جست‌وجوی استاد و پژوهشگر

Melissa C. Smithمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Melissa C. Smith is a Professor of Electrical and Computer Engineering and Associate Dean for Graduate Studies at Clemson University. She holds a Ph.D. from the University of Tennessee and degrees from Florida State University. Her research focuses on machine learning, reconfigurable computing, and high-performance systems, with applications in embedded systems and interdisciplinary scientific advancements. Before joining Clemson in 2006, she was a research associate at Oak Ridge National Laboratory (ORNL), contributing to projects like the Spallation Neutron Source and PHENIX experiments. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee M.S., Electrical Engineering, Florida State University B.S., Electrical Engineering, Florida State University Research Interests: Machine Learning and AI High-Performance and Reconfigurable Computing System Performance Modeling Embedded Systems Articles Summary: Her recent work spans machine learning applications, GPU/FPGA architectures, speech enhancement, and medical systems. Key themes include optimizing heterogeneous computing for real-time and scientific workloads, and advancing interdisciplinary solutions through architecture-application co-design. Lab & Collaborations: Leads the Future Computing Technologies Lab and collaborates with ORNL and national labs on projects like GEMmaker and HPC-enabled medical systems.

Jon Weissmanمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Jon Weissman is a Professor of Computer Science at the University of Minnesota, Twin Cities. His research focuses on distributed systems, edge and cloud computing, and high-performance computing (HPC), aiming to enhance performance, reliability, and energy efficiency. Education: Ph.D. in Computer Science, University of Virginia (1995) M.S. in Computer Science, University of Virginia (1989) B.S. in Applied Mathematics and Computer Science, Carnegie-Mellon University (1984) His research explores edge and cloud computing, IoT, and HPC, including subtopics like storage systems, resource management, and security. Publications highlight trends in adaptive prefetching, compressed sensing for medical devices, and IoT-informed autoscaling. Scientific Awards: NSF CAREER Award (1995) Senior Member, IEEE He has advised Ph.D. students like Albert Jonathan, Kwangsung Oh, and Francis Liu. His lab is located in 4-204A Keller Hall, and he serves on steering committees for conferences like HPDC.

Cecilia R. Aragonمشاهده پروفایل

استاد

• Human-Centered Data Science
• Human-Centered Artificial Intelligence
• Human-Computer Interaction
• +۵ مورد دیگر

Cecilia R. Aragon is a Professor in the Department of Human Centered Design & Engineering at the University of Washington, where she also serves as an Adjunct Professor in Computer Science & Engineering, Electrical and Computer Engineering, and the Information School. She is additionally a Senior Data Science Fellow at the eScience Institute. Aragon directs the Human-Centered Data Science Lab and has made significant contributions at the intersection of human-computer interaction and data science. Her research interests focus on human-centered data science, human-centered artificial intelligence, human-centered machine learning, human-computer interaction (HCI), computer-supported cooperative work (CSCW), visual analytics, aviation and astronautics sociotechnical systems, and emotion in informal text communication. Aragon's work bridges technical and social aspects of data science, particularly examining how humans interact with and gain insight from large datasets through both quantitative and qualitative methods. Aragon's recent publications demonstrate a strong focus on understanding online communities, sentiment analysis, distributed mentoring systems, and the ethical implications of AI. Her work spans multiple disciplines including social computing, data visualization, and astrophysics data analysis, showing her interdisciplinary approach to human-centered data science. Presidential Early Career Award for Scientists and Engineers (PECASE) 2008 Fulbright Fellowship 2017-18 HCDE Faculty Innovator in Research Award, University of Washington, 2015 Distinguished Alumni Award, Computer Science, University of California, Berkeley, 2013 Top 25 Women of the Year, Hispanic Business Magazine, 2009 Aragon has secured over $28 million in research funding from organizations including the National Science Foundation, National Institute of Standards and Technology, Department of Energy, Gordon and Betty Moore Foundation, Alfred P. Sloan Foundation, Washington Research Foundation, and industry partners like Microsoft and Intel. Her educational background includes a Ph.D. in Computer Science from UC Berkeley (2004), an M.S. in Computer Science from UC Berkeley, and a B.S. with Honors in Mathematics from Caltech. She leads the Human-Centered Data Science Lab and is affiliated with the eScience Institute, the Nearby Supernova Factory, and various research groups focused on data-intensive scientific collaborations. Her work on collaborative visual analytics systems like Sunfall has had significant impact in both academic and applied settings.

Kishalay Mitraمشاهده پروفایل

استاد

• Optimal Control Under Uncertainty
• Bioenergy Supply Chain Design
• Surrogate Multi-Objective Optimization
• +۱۳ مورد دیگر

Kishalay Mitra is a Professor at the Indian Institute of Technology Hyderabad , with affiliations to the Department of Chemical Engineering , Department of Climate Change , and Department of Artificial Intelligence . He also holds visiting professorships at Washington University in St. Louis and University of Washington, Seattle . His work in the Global Optimization & Knowledge Unearthing Laboratory (GOKUL) spans interdisciplinary optimization, machine learning, and their applications in industrial-scale engineering problems. Education : Ph.D. from IIT Bombay. Research Interests : Mitra's research focuses on optimization under uncertainty , surrogate modeling , multi-objective optimization , and integrating machine learning with physics-based models . His work addresses real-world challenges in wind energy , bioenergy supply chains , chemical process control , nanoscience , and environmental modeling (e.g., PM10 spatiotemporal analysis, forest fire prediction, and carbon capture). Article Trends : His recent publications emphasize wind energy systems (layout optimization, yaw control, forecasting), materials science (precipitate growth prediction, polymerization), and industrial processes (crystallization, grinding circuits). Techniques include neural operators , Bayesian optimization , generative adversarial networks (GANs) , and explainable AI .

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics (CFD)
• +۴ مورد دیگر

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.

David Thomasمشاهده پروفایل

استاد

• Event-driven Computing
• FPGAs
• Digital Architectures
• +۳ مورد دیگر

Professor David Thomas holds the position of Professor in Computer Engineering at the University of Southampton's Electronics and Computer Science Department. His research focuses on the intersection of software and hardware, particularly leveraging FPGAs for novel digital architectures and event-driven computing. He has a notable academic trajectory, having previously served as a Lecturer and Senior Lecturer at Imperial College London before joining Southampton in 2021. Dr. Thomas is actively involved in supervising PhD students and contributes to interdisciplinary research projects funded by the EPSRC, such as the SONNETS initiative exploring scalable event-triggered systems. Education: BSc in Computer Science (Imperial College London), PhD in Digital Architectures (Imperial College London). Postdoctoral roles included Research Associate and Research Fellow at Imperial's Department of Computing. Research Interests: Event-driven computing, FPGA-based systems, high-level synthesis, and high-performance computing. His work emphasizes practical implementations of theoretical models, such as custom processors and application-specific accelerators. Current projects include optimizing random number generation for FPGAs and exploring meta-programming techniques for hardware design. Advising and Grants: Supervises multiple PhD students in areas like neuromorphic computing and algorithm optimization. Active in securing funding for distributed system architectures and FPGA-based solutions. Labs/Teams: Member of the Cyber Physical Systems research group. Collaborates with interdisciplinary teams on projects like POETS (Partially Ordered Event-Triggered Systems) for large-scale parallel computing.

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +۵ مورد دیگر

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

• Computational Fluid Dynamics (CFD)
• Wave Hydrodynamics
• Wave Modeling
• +۹ مورد دیگر

Hans Bihs is a Professor in the Department of Civil and Environmental Engineering, Faculty of Engineering. His research focuses on computational fluid dynamics (CFD), wave hydrodynamics, and wave-structure interaction using the open-source framework REEF3D. Key Research Areas: CFD simulations, wave modeling, floating body dynamics, ocean wave energy, aquaculture hydrodynamics, sediment transport, and high-performance computing. Projects: ERC Consolidator Grant PARTRES (2023-2028), EEA Grants Portugal SurfWave (2023), NFR KPN IPIRIS (2021-2025), EEA Baltic SolidShore (2021-2024), NTNU's MAPLE (2022-2025), and DigiCoast (2021-2024). Email: hans.bihs@ntnu.no His recent publications (2025-2020) analyze fluid-structure interaction, ship-induced waves, floating offshore wind turbines, submerged vegetation, and coastal structures using advanced CFD techniques. Topics include wave hydrodynamics, turbulence, and numerical modeling for marine and aquaculture systems.

Ada Gavrilovskaمشاهده پروفایل

استاد

• Systems Software
• Emerging Technologies
• High-Performance Computing
• +۷ مورد دیگر

Ada Gavrilovska is a Professor at Georgia Tech's School of Computer Science under the College of Computing. Her work focuses on systems software for emerging technologies, including hybrid memory systems, edge computing, and cloud infrastructure. She leads projects in the PRISM Center and ADA Center , with funding from NSF, DoE, SRC, and industry leaders like Cisco and VMware. Education: PhD in Computer Science, Georgia Tech (2004) Research Interests: Designing systems for new hardware and applications, including edge computing, heterogeneous memory management, and LEO satellite platforms. Her work bridges low-level OS mechanisms with high-level distributed systems challenges. Recent Publications highlight trends in LEO satellite resource scheduling Edge-based ML preprocessing Hybrid memory OS abstractions Disaggregated graph analytics Compiler-assisted performance optimization Scientific Awards: Best paper, NFV World Congress (2016) Spotlight paper, IEEE Transactions on Cloud Computing (2014) ISCA-50 25-year retrospective (2023) Advising & Grants: Ada has mentored over 15 PhD students and 10 MS students, with research supported by NSF, DoE, SRC, and industry grants. She serves as PI in the SRC/DARPA PRISM Center.

• Structural Engineering
• Earthquake Engineering
• Bridge Engineering
• +۳ مورد دیگر

Andrew S. Whittaker is a SUNY Distinguished Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York . He serves as Director of the Institute of Bridge Engineering and Interim Director of the Stephen Still Institute for Sustainable Transportation and Logistics , both within the School of Engineering and Applied Sciences . A registered Civil and Structural Engineer in California, Whittaker specializes in structural and earthquake engineering, bridge engineering, blast and impact engineering, performance-based engineering, and nuclear structures. Research Interests: His work focuses on seismic isolation systems for nuclear reactors, fluid-structure interaction in advanced reactor vessels, gamma radiation effects on materials, and the dynamic behavior of graphite blocks in high-temperature gas reactors (HTGRs). He also explores the commodification of microreactors and soil-structure interaction for seismically isolated facilities. Scientific Awards: Distinguished Member, American Society of Civil Engineers (2025) Untermyer & Cisler Reactor Technology Medal (2023) Nathan M. Newmark Medal (2023) Fellow of multiple societies (ASCE, SEI, ACI) Awards and grants highlight his leadership in nuclear safety, seismic engineering, and reactor design.

Irina Rishمشاهده پروفایل

استاد

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +۱۳ مورد دیگر

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.

Andrew Rau-Chaplinمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +۱۴ مورد دیگر

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Minna Palmrothمشاهده پروفایل

استاد

• Space Physics
• Computational Astrophysics
• Plasma Dynamics
• +۵ مورد دیگر

Minna Palmroth is a Professor of Computational Space Physics at the University of Helsinki 's Faculty of Science , leading the Department of Physics 's Space Physics Research Group. She directs the Kestävän avaruustieteen ja -tekniikan huippuyksikön (Centre of Excellence in Sustainable Space Science and Technology) and serves as the principal investigator for the Vlasiator hybrid-Vlasov simulation framework.

Estela Suarezمشاهده پروفایل

استاد

• high performance computing (HPC)
• heterogeneous HPC system architectures
• modular supercomputing architecture (MSA)
• +۴ مورد دیگر

Prof. Dr. Estela Suarez is a Professor of High Performance Computing at the Institute for Computer Science, University of Bonn (W2 in the Jülich Model) and Joint Lead of the Division "Novel System Architecture Design" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH. She also leads the Research Group "Next Generation Architectures and Prototypes" at JSC and serves as Spokesperson of Helmholtz Information Program 1, Topic 2. Currently on sabbatical during the 2024/2025 and 2025 academic years, she remains active in research leadership roles. 2010: PhD in Physics from University of Geneva, Switzerland 2004: Master in Physics, Specialization in Astrophysics, University Complutense of Madrid, Spain Professor Suarez specializes in high performance computing with particular expertise in heterogeneous HPC system architectures and modular supercomputing architecture (MSA). Her research spans hardware prototyping and evaluation, system software development, operational data analysis, and co-design methodologies. She has pioneered approaches to address hardware heterogeneity through system-wide orchestration of diverse computing resources, enabling more efficient scientific computing across multiple domains. Her work bridges theoretical computer architecture with practical implementation challenges in exascale computing environments, focusing on real-world applications that require specialized hardware configurations. Professor Suarez's publication record shows a clear evolution from foundational work on the DEEP project (2016) through the development of modular supercomputing concepts (2019-2021) to current applications across diverse scientific domains (2022-2024). Her recent publications demonstrate how modular architectures can be effectively applied to climate modeling, neuroscience simulations, quantum chemistry calculations, and other computationally intensive fields. This trend highlights her focus on practical implementation challenges and the growing importance of adaptable computing architectures in modern scientific research. 2023/2024 Lehrpreis der Universität Bonn: UniBonn teaching award Professor Suarez has secured significant research funding through major projects including NUMERIQS (Projects A05, B02, and Z02), European Processor Initiative (EPI), DEEP-SEA (Software for Exascale Architectures), IFCES2 (optimization of simulation algorithms for exascale supercomputers), and AIDAS (virtual laboratory between Forschungszentrum Jülich and CEA on AI and data analytics). While currently not accepting new students due to sabbatical, she has previously mentored graduate students in high performance computing techniques and has delivered numerous invited lectures at international conferences. Professor Suarez leads the "Next Generation Architectures and Prototypes" research group at JSC and serves as Joint Lead of the "Novel System Architecture Design" division. She chairs the Research and Innovation Advisory Group (RIAG) from EuroHPC Joint Undertaking since 2024. Her work involves close collaboration with international research teams on advancing supercomputing architectures, including contributions to the University of Bonn's new HPC system "Marvin" which ranks on both the TOP500 and GREEN500 lists.

Johan Meyersمشاهده پروفایل

استاد

• Wind Energy
• Turbulent Flow Simulation
• Flow Control & Optimization
• +۵ مورد دیگر

Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.