جست‌وجوی استاد و پژوهشگر

Le Gruenwaldمشاهده پروفایل

استاد

• Database Management
• Information Privacy and Security
• Mobile and Distributed Databases
• +۹ مورد دیگر

Dr. Le Gruenwald is the David W. Franke Professor and Samuel Roberts Noble Foundation Presidential Professor at the School of Computer Science, University of Oklahoma. She also served as Program Director for Data Management Systems at the National Science Foundation (NSF) and held roles including Director of the University of Oklahoma's School of Computer Science. Her academic journey includes a PhD from Southern Methodist University (1990), an M.S. from the University of Houston, and a B.S. in Physics from the University of Saigon, Vietnam. Research Interests: Her work focuses on Database Management Information Privacy & Security Mobile/Distributed Databases Data Mining Quantum Computing Applications in Databases Outlier Detection in Data Streams Key Projects: Includes NSF-funded research on GPU-accelerated spatial data processing, cloud query optimization, and big data hubs. Notable projects: "Spatial Data Management on GPUs" (2013), "Big Data Innovation Hub for the South" (2015), and quantum data science initiatives (2023). Awards: 2012 NSF Directorate Recognition 2007 Distinguished Alumnus Award (SMU) 2009 ACM Recognition Award 2016-2017 Semantic Big Data Workshop Leadership Advising & Grants: Oversees NSF grants totaling millions, focusing on cyberinfrastructure, ecological forecasting, and medical data analytics. No formal student advisees listed but collaborates extensively with researchers globally. Labs & Teams: Leads initiatives in quantum database optimization, mobile-cloud systems, and GPU-accelerated spatial analytics within OU's School of Computer Science.

Sridhar Radhakrishnanمشاهده پروفایل

استاد

• Computer Networks
• High Performance Computing
• IoT and SDN Development
• +۵ مورد دیگر

Dr. Sridhar Radhakrishnan is a Professor and former Director of the School of Computer Science at the University of Oklahoma since 1990. He holds a PhD from Louisiana State University and multiple undergraduate degrees in Computer Science and Physics. His research focuses on network protocols, IoT/SDN development, high-performance computing, and algorithm design. He has led NSF-funded projects like the OneOklahoma Friction Free Network and TRAMS transit management system. With over 100 publications, his work bridges theoretical computer science with practical applications in critical infrastructure protection, graph compression, and adversarial network defense. Active in IEEE and ACM, he reviews for major conferences and advises on federal transportation and cybersecurity initiatives. Education: PhD, Computer Science, Louisiana State University (1990) M.S., System Sciences, Louisiana State University M.L.I.S., Library and Information Services, Louisiana State University B.S., Computer Science, University of South Alabama B.S., Physics, Vivekananda College, University of Madras Research Interests: His work spans network protocol development, RFID systems, parallel computing architectures, and resilience against weaponized disinformation. Recent breakthroughs include optimization of infrastructure networks under adversarial conditions and scalable graph compression techniques for massive datasets. Grants & Projects: NSF OneOklahoma Friction Free Network (2014-2016) Federal Transit Administration TRAMS System (2012-Current) PARA-TRANSIT Operations Technology (2005-2010) Awards & Recognition: While no formal awards listed, his sustained federal funding and high-impact publications reflect industry esteem. Lab & Teams: Leads research groups focused on IoT/SDN integration and network resilience, collaborating with federal agencies like FHWA and ODOT.

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

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

• Applied Mathematics
• Computational Science
• Groundwater Management
• +۴ مورد دیگر

Professor Elena Papadopoulou is a faculty member at the Technical University of Crete within the School of Mineral Resources Engineering . Her research focuses on applied mathematics and computational science , addressing problems in groundwater dynamics, tumor modeling, and numerical methods for partial differential equations. Key Research Areas: Stochastic optimization for coastal aquifer management GPU-accelerated simulations for biomedical applications High-order numerical schemes for reaction-diffusion systems Unified transforms in multi-domain PDEs She has published extensively on saltwater intrusion modeling , brain tumor invasion , and parallel computing algorithms . Her work integrates mathematical modeling with environmental and medical challenges , emphasizing computational efficiency and environmental sustainability. Contact : epapadopoulou@tuc.gr

• Computer Science
• Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Wei Cui is an active researcher with affiliations to multiple institutions, including Beijing Institute of Technology, Shandong University of Science and Technology, and others. Their work spans diverse domains such as artificial intelligence , robotics , quantum computing , and environmental engineering . Research Interests include multimodal fake news detection, vehicle routing optimization, biomedical signal processing, and remote sensing applications. Recent publications focus on machine learning for urban infrastructure , humanoid robotics , and quantum key distribution . Article Trends reveal a strong emphasis on interdisciplinary AI (e.g., healthcare waste logistics, 3D perception) and data-driven optimization . Collaborations include experts in control systems , quantum physics , and environmental science .

Markus Steinbergerمشاهده پروفایل

دانشیار

• GPU Computing
• Computer Graphics
• Parallel Processing
• +۳ مورد دیگر

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

Jake Renzellaمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Educational Technology
• Learning Analytics
• +۳ مورد دیگر

Dr Jake Renzella is a Senior Lecturer at the University of New South Wales' School of Computer Science and Engineering (CSE), where he serves as Co-Head of the Computing and Education research group and Director of Studies for Computer Science. His work focuses on Pedagogical Generative AI and education technology, with a particular emphasis on compiler tools like the Debugging C Compiler (DCC) and its AI debugger, as well as the Formatif teaching platform. Research: Premier computer science education conferences (SIGCSE TS, ICSE), AI-driven educational tools, learning analytics Collaboration: Key role in Day of AI Australia initiative Scientific Awards: 2024 Australian Financial Review Higher Education Innovation Award 2023 UNSW Engineering Faculty Education Award 2022 UNSW Teaching Excellence Award Associate Fellow of the Higher Education Academy Grants: $2,165,000 total including Google.org Generative AI Accelerator ($1.5M), NVIDIA Academic Grant (cloud GPU hours), and multiple Google/UNSW grants for inclusion research and teaching innovation.

• Virtual Reality
• Computer Graphics
• Scientific Visualization
• +۲ مورد دیگر

Eric Gene Shaffer serves as a Teaching Associate Professor at the Siebel School of Computing and Data Science, University of Illinois, with research spanning virtual reality, computer graphics, and scientific visualization applications in physics and education. His primary research areas include Virtual Reality, Computer Graphics, Scientific Visualization, Human-Computer Interaction, and Educational Technology. Shaffer develops GPU-accelerated rendering techniques for ray tracing optimization, immersive tools for gravitational wave visualization, and VR-based educational games focused on user comfort and crystallography instruction. His work bridges computational methods with practical STEM learning outcomes. Analysis of recent publications reveals consistent innovation in VR interaction paradigms and cross-domain applications. Key trends include memory-efficient rendering architectures, gaze-depth manipulation interfaces, and equity-focused decision-making simulations. His research demonstrates strong interdisciplinary collaboration across computer science, physics, and engineering education, with publications appearing in venues like ISVC and CHI.

• Biomedical Engineering
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Ujjal Kumar Bhowmik is a Teaching Assistant Professor in the Department of Electrical & Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. His expertise spans biomedical imaging, signal processing, and engineering education. He has held academic roles since 2008, with a focus on curriculum innovation and student learning experiences in ECE courses. Education: Ph.D. in Electrical Engineering, University of Alabama in Huntsville (2012) MSE in Electrical Engineering, University of Alabama in Huntsville (2008) M.Tech in Electrical Engineering, Indian Institute of Technology Kharagpur (India) B.Sc. in Electrical & Electronic Engineering, Khulna University of Engineering and Technology (Bangladesh) Research Interests: His work focuses on mitigating motion artifacts in 3D medical imaging systems, computational methods for engineering education, and GPU-accelerated solutions for environmental models. He has pioneered algorithms for crack segmentation in X-ray CT images and adaptive learning paths in parallel computing curricula. Awards & Honors: Collins Scholar (Grainger College of Engineering, 2019-2020) Strategic Instructional Innovations Program (SIIP) Award 2019 Kaman Best Teacher Award (2015, Catholic University of America) Best Technical Paper Award at IEEE SoutheastCon 2014 Phi Kappa Phi Honor Society (2011) Recent Contributions: His 2023 publications address curriculum improvements for parallel computing education, while earlier work (e.g., 2014) demonstrated a best paper in biomedical sensor design. He integrates computational thinking into lower-level courses through extended learning modules.

Christoforos Kozyrakisمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Cloud Computing
• +۳ مورد دیگر

Christoforos Kozyrakis is a Professor of Electrical Engineering and Computer Science at Stanford University. He leads the MAST research group and serves as faculty director of the Stanford Platform Lab. His research focuses on computer architecture, systems, cloud computing, and machine learning integration. Education: B.S. from University of Crete (undated), Ph.D. in Computer Science from UC Berkeley (2002). He holds ACM and IEEE Fellowships and has received awards including the ACM SIGARCH Maurice Wilkes Award (2015) and NSF Career Award (2006). Research interests include cloud computing systems, ML-driven systems optimization, and secure hardware-software co-design. Recent work emphasizes scalable storage fabrics for ML training and secure GPU execution frameworks. Key contributions span 207+ publications, including influential papers on database systems, storage optimization, and cloud security. Current projects explore low-latency storage systems (RAIL), composite storage fabrics (Tectonic-Shift), and secure enclave technologies (ShEF). Awards: ACM SIGARCH Maurice Wilkes Award (2015), IEEE Fellow (2014), IBM Faculty Award (2006) Advising: Supervises doctoral students like Athinagoras Skiadopoulos and master's advisees including Andrew Bartolo Labs: MAST Research Group and Stanford Platform Lab

Suhail Yousafمشاهده پروفایل

مدرس

• Distributed Computing
• Machine Learning
• Environmental Monitoring
• +۴ مورد دیگر

Suhail Yousaf is a Lecturer in Computer Science at the School of Computer Science & Engineering, Constructor University Bremen gGmbH. His academic career spans roles at the University of Engineering and Technology Peshawar, Pakistan, where he served as an Assistant Professor (2018-2023) and Lecturer (2013-2018, 2005-2007). He earned his PhD and MSc in Distributed and Parallel Computer Systems from Vrije Universiteit Amsterdam (2009-2014, 2007-2009), and an MSc in Computer Science from Quaid-i-Azam University, Islamabad (2002-2004). Education PhD in Distributed Computer Systems, Vrije Universiteit Amsterdam (2009-2014) MSc in Parallel and Distributed Computer Systems, Vrije Universiteit Amsterdam (2007-2009) MSc in Computer Science, Quaid-i-Azam University (2002-2004) His research focuses on distributed computing, machine learning, and environmental monitoring. Key areas include earthquake phase classification, landslide detection, and PM2.5 forecasting using neural networks and time-series analysis. He has also contributed to GPU acceleration in optimization algorithms and fake news detection via ensemble methods. Recent publications highlight his interdisciplinary work in environmental science and high-performance computing. For instance, the 2024 paper 'ConvEQ' applies convolutional neural networks to seismic signal processing, while 2020 studies explore crop monitoring, air quality prediction, and GPU-based simplex method improvements. Teaching Roles MDE-CS-02 Parallel and Distributed Computing (Spring 2025, Spring 2024) SDT-104 Scientific Programming with Python (Fall 2024) CH-250 Programming in Python and C++ (Fall 2023) ACS-201 Databases and Web Services (Fall 2024, Fall 2023) ACS-106 Distributed Development (Spring 2024, Fall 2023)

Minsi Chenمشاهده پروفایل

پژوهشگر

• Computer Science
• Augmented Reality
• Data Visualization
• +۷ مورد دیگر

Minsi Chen is a Researcher at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Computer Science . They serve as Subject Area Leader (CIS - U/G) and are a member of the Centre for Industrial Analytics and Centre for Sustainable Computing . Their work spans interdisciplinary research with a focus on computer science, augmented reality, and medical applications. Research Interests : Minsi Chen's research expertise includes real-time rendering , volume rendering , augmented reality , multimodal sensing data fusion , and visualization of large datasets . They have contributed to advancements in medical imaging , hybrid system modeling , and graph neural networks , with applications in trauma surgery simulation , automotive systems , and industrial analytics . Collaborative Activity : Recent research outputs indicate strong collaborations with institutions such as University of Huddersfield , CERN , and University of Leeds . Their work intersects with UN Sustainable Development Goals related to good health , industry innovation , and climate action through sustainable computing initiatives.

Peng Jiangمشاهده پروفایل

استادیار

• Machine Learning Systems
• High-Performance Computing
• Graph Processing & Mining
• +۵ مورد دیگر

Peng Jiang is an Assistant Professor in the Computer Science Department at the University of Iowa. His research focuses on machine learning systems, high-performance computing, and graph processing, with a particular emphasis on compiler and programming techniques for GPU acceleration. He earned his Ph.D. in Computer Science from The Ohio State University in 2019 under Dr. Gagan Agrawal. Education: Ph.D., The Ohio State University, 2019 His work spans sparse training, knowledge graph embedding, and subgraph matching, often leveraging fine-grained parameter management and GPU optimization. Key trends in his publications include compiler design for high-performance systems, parallel programming models, and performance-aware weight pruning for neural networks. Scientific Awards 2024 NSF CAREER Award Peng Jiang has collaborated extensively with researchers such as Lihan Hu, Yihua Wei, Shihui Song, and Gagan Agrawal. His contributions to sparse matrix multiplication, distributed learning communication optimization, and PIM architecture-aware frameworks highlight his expertise in bridging machine learning and systems research.

• High-Performance Computing
• Data Governance
• Big Data Analytics
• +۴ مورد دیگر

Anna Queralt Calafat is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics. Her research focuses on High-Performance Computing (HPC), distributed systems, and data governance, with notable contributions in knowledge graphs, cloud-edge continuum management, and parallel workflow optimization. She leads projects funded by European and national grants, including contributions to strategic research agendas like ETP4HPC. Queralt has supervised doctoral students like Jonathan Marti and Rizkallah Touma, and her work spans over 100 publications in top venues such as Future Generation Computer Systems and the International Semantic Web Conference. She actively participates in conference committees and has received a Best Student Paper Award for collaborative research. Her educational background includes a degree in Computer Engineering and a doctorate in Software. She is part of research groups inSSIDE and DTIM, advancing areas like HPC integration with big data analytics. Key projects include automated data lifecycle management and fog-to-cloud distributed processing. Her work bridges theoretical models with practical systems like DataClay and PyCOMPSs, emphasizing scalable and efficient computing solutions.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Languages
• Performance Programming
• +۷ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.