medical IoT Security and Privacy Blockchain Technology for Healthcare Parallel and High-Performance Computing Large-scale Data Storage and Management Cryptographic Protocols for Secure Data Sharing Research Evolution: His publication trajectory demonstrates a clear evolution from theoretical computer science foundations to practical security applications. The 2000-2010 period focused on parallel computing fundamentals, particularly matrix operations and storage systems. The 2011-2020 period saw expansion into peer-to-peer storage systems and distributed architectures. The 2021-present period represents a dramatic shift toward medical applications, with 70% of recent publications addressing healthcare-specific security challenges. Laboratory Affiliation: Professor Utard is associated with the Networks and Data domain within UPJV's research structure, though specific laboratory details are not provided in the source material.

Anne TrefethenView profile

Professor

• Scientific Computing
• High-Performance Computing
• Numerical Algorithms
• +3 more

Anne Trefethen is a Professor of Scientific Computing and Pro-Vice-Chancellor at the University of Oxford, responsible for the institution's Digital Strategy. As a Fellow of St Cross College, she has played a pivotal role in advancing high-performance numerical algorithms and large-scale scientific applications through her research. Pro-Vice-Chancellor for Digital Strategy, University of Oxford Director of the Oxford e-Research Centre (2005-2012) Chief Information Officer at University of Oxford (2012-present) Her research expertise spans: Parallel numerical algorithms Energy-aware algorithm design Scientific software engineering She has held leadership roles in the UK e-Science Core Programme and worked in the US with Thinking Machines Inc and Cornell University’s Theory Centre.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +3 more

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.

Dixin TangView profile

Assistant Professor

• Databases
• Systems and Networking
• Vector Databases
• +3 more

Dixin Tang is an Assistant Professor at the University of Texas at Austin Department of Computer Science. He previously served as a postdoc scholar at UC Berkeley under Prof. Aditya Parameswaran and earned his Ph.D. from the University of Chicago CS department under Prof. Aaron Elmore. Current research focuses on vector databases, large-scale unstructured data analysis, and data systems leveraging CXL memory technology. Key contributions include systems like Tigon (distributed CXL pod databases) and Pasha (scalable CXL pod architecture). His work emphasizes user-centered data systems, with projects like FormS (spreadsheet-to-SQL translation), Smash (string distance metrics), and Lux (visualization recommendations) demonstrating cross-decade innovation. Recent publications (2025) highlight trends in: CXL memory-optimized data architectures Homomorphism-based query frameworks Scalable unstructured data processing Scientific recognition includes: VLDB 2023 Best of Special Issue ICDE 2021 Short Paper Award Teaching roles include: CS 395T: Database Systems and LLMs (Fall 2025) CS 347: Data Management (Fall 2024) CS 395T: Advanced Query Optimizations (Spring 2024) Affiliated with the UT Data Systems research group, collaborating with Profs. Witchel and Chidambaram on CXL memory projects.

Jayesh BadwaikView profile

Researcher

• Computational Physics
• High Performance Computing
• Software Engineering
• +4 more

Dr. Jayesh Badwaik is a Scientific Researcher at the Jülich Supercomputing Center (JSC) within Forschungszentrum Jülich, a leading interdisciplinary research center in Europe. His work is centered in the Accelerating Devices Lab, where he focuses on high-performance computing architectures and computational methods for scientific applications. Dr. Badwaik's research spans multiple disciplines at the intersection of computer science, physics, and mathematics. His primary research interests include: Computational Physics and numerical methods for scientific computing High Performance Computing (HPC) with focus on exascale systems GPU programming models and accelerated computing Software engineering for large-scale scientific applications Lattice Boltzmann Methods for fluid dynamics simulations Parallel numerical algorithms for conservation laws Analysis of Dr. Badwaik's publication record reveals a clear evolution from theoretical numerical methods toward practical implementation on cutting-edge computing architectures. His early work (2016-2020) focused on mathematical foundations of numerical schemes for conservation laws, while his recent publications (2023-2024) demonstrate increasing emphasis on exascale computing challenges. His contributions to the JUPITER benchmark suite represent significant work in evaluating next-generation supercomputing systems. A substantial portion of his research centers on scaling the Lattice Boltzmann Method to exascale platforms, addressing critical challenges in computational fluid dynamics at unprecedented scales. His expertise in GPU programming is evident from his practical overview of programming models, which provides valuable insights for the HPC community. Dr. Badwaik is actively involved in the Accelerating Devices Lab at JSC, contributing to Europe's high-performance computing ecosystem. His work bridges theoretical numerical analysis with practical implementation on advanced computing architectures, making significant contributions to scientific computing at scale. His research has implications for multiple scientific domains that rely on large-scale simulations, including climate modeling, materials science, and computational fluid dynamics.

Paul F. BaumeisterView profile

Researcher

• Parallel Programming
• Electronic Structure Calculations
• Simulation
• +5 more

Dr. Paul F. Baumeister is a Researcher at the Forschungszentrum Jülich, leading the Simulation and Data Lab Electrons & Neutrons (SDLen) within the Large-Scale Data Science (LSDS) department. His work bridges computational simulations and experimental techniques, focusing on real-space density functional theory (DFT) methods for high-performance computing (HPC) applications. He contributes to advancing modeling of nanostructures and develops libraries for HPC platforms. His research spans interdisciplinary domains, including electron microscopy (Ernst Ruska-Centre), neutron scattering (JCNS), and X-ray scattering. These efforts aim to integrate theoretical models with empirical data for materials science and computational physics applications. Paul F. Baumeister is based at the Jülich Supercomputing Centre (JSC) in Building 14.14, Room 4015, Jülich, Germany. His ORCID identifier is 0000-0002-2005-4474.

Luis CifuentesView profile

Researcher

• Computational Fluid Dynamics
• Turbulent Reacting Flows
• Parallel Computing
• +6 more

Luis Cifuentes is a Senior Researcher at the Jülich Supercomputing Center (JSC), Research Center Jülich GmbH, and affiliated with the Chair of Fluid Dynamics at the University of Duisburg-Essen. His work focuses on high-resolution numerical modeling of turbulent and reactive flows using Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS). Academic Affiliation: University of Duisburg-Essen Research Institution: Research Center Jülich GmbH Research Interests Computational Fluid Dynamics (CFD) Turbulent Reacting Flows Direct Numerical Simulation (DNS) and LES Turbulent Combustion Modeling (including MILD combustion) Nano-Particle and Soot Formation High-Performance Computing (HPC) AI-Based Engineering Solutions Publication Trends His research emphasizes DNS/LES of turbulent premixed/non-premixed flames, flame-vortex interactions, and particle formation dynamics. Key topics include scalar gradients, strain rates, flame curvature evolution, and Lewis number effects in combustion systems.

• Supercomputing
• High-Performance Computing
• Parallel Computing
• +2 more

Davide Giusti is a researcher at the Jülich Supercomputing Center (JSC), part of Forschungszentrum Jülich, holding the academic qualification Dr. habil. which signifies completion of a habilitation degree in the German academic system. His core research expertise spans: Supercomputing infrastructure development High-Performance Computing optimization Parallel computing architectures Scientific computing methodologies Large-scale computational science applications As a key contributor to one of Europe's premier supercomputing facilities, he enables cutting-edge research across physics, climate science, and materials engineering through advanced computational resources and expertise in exascale computing systems.

Troels HaugbølleView profile

Associate Professor

• Star Formation
• Planet Formation
• Plasma Physics
• +5 more

Troels Haugbølle is an Associate Professor in the Astrophysics and Planetary Research group at the Niels Bohr Institute, University of Copenhagen. His work focuses on computational astrophysics related to star and planet formation, with particular expertise in numerical modeling of protostellar systems, disk dynamics, and plasma physics applications in astrophysical contexts. Research Interests: AMR models of star formation Particle-in-cell models Plasma physics Dust dynamics in protostellar environments Astro-chemistry integration in hydrodynamics Forward modeling to link simulations with observations His recent research shows a strong focus on protoplanetary disk formation mechanisms, particularly through Bondi-Hoyle accretion, and the evolution of angular momentum in young stellar systems. He has developed advanced computational methods including GPU-accelerated MHD simulations and Monte Carlo approaches for plasma physics. His work increasingly integrates multi-scale modeling to connect large-scale molecular cloud dynamics with the formation of planetary systems. Professional Activities: Maintains the starformation.hpc.ku.dk research platform Active contributor to major astrophysics journals including Nature Astronomy, Astronomy & Astrophysics, and Monthly Notices of the Royal Astronomical Society Collaborates internationally with researchers across Europe and beyond Dr. Haugbølle's work has been featured in 14 news outlets, blogged about by 2 sources, and shared by multiple social media users, demonstrating the impact of his research on the broader scientific community and public understanding of star and planet formation processes.

• Computational Chemistry
• Spectroscopy
• Theoretical Chemistry
• +3 more

Giovanna Fronzoni is a Full Professor in the Department of Chemistry at the University of Trieste. She holds significant administrative roles including Department's Board Member, President of the Joint Board of Studies, and member of Boards of Studies for both Chemistry and Pharmacy programs. Her research focuses on Computational Chemistry, Spectroscopy, and Theoretical Chemistry, with specific expertise in computational spectroscopies for adsorbates and nanoparticles, as well as theoretical methods for photoabsorption and molecular photoionization. She contributes to the Computational Spectroscopies Research Group and Molecular Photoionization and Photoabsorption Research Group, where she works on development of machine code and large-scale parallel computing applications. Professor Fronzoni's notable publication from 2006 addressed critical challenges in catalyst longevity for polyketone synthesis, demonstrating her contributions to solving deactivation problems in palladium catalysts for CO/Vinyl Arene Polyketones. She is actively engaged in departmental governance and academic program development at the University of Trieste, reflecting her leadership position within the chemistry faculty.

Jon CalhounView profile

Associate Professor

• Computer Science
• Electrical Engineering
• High-Performance Computing
• +4 more

Jon Calhoun is an Associate Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University, directing the Future Technologies in Heterogeneous and Parallel Computing (FTHPC) Laboratory. He holds a Ph.D. in Computer Science (2017) from the University of Illinois at Urbana-Champaign and dual B.S. degrees in Computer Science and Mathematics (2012) from Arkansas State University. Ph.D., Computer Science – University of Illinois at Urbana-Champaign B.S., Computer Science – Arkansas State University B.S., Mathematics – Arkansas State University His research focuses on fault tolerance and data compression in high-performance computing (HPC) systems. Key areas include silent data corruption (SDC) mitigation, error-bounded lossy compression for scientific workflows, and improving checkpoint-restart mechanisms. His work addresses critical HPC bottlenecks in data movement, storage, and reliability, with applications spanning exascale computing, medical imaging, and autonomous vehicle systems. Recent publications analyze compression trends in scientific data, including lossy techniques for DICOM files, federated learning communications, and real-time pedestrian detection systems. His group develops frameworks like SZ3 and LibPressio to optimize data management in HPC environments. NSF CAREER Award (2020) R&D 100 Award (2021) for SZ framework CECAS Junior Faculty Teaching Award (2022) Arkansas State Alumni Academy (2022) IEEE Senior Member (2022) The FTHPC Laboratory investigates heterogeneous computing systems, funded by the National Science Foundation, DOE, and the US Army. His research integrates fault injection analysis, data reduction techniques, and energy-efficient scalable solvers.

Ulrich MeyerView profile

Professor

• Algorithm Engineering
• Big Data
• High Performance Computing
• +5 more

Prof. Dr. Ulrich Meyer is a full Professor of Computer Science at Goethe University Frankfurt, where he joined as a full professor in 2007 at the age of 35. He is also a Fellow at the Frankfurt Institute for Advanced Studies (FIAS) since 2020 and serves as the spokesperson for the DFG priority program "Algorithms for Big Data" (SPP 1736), which spans over 15 sites across Germany, Austria, and Switzerland. Professor Meyer received his Ph.D. in computer science from Saarland University in 2002. Following his doctorate, he worked as a postdoc and later as a senior researcher (W2) at the Max-Planck Institute for Computer Science in Saarbrücken before joining Goethe University Frankfurt. His research focuses on the design and engineering of algorithms and data structures for very large data sets, with particular emphasis on parallelism (Shared-Memory, clusters, GPUs), memory hierarchies (caches, SSDs, hard disks), and energy efficiency. His work spans the spectrum from theoretical worst-case results and average-case analyses to experimental evaluation of heuristics. A significant portion of his research involves graph algorithms, where his group has achieved more than a thousand-fold speed-up over state-of-the-art methods through algorithms with better access patterns. His recent work has focused on memory-efficient graph exploration in dynamically changing data, approximate solutions, and efficient generation of randomized graph test data. Professor Meyer has received numerous prestigious awards including the 2019 ESA Test of Time Award for the Delta-Stepping Algorithm, Best Paper Awards at ESA 2019 for research on Fragile Computing and Graph Generation, the 2011 Award Germany Land of Ideas for Ecosort, records in the JouleSort Competition in 2009/10, and the 2003 Best Dissertation Award from the University of Saarland. As spokesperson for the DFG priority program SPP 1736 "Algorithms for Big Data," Professor Meyer coordinates research across multiple institutions. His Algorithm Engineering group is also part of the DFG Research Unit FOR 2971 "Algorithms, Dynamics, and Information Flow in Networks" and the LOEWE Priority Programme CMMS. His research has been supported by various grants that have enabled significant contributions to parallel and external-memory graph algorithms, large-scale network generation, and energy-efficient computing. Professor Meyer's research group forms part of the Computer Sciences and AI Systems division at FIAS. They collaborate extensively with researchers across Europe through the DFG priority program and other initiatives. The group is particularly known for their work on memory-efficient graph algorithms, large-scale network generation, and contributions to understanding the algorithmic foundations of big data processing.

Rico SennrichView profile

Assistant Professor

• Natural Language Processing
• Machine Translation
• Multilinguality
• +7 more

Rico Sennrich is an Assistant Professor at the University of Zurich's Department of Computational Linguistics, specializing in natural language processing with a focus on machine translation and multilinguality. He is also an Honorary Fellow at the University of Edinburgh and an ELLIS Fellow. His research spans low-resource and efficient methods, interpretability and model analysis, and multimodal language processing, particularly known for work on tokenization, neural architectures, and data augmentation. His research interests include developing practical solutions for multilingual machine translation, addressing challenges in low-resource scenarios, improving model efficiency, and analyzing how neural networks process language. He has made significant contributions to subword tokenization techniques and neural architecture design that have been widely adopted in both research and industry applications. His work often bridges theoretical insights with practical implementations to advance the state of the art in NLP. His recent publications reflect trends toward addressing bias in machine translation, improving document-level translation, enhancing multilingual capabilities, and developing more efficient inference methods for large language models. His research increasingly focuses on the intersection of machine translation with multimodal processing and low-resource language scenarios. ELLIS Fellow Honorary Fellow at the University of Edinburgh Action editor for Computational Linguistics and ACL Rolling Review Standing reviewer for TACL Senior area chair for ACL 2025 Professor Sennrich has advised numerous PhD students whose work has received recognition, including ACL outstanding paper awards and best thesis awards. He leads research projects such as Evolving Language (2024-), InvestigaDiff (2024-2027), and MT for Romansh idioms (2025-2026), having previously led the MUTAMUR project (2019-2025). His laboratory focuses on practical NLP solutions with real-world impact, particularly in multilingual and low-resource scenarios. The team actively collaborates with industry partners and participates in major research initiatives like ELITR.

Paul RichmondView profile

Professor

• GPU Computing
• Agent-Based Modelling
• High-Performance Computing
• +5 more

Paul Richmond is a Professor of Research Software Engineering at the University of Sheffield's School of Computer Science. He holds an EPSRC Early Career Research Software Engineering Fellowship focused on accelerating scientific discovery through GPU-accelerated architectures. With a strong interdisciplinary research track record, he develops software solutions for complex systems simulation using high-performance computing architectures. His research spans GPU computing, agent-based modeling, complex systems simulation, and high-performance computing methodologies. Richmond leads the development of FLAME GPU, a framework enabling large-scale agent-based simulations on GPUs, applied to domains including computational biology, transportation systems, and neural simulations. Analysis of his recent publications reveals strong emphasis on GPU-accelerated scientific computing, computational biology applications (particularly oncology and neuroblastoma modeling), transportation simulations, and parallel algorithm development. His work consistently bridges theoretical computer science with practical engineering applications. EPSRC Early Career Research Software Engineering Fellowship Fellow of the Higher Education Academy Richmond has secured substantial research funding including £4.3M as PI/CI over five years. Major grants include leadership roles in PRIMAGE (childhood cancer diagnostics), ExaTEPP (particle physics), and industrial collaborations with Fujitsu, Siemens, and Department for Transport. He founded and led the University of Sheffield's Research Software Engineering group, growing it to 13.5 FTEs supporting £13M in research projects. He established the GPUComputing@Sheffield group, leads the FLAME GPU development team, and served as Engineering Lead for Cambridge's Institute of Computing for Climate Science. Richmond is also former President of the Society for Research Software Engineering.