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Miguel García FolgadoView profile

Researcher

• Dark Matter Phenomenology
• Deep Learning
• High-Performance Computing (HPC)
• +3 more

Miguel García Folgado is a Momentum Postdoctoral Researcher at the Institute of Corpuscular Physics (IFIC) under CSIC, with a PhD in Astroparticle Physics from the University of Valencia. He holds dual expertise in theoretical physics research and high-performance computing (HPC) systems administration. His academic journey includes a BSc and MSc in Physics from the University of Valencia, followed by a PhD exploring dark matter models and deep learning applications (2016-2021). He has held postdoctoral positions at the Polytechnic University of Valencia and IFIC, focusing on AI-driven climate change solutions and particle physics phenomenology. Research interests span dark matter phenomenology, deep learning applications in physics and environmental science, and HPC infrastructure optimization. His work integrates theoretical physics with computational tools like TensorFlow/PyTorch and C++ for high-performance simulations. He has developed predictive models for urban traffic, wildfire emissions, and political sentiment analysis using NLP techniques. Miguel is actively involved in maintaining the GLUON HPC cluster at IFIC, managing distributed storage systems (e.g., MinIO), and automating workflows via Ansible. He has authored multiple peer-reviewed articles in journals like JCAP and JHEP, and his doctoral work earned the Extraordinary Doctorate Award. His technical skills include system administration (Linux/RedHat), database management (MySQL/PostgreSQL), and static website development using Hugo.

• Computational Fluid Dynamics
• Aeronautical Systems
• High-Order Numerical Methods
• +5 more

Dr. Antonios Antoniadis is a Lecturer in Computational Engineering Science and Director of the Computational Fluid Dynamics (CFD) Masters program at Cranfield University. He leads the VR/AR Laboratory within the Aircraft Integration Research Center and a research group focused on computational methods for fluid dynamics. His expertise spans aeronautical systems, turbulence modeling, and renewable energy technologies. Education: BEng in Automotive Engineering, University of Sussex MSc in Mechanical Engineering, University College London PhD in Aerospace CFD, Cranfield University (2013) Research Interests: Dr. Antoniadis develops cutting-edge computational frameworks for engineering challenges. His work includes high-order numerical methods on unstructured grids, data-driven turbulence models, and multi-physics simulations. Applications range from helicopter aerodynamics and wind-farm optimization to supersonic flow analysis and fluid-structure interactions in morphing UAVs. He integrates AI/ML techniques for turbulence modeling and design automation, advancing sustainable aerospace solutions. Publication Trends: Recent articles highlight advancements in rotorcraft CFD, shock absorber fluid dynamics, and high-performance solver development. Predominant themes include validation of high-order schemes for transonic flows, vortex dynamics in hovering rotors, and scalable algorithms for renewable energy systems. His work consistently bridges theoretical computational methods with industrial aerospace applications. Awards: Fellowship of the Higher Education Academy (FHEA) Labs & Teams: Dr. Antoniadis directs a VR/AR laboratory pioneering human-computer interaction for CAE applications. His research group collaborates with industry partners (e.g., BAE Systems, Airbus, Red Bull Racing) on projects involving aerodynamic prediction, multi-phase flows, and HPC parallelization.

Thomas ZeiserView profile

Researcher

• High-Performance Computing
• Lattice Boltzmann Methods
• Computational Fluid Dynamics
• +3 more

Dr. Thomas Zeiser serves as Head of Systems & Services and Chief Operating Officer HPC at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich Alexander University Erlangen-Nuremberg. He leads the Systems & Services group of NHR@FAU and HPC4FAU since the end of 2020 and serves as deputy for NHR@FAU in the NHR Betreiberausschuss. His work focuses on transitioning from serving FAU only to national center operations, managing HPC systems, procuring new infrastructure, financial controlling of NHR@FAU's budgets, and supporting planning for a new data center building. Dr. Zeiser's research interests center around High-Performance Computing with specific expertise in Lattice Boltzmann Methods , large-scale simulations, evaluation of HPC hardware and software, and efficient operation of HPC systems. His work bridges the gap between theoretical computational methods and practical implementation in high-performance environments. He has implemented the first job-based job and performance monitoring for RRZE's HPC systems and has extensive experience in procurement of HPC infrastructure. Analysis of Dr. Zeiser's publication record reveals a consistent focus on practical applications of computational methods in HPC environments. His research demonstrates a progression from fundamental lattice Boltzmann method development toward increasingly complex system-level concerns including fault tolerance, performance monitoring, energy efficiency, and scalable implementations. The publications show strong collaboration patterns with researchers at FAU and other German institutions, particularly in the areas of computational fluid dynamics and parallel computing. Dr. Zeiser regularly serves as a reviewer for various journals and compute time commissions of different HPC centers. He is also one of the local organizers for the Ferienakademie of TUM, FAU, and Universität Stuttgart held in Sarntal. His work with NHR@FAU positions him at the forefront of national high-performance computing infrastructure in Germany. At NHR@FAU and RRZE (Regional Computing Center Erlangen), Dr. Zeiser leads teams responsible for operating some of Germany's most powerful academic supercomputing resources. His group plays a critical role in supporting computational research across multiple disciplines at FAU and increasingly at the national level through the NHR initiative.

• Cloud Computing
• Database Systems
• Parallel Processing
• +3 more

Mariusz Fraś is a researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He works within the Department of Computer Science and Systems Engineering , focusing on computational systems' performance and cloud-based architectures. His research spans database optimization, parallel processing, and mobile application frameworks. Research interests include: Computational cloud scalability and cost optimization Database application performance analysis Parallel processing frameworks Mobile sensor data processing Web content delivery efficiency His publications demonstrate expertise in cloud computing trends, with 10 peer-reviewed works since 2014. Notable collaborations include researchers like Piotr Karwaczyński and Bogdan Marczuk. Current research likely continues exploring hybrid cloud environments and software performance metrics. Contact: mariusz.fras@pwr.edu.pl , Room 301, Building C-3, Wrocław University of Science and Technology.

Hans-Joachim BungartzView profile

Professor

• Scientific Computing
• Fluid Mechanics
• Plasma Physics
• +4 more

Prof. Hans-Joachim Bungartz is a Full Professor of Scientific Computing at the Technical University of Munich (TUM), leading the Department of Computer Science. He holds the TUM School of Computation, Information and Technology affiliation. His career includes roles at the University of Augsburg and Stuttgart, and he has been at TUM since 2004. He specializes in scientific computing, focusing on numerical algorithms, HPC software, and applications in fluid mechanics, plasma physics, and quantum simulations. Education: Bachelor/Master in Mathematics, Informatics, and Economics (TUM, 1982–1989) PhD (1992) and Habilitation (1998) in Sparse Grids and Numerical Methods (TUM) Research Interests: His work spans adaptive grids, parallel computing frameworks (e.g., Peano), and interdisciplinary applications in computational engineering. He emphasizes bridging modeling, algorithms, and HPC infrastructure. Awards: ISC PRACE Award (2013) Bavarian Habilitation Award (1994) His contributions include over 150 publications and leadership roles in institutions like the Leibniz Supercomputing Center and the TUM Graduate School. Leadership: As Dean of the Informatics Department (since 2013) and Director of the TUM Graduate School, he shapes academic policies. He chairs key national/international bodies like the German Research Network (DFN) Executive Board (2011–2020).

• Black Women
• African American Women
• Lived Experience
• +6 more

Malaika W McKee serves as Assistant Professor in the Department of Education Policy, Organization and Leadership at the University of Illinois Urbana-Champaign and holds a joint appointment at the National Center for Supercomputing Applications (NCSA). Her interdisciplinary work bridges educational policy, social justice, and computational research methodologies. Her research centers on recovering Black women's lived experiences through big data analytics and statistical modeling, addressing historical erasure in archival systems. She investigates resistance strategies among African American mothers in urban contexts and examines commitment drivers for pre-service teachers in underserved schools, contributing to critical race theory and educational equity frameworks. McKee's publication record demonstrates consistent innovation in applying computational methods to social science questions, particularly in digital humanities and educational data mining. Her work shows increasing integration of high-performance computing with qualitative social analysis since 2014. Her research has been recognized with: HPC Innovation Excellence Award (2016) for rescuing Black women's histories using big data At NCSA, she collaborates on cross-disciplinary teams developing statistical models to analyze large-scale educational datasets and historical archives, focusing on intersectional identity frameworks and resistance narratives.

Anthony GoodacreView profile

Professor

• Computer Architecture
• High-Performance Computing (HPC)
• Embedded Systems
• +7 more

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.

Selim RomeroView profile

Researcher

• Quantum Computing
• Computational Science
• High-Performance Computing
• +5 more

Selim Romero is a Post-Doctoral Research Associate at Texas A&M University's Single Cell Data Science Core, specializing in computational science and quantum computing. His work bridges physics, chemistry, and biology through high-performance computing (HPC) and algorithm development in C, C++, Python, Fortran, and MATLAB. Education: Ph.D. and M.S. in Computational Science, M.S. in Physics, and B.S. in Engineering Physics Technical Expertise: Parallel computing (MPI/OpenMP), GPU programming (CUDA/OpenMP), Fortran90, and density functional theory His research focuses on quantum materials, self-interaction-corrected density functional theory, and quantum computing applications. Recent publications emphasize self-interaction correction methods, quantum algorithms, and single-cell RNA sequencing data analysis using quantum annealing. He actively participates in workshops like Qiskit Global Summer School and NERSC GPU programming bootcamps. The trend in his publications reveals a dual emphasis on quantum computing (e.g., quantum annealing for feature selection) and computational chemistry/physics (e.g., self-interaction correction in density functional theory). His work spans algorithm development, HPC optimization, and interdisciplinary applications in gene expression analysis and quantum materials. As part of Texas A&M AgriLife and the College of Agriculture & Life Sciences, Selim contributes to cutting-edge computational research while engaging with institutions like NERSC and Intel Developer Tools Optimization programs.

Seyoon KoView profile

Assistant Professor

• Bioinformatics
• Statistics
• High-Performance Computing
• +7 more

Seyoon Ko is an Assistant Adjunct Professor of Mathematics and Biostatistics at the University of California, Los Angeles (UCLA). They teach courses in UCLA’s Program in Computing (PIC) and Master of Data Science in Health program. Prior to this role, they were a postdoctoral scholar in Computational Medicine and Biostatistics departments at UCLA, collaborating with Dr. Kenneth Lange and Dr. Hua Zhou. Dr. Ko earned a Ph.D. in Statistics in 2020 from Seoul National University, where they were advised by Dr. Joong-Ho Won. Their research focuses on bioinformatics, statistical methods for large-scale data analysis, and the application of high-performance computing (HPC) to advance statistical inference and pattern discovery in complex datasets. As a collaborator, Dr. Ko has contributed to interdisciplinary projects spanning particle physics, cancer research, genetics, public health, and medical imaging data. Their work emphasizes integrating computational techniques with statistical rigor to address challenges in data-intensive scientific domains.

Dong DaiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• High-Performance Computing
• +5 more

Dong Dai is an Associate Professor at the University of Delaware in the Department of Computer and Information Sciences under the College of Engineering. He directs the Data Intelligence Research Lab (DIRLab) , focusing on optimizing intelligent infrastructure for high-performance, data-intensive systems using machine learning techniques. Research Interests : Artificial Intelligence, Machine Learning, High-Performance Computing, Storage Systems, Metadata Management, Graph Storage, Job Scheduling Education : PhD and BSc from the University of Science and Technology of China Grants : CCF-EAGER, CNS, CCF-Hybrid NVM, CCF-Parallel Graph-Based Paradigm, OAC-Provenance Collection Teaching : Courses include CISC 361 Operating Systems , CISC 360 Computer Architecture , ITCS 5145 Parallel Computing , and ITSC 3050 Undergraduate Research .

Shantenu JhaView profile

Professor

• High-Performance Computing
• Distributed Computing
• Computational Science
• +3 more

Shantenu Jha is a Professor of Computer Engineering at Rutgers University and Chair of the Department (Center) for Data Driven Discovery at Brookhaven National Laboratory. He leads the RADICAL Lab and the RADICAL-Cybertools project, focusing on middleware for large-scale scientific applications. His research bridges high-performance computing, data-driven science, and health informatics, collaborating with diverse fields like molecular sciences and high-energy physics. Education: Ph.D., Syracuse University (2004); M.Sc., IIT Delhi (1995). Research Interests : High-Performance and Distributed Computing Data-Intensive Science & Engineering Cyberinfrastructure for Science Health Computing (Personalized Medicine) Awards : NSF CAREER Award (2013) Chancellor’s Excellence in Research (2016) Rutgers Board of Trustees Fellowship (2014) Multiple best paper awards at SC/ISC conferences Grants & Funding : Supported by NSF, U.S. DOE, NIH, and UK EPSRC. Current projects include ExaWorks and quantum-HPC middleware development. Labs/Teams: PI of the RADICAL Lab, leading initiatives in FAIR principles, quantum computing integration, and autonomous laboratories.

• Sequencing and Scheduling
• Combinatorial Optimization
• Computational Complexity
• +6 more

Dr. Yumei Huo is a Professor in the Department of Computer Science at the College of Staten Island, City University of New York (CUNY). Her work spans theoretical and applied domains, including combinatorial optimization, sequencing and scheduling, high performance computing, and medical imaging. She has developed and taught multiple HPC-focused courses and actively supervises undergraduate and graduate research. Education : BS, University of Science and Technology (China) MS, University of Science and Technology (China) PhD, New Jersey Institute of Technology Dr. Huo's research focuses on Sequencing and Scheduling , Combinatorial Optimization , Computational Complexity , Operations Research , High Performance Computing (HPC) , and more recently, Medical Imaging and Big Data . Her recent publications apply deep learning and CNN models to lung nodule detection, polyp classification, and CT image analysis, demonstrating a shift toward biomedical data science. Her recent publications (2018–2021) show a strong trend in applying machine learning to medical imaging, particularly in CT scans for lung and colon analysis. Earlier works focus on theoretical scheduling problems with constraints like machine availability, processing sets, and completion time optimization, indicating a foundation in algorithmic design and complexity theory. Scientific Awards and Recognition : Extensive peer reviewer for over 40 journals and conference proceedings Reviewed more than 50 PSC-CUNY research grant proposals and Chancellor’s Research Fellowship applications Contributed to NSF/IEEE TCPP curriculum initiatives in parallel computing education Dr. Huo has supervised numerous students on research projects and co-authored publications with them. She has also led curriculum development, including launching four HPC courses. She has no known active grants mentioned, but her sustained publication and educational contributions reflect ongoing research support. She is affiliated with research teams in algorithm design and medical imaging AI, particularly collaborating with researchers like H. Zhao, J. Y-T. Leung, and J. Tan.

Matthew NewbyView profile

Associate Professor

• Astronomy
• Astrophysics
• Galactic Dynamics
• +3 more

Matthew Newby is an Associate Professor of Instruction in the Department of Physics at Temple University. He focuses on astronomy, astrophysics, and the history of science, with a strong emphasis on integrating computational skills into undergraduate and graduate education. His research explores the dynamics and structure of the Milky Way galaxy, leveraging data from projects like the Sloan Digital Sky Survey (SDSS) and the MilkyWay@home distributed computing initiative. Newby also contributed to Temple’s Data Science program and restructured its computational physics curriculum, while leading graduate-level computing workshops and participating in the High-Performance Computing for Scientific Applications Master’s Program. His research interests span galactic substructures—including tidal tails of dwarf galaxies, stellar halo dynamics, and dark matter distribution—as well as Renaissance-era astronomical studies, such as analyzing 15th-century astronomical paintings. Newby’s work bridges computational methods with observational astrophysics, emphasizing interdisciplinary problem-solving and active learning in education. He has published on topics like Sagittarius Dwarf Galaxy streams, metallicity distribution functions, and Milky Way substructure modeling. Newby’s educational contributions include fostering data literacy, organizing computational workshops, and developing curricula that blend professional development with ethical considerations. His current involvement in Temple’s HPC program underscores his commitment to advancing computational tools for scientific research.

Ravindra Laxman ShindeView profile

Researcher

• Computational Chemical Physics
• Quantum Monte Carlo
• Electronic Structure Theory
• +7 more

Ravindra Laxman Shinde is a Researcher in Computational Chemical Physics at the MESA+ Institute, University of Twente. His work focuses on quantum Monte Carlo methods, exascale quantum simulations, and high-performance software development for accurate electronic structure calculations. Research Interests: Quantum Monte Carlo (QMC) Machine-Learned Force Fields Exascale Computing Electronic Structure Theory Scientific Software (CHAMP, AiiDA) Reproducibility in Computational Physics His recent publications highlight a strong trend in developing robust, scalable software solutions for quantum mechanical simulations, particularly in navigating hardware-software challenges at the exascale. His work bridges theoretical physics, computational chemistry, and computer science, with a focus on practical implementation and data integrity. Scientific Awards: FAIR data fund 4TU 2024 Advising and Grants: While specific students are not listed, Shinde is deeply involved in large-scale collaborative research projects such as TREX and NWO CHAINS, indicating leadership in grant-funded, team-based scientific computing initiatives. His role in creating software and datasets suggests mentorship in computational methods and data practices. Labs and Teams: He is a key contributor to the TREX project and the development of the CHAMP software suite, working within interdisciplinary teams focused on advancing quantum simulation capabilities at the exascale.

• High-Performance Computing
• Energy Efficiency in Computing
• Parallel Algorithms
• +4 more

Dr. Lennart Johnsson is a Professor of Computer Science at the University of Houston, with affiliations at the Royal Institute of Technology (KTH) in Sweden. He has held faculty positions at Caltech, Yale University, Harvard University, and KTH, and industry roles at ABB Research and Thinking Machines Corp. His research focuses on High-Performance Computing (HPC), energy-efficient systems, parallel algorithms, and grid computing. He has collaborated with institutions like PRACE, and companies including AMD, Intel, and Texas Instruments, leading to innovations such as energy-efficient HPC servers and DSP-based architectures. Research interests include optimizing HPC for energy efficiency, embedded processors (e.g., DSPs), and novel interconnection networks. He has pioneered software libraries like CMSSL and contributed to standards such as MPI and High-Performance Fortran. Awards include the Machtey Best Student Paper Award and recognition in the Gordon Bell Prize competition. Dr. Johnsson has supervised numerous students, including those working on adaptive scheduling, grid computing, and bioinformatics. He founded the Texas Learning and Computation Center and led initiatives like the Texas GigaPoP and RENoH network. Current projects explore energy-efficient HPC using DSP architectures. Key contributions include the first No. 1 system on the Top500 list (1993), grid computing frameworks, and infrastructure for distributed applications. He has served on boards for PRACE, NSF, and Swedish research councils, and advises on national HPC strategies.