جست‌وجوی استاد و پژوهشگر

Ebru Bozdağمشاهده پروفایل

دانشیار

• Computational Seismology
• Global Seismic Tomography
• Full-Waveform Inversion
• +۳ مورد دیگر

Ebru Bozdağ is an Associate Professor at the Colorado School of Mines, jointly appointed in the Department of Applied Mathematics and Statistics and Geophysics , and serves as Director of Graduate Studies. She holds affiliations with the Center for Space Resources and is a Faculty Fellow at the Payne Institute . Previously, she was an Associate Professor at Université Côte d’Azur (France) and a researcher at Princeton University. Education: PhD in Geophysics, Utrecht University (2005–2009) MSc & BSc in Geophysics, Istanbul Technical University (2000–2002 & 1996–2000) Research Interests: Focuses on computational seismology, including full-waveform inversion, global seismic tomography, and planetary seismology (e.g., Mars InSight mission). Specializes in 3D seismic wave simulations and adjoint techniques for Earth’s interior imaging. Recently expanded to exploration-scale problems and computational optimizations. Awards & Grants: Chaire d’Excellence (France) NSF SCOPED Project (collaborative multi-institutional) Leadership Resource Allocation (LRAC) on Frontera supercomputer PRACE award for Marconi100 GPU system Advising: Supervised PhD student Caio, who defended in 2021. Actively recruiting students with backgrounds in geophysics, computational sciences, or mathematics. Labs & Teams: Leading computational efforts in global seismology, including collaborations on the Mars InSight mission and high-performance computing projects on systems like Fugaku and Titan.

• Computer Architecture
• Memory Systems
• Hardware/OS Interaction
• +۷ مورد دیگر

Vasileios Karakostas is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is a member of the Computer Architecture Lab and focuses on computer architecture, memory systems, and resource management. Previously, he was a postdoctoral researcher at the National Technical University of Athens' Computing Systems Lab. He holds a PhD in Computer Architecture from Universitat Politècnica de Catalunya and Barcelona Supercomputing Center. Education: Ph.D. in Computer Architecture, Universitat Politècnica de Catalunya (2016) M.Sc. in Computer Architecture, Networks, and Systems, UPC (2012) B.Eng. in Electrical and Computer Engineering, NTUA (2009) Research Interests: Memory systems (virtual memory, NVM) Hardware/OS interaction Resource management in data centers Parallel systems and serverless computing RISC-V architectures and cloud infrastructure Projects: Active in Horizon Europe projects Vitamin-V, Neuropuls, and REBECCA. Previously contributed to DAPHNE and ACTiCLOUD (EU H2020). Awards: 2024: Distinguished Artifact Award (ASPLOS) 2011: Best Paper Award (ICPE) Selected for IEEE Micro's Top Picks (2015, 2016) Teaching: Courses include Logic Design, Parallel Systems, and Large-scale Computing Systems at undergraduate and graduate levels. Labs/Teams: Leads research in the Computer Architecture Lab, collaborating on resilient architectures and cloud computing innovations.

Yuefan Dengمشاهده پروفایل

استاد

• Parallel Computing
• Molecular Dynamics
• Computational Biology
• +۵ مورد دیگر

Yuefan Deng is a Professor of Applied Mathematics and Statistics at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He holds additional roles such as Associate Director of the Stony Brook Institute of Engineering-Driven Medicine. With a BA from Nankai University (1983) and a PhD in Theoretical Physics from Columbia University (1989), his career spans academic and research roles at institutions like NYU’s Courant Institute and IBM’s Watson Lab. His research focuses on parallel computing, molecular dynamics, and biomedical engineering, particularly multiscale modeling of platelet activation and supercomputer network optimization. He has advised nearly 30 PhD students and received prestigious teaching awards, including the 2016 SUNY Chancellor’s Award for Excellence in Teaching. Education: BA in Physics (Nankai University, 1983); PhD in Theoretical Physics (Columbia University, 1989). Key affiliations include Stony Brook, NYU Abu Dhabi, and Singapore’s National University. His work combines algorithm development with applications in engineering, medicine, and finance. Major contributions include the Galaxy Beowulf supercomputer (1997) and NankaiStars (China’s fastest in 2004). Current research emphasizes machine learning integration into biomedical modeling and optimizing supercomputer performance. Scientific Awards: SUNY Chancellor’s Award (2016), Stony Brook Dean’s Teaching Award (2015–16), and recognition for contributions to supercomputing architectures. His teaching spans courses like Parallel Computing and Numerical Analysis, reaching over 20,000 students. Active in conferences, he co-chaired Supercomputing Frontiers (2015) and chairs engineering-driven medicine events. Labs/Teams: Leads multiscale modeling projects at the IEDM, collaborating on thrombosis research and AI-driven drug discovery. His work bridges computational methods with biomedical applications, aiming to address challenges in hemostasis and cardiovascular health.

Alice Konigesمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Computational Science
• Parallel Algorithms
• +۵ مورد دیگر

Dr. Alice Koniges is a computational scientist at the University of Hawaii at Manoa and Maui High Performance Computing Center. She specializes in modeling complex physical phenomena using advanced HPC techniques, with research interests spanning emerging architectures, machine learning, and partial differential equations. Her career includes roles at Berkeley Lab and joint appointments with UC Berkeley and UCLA. She leads the ALE-AMR code development team, which models plasma, fluid, and solid interactions in high-energy density regimes. As a DOE Exascale project PI, she achieved milestones in supercomputing performance and secured multimillion-dollar contracts. Previously, she served as Head of Institutional Computing at Livermore Lab, fostering industry partnerships through CRADAs. Education: PhD in Applied and Computational Mathematics from Princeton University (first woman recipient), MSE and MA from Princeton, and BA from UC San Diego. Over 100 technical papers, two HPC books, and 1,500+ citations highlight her scholarly impact. She has mentored >15 postdocs and graduate students, emphasizing HPC's practical applications. Recent work includes coastal aquifer management models and epidemiological studies of pandemic spread in island environments. Her research bridges computational methods with real-world challenges, such as fusion energy simulations and climate modeling. The PISALE codebase and ALE-AMR framework are core to her contributions. Current focus areas include exascale computing readiness, multiphysics integration, and optimizing HPC for next-gen architectures.

Brandon Wigginsمشاهده پروفایل

استادیار

• Astrophysics
• Computational Physics
• Theoretical Astrophysics
• +۸ مورد دیگر

Brandon Wiggins is an Assistant Professor of Physics at Southern Utah University (SUU) within the College of Science and Engineering. He holds a Ph.D. in Physics and Astronomy from Brigham Young University and has returned to SUU to pursue his lifelong goal of teaching and research. Known for his innovative teaching style, Wiggins has earned a 4.9/5.0 rating on RateMyProfessor.com and SUU’s Outstanding Educator Award. His research focuses on computational/theoretical astrophysics, including supernovae, neutron star mergers, black hole accretion, and astrochemistry. He collaborates with Los Alamos National Laboratory’s Center for Theoretical Astrophysics, simulating cosmic phenomena such as galaxy formation and early universe water creation. Education: B.S. in Pure Mathematics (SUU, 2012), Ph.D. in Physics and Astronomy (BYU, 2016). Research affiliations include Los Alamos National Laboratory as a Guest Scientist. Research Interests: Wiggins explores the universe’s most energetic events—supernovae, compact object mergers, and black hole dynamics—using supercomputers. His work bridges astrophysical theory with practical simulations, aiming to uncover cosmic mysteries like water’s origins and stellar explosions. Recent projects include studying first-generation supernovae’s role in molecular formation and developing tools like the SuperNovae Analysis aPplication (SNAP). Teaching: Wiggins teaches physics courses ranging from foundational (PHYS 2010/2020) to advanced (PHYS 2210/2220), emphasizing hands-on demonstrations to engage students. Colleagues and students praise his ability to make complex topics accessible and captivating. Awards: Beyond academic accolades, Wiggins values mentoring students, citing their success as his greatest reward. His outreach efforts include community science initiatives and authoring a science book during high school. Labs/Teams: Active contributor to the Center for Theoretical Astrophysics at Los Alamos, collaborating with scientists like Chris Fryer on cutting-edge projects. His research often involves interdisciplinary approaches combining computational modeling with observational astrophysics.

• Data Science
• Parallel Algorithms
• Graph Analytics
• +۳ مورد دیگر

David Bader is the Distinguished Professor of Data Science at New Jersey Institute of Technology (NJIT). He is a leading expert in high-performance computing, parallel algorithms, and large-scale graph analytics. His work focuses on developing scalable frameworks for big data problems, including graph processing, anomaly detection, and quantum computing methods. He has held continuous federal grants since 2000, with recent projects involving cyber-infrastructure for community detection, streaming data science frameworks, and gravitational wave research. Bader leads the development of open-source tools like Arachne and Arkouda, which are widely used for large-scale graph analytics. His research interests span parallel algorithms, graph theory, and high-performance computing applications. Notable contributions include the GraphBLAS initiative and the Einstein Toolkit. Bader was inducted into the Hall of Fame in 2025 for pioneering modern supercomputing innovations.

Myoungkyu Leeمشاهده پروفایل

استادیار

• High-Performance Computing
• Turbulent Flows
• Multiphysics Simulations
• +۳ مورد دیگر

Dr. Myoungkyu Lee is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Houston (2023–current). Previously, he served as an Assistant Professor at the University of Alabama (2022–2023) and held postdoctoral positions at Sandia National Laboratories (2018–2021) and the Oden Institute of Computational Science and Engineering (2016–2018). His research focuses on high-performance computing, thermal/chemical nonequilibrium flows, and direct numerical simulations of multiphysics turbulent flows. He earned his Ph.D. in Mechanical Engineering from the University of Texas at Austin (2015) and B.S. from Hanyang University (2008). Dr. Lee’s research integrates computational fluid dynamics, turbulence modeling, and high-performance computing to study wall-bounded flows, magnetohydrodynamic effects, and nonequilibrium phenomena. His work emphasizes spectral analysis, data-driven modeling, and advanced numerical methods for resolving complex fluid behaviors. Notable contributions include studies on Reynolds stress scaling, wall-pressure correlations, and stochastic forcing in turbulent flows. His awards include the Early Career Presenter Fellowship (2021) and a Best Student Paper Finalist at SuperComputing SC13 (2013). He has published extensively in journals like Journal of Fluid Mechanics and Physical Review Fluids , with over 30 peer-reviewed articles. His teaching includes courses on fluid dynamics and computational methods. Dr. Lee’s academic trajectory reflects a strong dedication to advancing computational turbulence research and fostering interdisciplinary collaborations in engineering and applied mathematics.

Dawn Holmesمشاهده پروفایل

استاد آموزشی

• Bayesian Networks
• Machine Learning
• Maximum Entropy Formalism
• +۴ مورد دیگر

Dr. Dawn E. Holmes is a Teaching Professor at the University of California, Santa Barbara (UCSB), affiliated with the Department of Statistics and Applied Probability and the College of Letters and Science. She holds dual office locations: South Hall 5506 (primary) and Cheadle Hall 1117F. Her work focuses on Bayesian Networks, Maximum Entropy Formalism, Machine Learning, and statistical education innovation. She received the Academic Senate Distinguished Teaching Award 2007-2008 for her contributions to pedagogy. Research Interests: Holmes' research spans foundational topics in Bayesianism, data mining, and intuitionistic mathematics. She has pioneered methods for handling incomplete information in causal networks using entropy optimization and has contributed to probabilistic decision support systems. Her work bridges theoretical advancements with practical applications in education and healthcare. Editorial Contributions: Holmes serves on editorial boards for journals such as The Reasoner , Journal of Supercomputing , and International Journal of Knowledge-Based and Intelligent Information Systems . She has guest-edited special issues on maximum entropy applications and authored/co-edited influential books in data mining and Bayesian networks. Teaching and Mentorship: Holmes teaches both undergraduate and graduate courses in statistics, stochastic processes, and Bayesian methods. She has supervised PhD students and internships, emphasizing statistical computing and pedagogical innovation. Her courses include PSTAT 5A (Statistics), PSTAT 160A/B (Stochastic Processes), and specialized topics like Bayesian Networks (PSTAT 262BN). Awards and Recognition: Beyond her teaching award, Holmes has published over 50 peer-reviewed articles and authored/co-edited eight books. Her work appears in venues like Statistical Methods in Medical Research and Bayesian Inference and Maximum Entropy Methods . Labs and Teams: While no specific lab is named, her research collaborations involve interdisciplinary teams addressing challenges in data mining, healthcare analytics, and educational technology. Her editorial roles reflect active participation in global academic networks.

Sheikh K. Ghafoorمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• High-Performance Computing
• Autonomic Resource Management
• +۲ مورد دیگر

Sheikh K. Ghafoor is a Professor in the Department of Computer Science at Tennessee Technological University. He holds a Ph.D. in Computer Science from Mississippi State University (2007) and previously worked as a Research Associate at the Engineering Research Center (ERC) at Mississippi State University. His research focuses on parallel/distributed computing, high-performance computing (HPC) systems, autonomic resource management, and computer science education. Dr. Ghafoor has led grants from the National Science Foundation (NSF) and Department of Energy (DOE). Recent student achievements include: Ryan Marshall & Md Bulbul Sharif: Best Paper Award at ICCIT 2018 Sheikh Rabiul Islam: Presentations at IEEE Big Data 2018 Md Bulbul Sharif: Travel grant to SC18 Supercomputing Conference Key research themes include HPC optimization, programming models for heterogeneous systems, and education innovations in computing disciplines. Upcoming conference engagements include IEEE Big Data, IPDPS, and Supercomputing events.

Rod Fatoohiمشاهده پروفایل

استاد

• Computer Networking
• High-Speed Computing
• Supercomputers
• +۴ مورد دیگر

Dr. Rod Fatoohi is a tenured Professor and Chair of the Computer Engineering Department at San José State University. With expertise in computer networking, high-speed computing, and performance analysis, he has taught advanced courses in networking and parallel processing since 1996. Prior to academia, he spent eight years as a Research Scientist at NASA Ames Research Center, focusing on performance modeling and parallel computing. Education: Doctor of Philosophy (Old Dominion University, 1987) Master of Science in Computer Engineering (Syracuse University, 1982) Research Interests: Computer Networking High-Speed Computing Supercomputers Performance Analysis Parallel Processing Distributed Systems Middleware Technologies Scientific Awards: 2015 Supercomputing Conference (SC15) Test of Time Award (co-authored NAS Parallel Benchmarks paper from 1991) Professional Activities: Consultant for NASA, DOE, and IT industry Service on academic senate and university committees Panel reviewer for National Research Council Chaired University Information Technology Board

Naomi McClure-Griffithsمشاهده پروفایل

استاد

• Interstellar Gas Dynamics
• Galactic Magnetic Fields
• Galaxy Evolution
• +۳ مورد دیگر

Prof. Naomi McClure-Griffiths is an ARC Laureate Fellow and Associate Director (Research) at the Research School of Astronomy & Astrophysics, ANU. She holds a BA in Physics from Oberlin College and a PhD in Astrophysics from the University of Minnesota. Previously, she worked at CSIRO’s Australia Telescope National Facility. Her research focuses on interstellar gas and magnetic fields in the Milky Way and nearby galaxies, leading major projects like GASKAP and POSSUM with ASKAP. She chairs the SKA Science and Engineering Advisory Committee. Key awards include the 2006 Malcolm McIntosh Prize and 2015 Pawsey Medal, and she was elected a Fellow of the Australian Academy of Science in 2022. Her work includes studying gas evolution, magnetism, and feedback in galaxies. Recent projects involve the Galactic ASKAP survey (GASKAP) and polarization studies (POSSUM). She has supervised six students and contributed to numerous grants and collaborations, including international initiatives like the SKA. Her research has been published widely in journals like Astronomy & Astrophysics and Monthly Notices of the Royal Astronomical Society .

• High-Performance Computing
• Parallel Computing
• Linear Algebra
• +۳ مورد دیگر

Jack Dongarra is a Professor and Turing Fellow in Pure Mathematics. He is affiliated with research groups in the Data Science Institute, Software Systems, Numerical Analysis and Scientific Computing, and Applied Mathematics. His work contributes to UN Sustainable Development Goals through research in Digital Futures. Research Interests: High-Performance Computing, Parallel Architectures, Linear Algebra, GPU Computing, and Scientific Software Development. His fingerprint includes topics like Graphics Processing Units, Multicore Systems, and Performance Analysis. Publications (429 total) focus on algorithm design, high-performance computing frameworks, and exascale technologies. Notable contributions include the MAGMA project for GPU/multicore architectures and the development of the heFFTe library for exascale FFT. Awards: Turing Fellow Supervised 1 student. Active in datasets like 'Accelerating scientific computations with mixed precision algorithms' (2009) and impacts such as MAGMA's development.

Chenhui Shaoمشاهده پروفایل

دانشیار

• Smart manufacturing
• Machine learning
• Statistics
• +۱۵ مورد دیگر

Chenhui Shao is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, leading the Connected and Intelligent Manufacturing Systems (CIMS) Lab. His work bridges smart manufacturing, machine learning, and data science to advance industrial processes. Ph.D., Mechanical Engineering, University of Michigan (2016) M.A., Statistics, University of Michigan (2013) M.S.E., Industrial and Operations Engineering, University of Michigan (2013) B.E., Automation, University of Science and Technology of China (2009) Chenhui Shao's research focuses on smart manufacturing systems , integrating machine learning , statistics , and cyber-physical infrastructure to address challenges in in-situ process monitoring , quality control , and human-robot collaboration . His methodologies prioritize data efficiency and real-time decision-making for applications in additive manufacturing , ultrasonic welding , and nanomanufacturing . Recent publications highlight trends in edge computing for cost-effective monitoring, hybrid physics-data-driven models for geometric accuracy, and federated learning for cross-industry fault diagnosis. His lab's work spans 3D metrology , anomaly detection , and energy-efficient manufacturing processes . Scientific Awards: IEEE UEMCON Best Paper Award (2023) UIUC Dean’s Excellence in Research (2022) ASME Chao and Trigger Young Manufacturing Engineer Award (2021) NSF CAREER Award (2020) ASME Best Reviewers Award (2021) Chenhui Shao has advised numerous Ph.D. students, many of whom now hold roles at institutions like City University of Hong Kong and companies including Meta , Intel , and C3 AI . His lab collaborates with leading manufacturing entities and research centers such as Oak Ridge National Laboratory and National Center for Supercomputing Applications , developing solutions for cyber-physical infrastructure and agile production systems .

Piotr Magierskiمشاهده پروفایل

استاد

• Nuclear Theory
• Cold Atomic Gases
• Nuclear Dynamics
• +۳ مورد دیگر

Piotr Magierski is a Professor at the Faculty of Physics, Warsaw University of Technology, and an Affiliate Professor at the University of Washington's Department of Physics. His research focuses on quantum many-body systems, including atomic nuclei, cold atomic gases, and neutron stars. He utilizes supercomputers like Titan and LUMI for simulations in nuclear dynamics and superfluidity. Education & Positions: Professor, Warsaw University of Technology Affiliate Professor, University of Washington Research Interests: Explores superfluidity in cold atomic gases, nuclear fission/fusion, neutron star properties, and theoretical methods like Path Integral Monte Carlo and Density Functional Theory. His work bridges experimental and computational physics, addressing non-equilibrium phenomena and quantum chaos. Key Contributions: Pioneered studies on fermionic superfluid dynamics, including the BEC-BCS crossover. Developed computational frameworks for simulating nuclear fission using world-leading supercomputers. Awards: No explicit awards listed, but recognized through high-impact publications and media features (e.g., Science Daily, PNNL).

• Applied Mathematics
• Mathematical Physiology
• Fluid Dynamics
• +۳ مورد دیگر

Radostin D Simitev is a Professor of Applied Mathematics at the University of Glasgow, part of the Department of Mathematics and Statistics within the College of Science & Engineering. He has held this position since 2006, following roles at the University of Liverpool (2004–2006) and the University of Bayreuth (2000–2004). His research focuses on Mathematical Physiology and Fluid Dynamics/Magnetohydrodynamics, applying nonlinear analysis to cardiac tissue and studying convection in rotating systems relevant to planetary magnetism. Education: Dr. rer. nat. (PhD, Theoretical Physics), University of Bayreuth, 2004 MSc (Physics), University of Sofia, 2000 DipHE (Computer and Information Technologies), University of Mining and Geology, Sofia, 1998 Chartered Mathematician (CMath), Institute of Mathematics and Applications, 2017 Research Interests: Simitev’s work bridges applied mathematics and physics, including cardiac electrophysiology modeling, dynamo theory in planetary systems, and fluid dynamics in rotating environments. His studies explore excitable media, magnetic field generation, and multiscale biological systems. Grants & Projects: He leads and participates in major grants such as the STFC-funded Hybrid Dynamo Models of Solar Magnetic Cycles and the SofTMech Statistical Emulation Hub. His research also leverages supercomputing resources via STFC DiRAC allocations. Supervision & Teaching: Simitev has guided numerous PhD students, including recent graduates like Parag Gupta and Tahani Al Sariri. He teaches advanced courses such as Biological and Physiological Fluid Dynamics and Numerical Methods.