Prof. Dr.-Ing. Michael Uelschen is a Professor of Software Engineering for Technical Systems at Osnabrück University of Applied Sciences, part of the Faculty of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and has over a decade of industry experience in automotive software development. His research focuses on energy-efficient embedded systems, model-driven software engineering, multicore systems, and medical technology applications such as motion analysis for rehabilitation. He teaches courses in software engineering, embedded systems, and parallel algorithms at both bachelor's and master's levels. Research Interests : Software Engineering for Automotive Embedded Systems, Power Optimization, Energy-Aware Design Patterns, Medical Motion Analysis (gait stability), IoT Sustainability, and Process Improvement (CMMI). His work often bridges theoretical models with practical implementation in real-time systems. Key Projects : Developed a contactless monitoring system for elderly/patient care using commodity electronics, supported by Bayer Healthcare Grants4Apps. Also contributed to frameworks for energy-efficient embedded systems design patterns and algorithms for kinematic analysis in clinical settings. Teaching : Leads courses like 'Software Engineering for Parallel Systems', 'Embedded Systems', and 'Object-Oriented Design'. Supervises student projects and theses in automotive and medical software domains.
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱۶-۲۵ از ۲۵ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Juan Luis Aragon Alcaraz is a Full Professor in Computer Architecture at the University of Murcia (Spain), affiliated with the Faculty of Informatics and the Department of Computer Engineering and Technology. He obtained his PhD in Computer Engineering from the University of Murcia in 2003 and held a postdoctoral position as a Visiting Assistant Professor and Researcher at the University of California, Irvine. He has held visiting researcher positions at EPFL (2013) and Princeton University (2015–2022). His research focuses on computer architecture, emphasizing heterogeneous parallel systems, GPUs, memory hierarchies, and energy-efficient microarchitecture design. He has advised 7 PhD theses and co-authored over 60 publications in top-tier conferences and journals. His work includes contributions to GPU-accelerated medical imaging, energy-efficient graphics pipelines, and hardware-software co-design for parallel systems. Key research trends in his articles include optimizing GPU performance for real-time applications, energy efficiency in graphics rendering, and scalable latency tolerance in manycore systems. His patents include innovations in presbyopia correction devices leveraging GPU and FPGA technologies. Dr. Aragón has taught numerous courses in computer architecture and design, including Computer Architecture and Organization, Advanced Aspects in Multicore Architectures, and Embedded Systems Technologies. His labs and collaborations span academic-industrial partnerships, emphasizing practical applications of his theoretical contributions.
Venetis Ioannisمشاهده پروفایل
استادیار
Venetis Ioannis is an Assistant Professor at the Department of Informatics, School of Information and Communication Technologies, University of Piraeus. His research focuses on operating systems, parallel computing, and high-performance computing applications in seismology and computational neuroscience. His research interests include: Operating Systems Parallel Computing Models Scheduling in Parallel Systems Real-Time Parallel Applications Seismology and Computational Neuroscience GPU and Many-Core Optimization Recent publications highlight his work on heterogeneous cluster scheduling, GPU-based neuronal modeling, and seismic event detection using grid search algorithms. Venetis has participated in European and national research programs related to parallel computing and high-performance applications. He teaches courses such as Operating Systems, Parallel Processing, and Assembly Language Programming, with a focus on practical implementations in high-performance environments.
Sebastian Altmeyer is a Professor holding the Chair for Embedded Systems at the Institute of Computer Science within the Faculty of Applied Computer Science at the University of Augsburg. He has been in this position since August 2019, following his role as Assistant Professor at the University of Amsterdam from September 2017 to July 2019. Professor Altmeyer's research focuses on real-time systems and embedded computing, with particular expertise in multi-core architectures, cache analysis, scheduling algorithms, and response time analysis. His work bridges theoretical computer science with practical applications in safety-critical systems. He leads research projects including ADMORPH EU H2020, Multi-Core Response Time Analysis (MRTA), Design of Hardware Transactional Memory for Embedded Systems, and several others focused on manycore architectures and embedded system design. His extensive publication record demonstrates consistent contributions to the field of real-time systems, with recent work focusing on systematic evaluation techniques for real-time systems, multicore interference analysis, safety-critical edge robotics, and hardware transactional memory. His research shows a clear trajectory toward addressing the challenges of modern embedded systems with increasingly complex hardware architectures while maintaining real-time guarantees. Professor Altmeyer leads the Chair for Embedded Systems team at the University of Augsburg, which includes Prof. Dr. Theo Ungerer, Petra Zettl, Dr.-Ing. Martin Frieb, and several other researchers. The team conducts research in areas including hardware transactional memory, manycore architectures, and real-time system analysis. Their work has practical applications in safety-critical systems where timing guarantees are essential.
Md Farhadur Rezaمشاهده پروفایل
استادیار
Md Farhadur Reza serves as an Assistant Professor in the Department of Mathematics and Computer Science at Eastern Illinois University (EIU). His expertise spans resource management, interconnection networks, multi-core architectures, and machine learning/artificial intelligence, with active research and teaching in computer science courses including Machine Learning and Operating Systems. Dr. Reza's research focuses on optimizing Networks-on-Chip (NoC) architectures through innovative application mapping techniques. He develops energy-efficient solutions for deep neural network acceleration using reinforcement learning, convolutional neural networks, and nature-inspired algorithms like the Dragonfly Algorithm. His work bridges computer architecture and AI to solve performance bottlenecks in manycore systems. Recent publications (2023-2025) reveal a strong trend in AI-driven hardware optimization, particularly for healthcare applications like dementia prediction and energy-aware NoC designs. Key contributions include robust CNN frameworks for medical diagnostics and adaptive multi-objective mapping strategies for DNN workloads. Scientific Awards and Grants Achievement and Contribution Award (ACA) in Balanced category (2024) CLAS Travel Award (2022, 2023, 2024) EIU Council on Faculty Research Grants (Summer 2022, Fall 2023, Summer 2024, Fall 2024) Dr. Reza actively contributes to academic service as General Co-Chair and Technical Program Co-Chair for the IEEE/ACM International Workshop on Network-on-Chip Architectures (NoCArc) 2023-2024. He maintains professional affiliations with IEEE, ACM, and IEEE Young Professionals, and has delivered invited talks internationally including at National Yang Ming Chiao Tung University, Taiwan (April 2024).
Mark C. Jeffreyمشاهده پروفایل
استادیار
Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.
Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.
Harini Ramaprasadمشاهده پروفایل
استاد آموزشی
Harini Ramaprasad is a Teaching Professor and Associate Dean for Undergraduate Programs and Student Success at the University of North Carolina at Charlotte's College of Computing and Informatics. She holds a Ph.D. in Computer Science from North Carolina State University (2008). Her research focuses on real-time embedded systems, cyber-physical systems, and computer science education, with recent work addressing predictable execution on multi-core architectures and cybersecurity education through interactive modules. Dr. Ramaprasad previously served as Director of Undergraduate Programs at the College and was an Assistant Professor at Southern Illinois University Carbondale. She is an active member of IEEE, ACM, and SWE. Her teaching expertise spans operating systems, real-time systems, software design, and database systems. Her recent publications emphasize practical educational strategies (e.g., using classroom games to develop soft skills) and technical advancements in real-time scheduling, embedded system security, and multi-core architecture optimizations. Despite no listed scientific awards, her contributions to pedagogy and real-time systems highlight impactful interdisciplinary work.
Ioannis Venetisمشاهده پروفایل
استادیار
Ioannis Venetis is an Assistant Professor at the University of Piraeus, School of Information and Communication Technologies, Department of Informatics, specializing in Operating Systems and Parallel Computing. He earned his PhD from the Department of Computer Engineering and Informatics at the University of Patras. His research spans programming models for parallel systems, scheduling optimization, and applications in computational neuroscience and seismology. Research Interests Operating Systems Parallel Computing Scheduling Algorithms High-Performance Computing Computational Neuroscience Seismology Projects Participation in European and national research programs Development of Gisola (GPU-accelerated seismic inversion tool) His teaching portfolio includes courses like Operating Systems, Parallel Processing, and Symbolic Programming. Articles highlight expertise in GPU acceleration, tridiagonal solvers, sensor networks, and many-core architectures. Notable contributions include work on Chimera states in neuronal dynamics and hierarchical workload scheduling frameworks.
- Programming systems of heterogeneous machines
- Parallelization and optimization of deep learning frameworks
- Deep learning compilers/architectures
- +۱ مورد دیگر
Jaejin Lee is a Professor in the Department of Computer Science and Engineering at Seoul National University (SNU) and serves as the Director of the Center for Manycore Programming and Multicore Computing Research Laboratory. He holds a BS in Physics from SNU (1991), an MS in Computer Science from Stanford University (1995), and a PhD in Computer Science from the University of Illinois at Urbana-Champaign (1999), where his research was supported by IBM and Korea Foundation for Advanced Studies fellowships. Research Focus: His work centers on heterogeneous computing systems with expertise in GPU/FPGA programming, deep learning compiler architectures, PyTorch/TensorFlow optimization, and quantum computing simulation environments. Key areas include parallelization techniques and performance enhancement for machine learning frameworks. Publications: His research output demonstrates consistent focus on GPU efficiency, compiler-directed optimizations, and distributed computing, with recent emphasis on deep learning acceleration and error resilience in heterogeneous architectures. Awards & Honors: IEEE Fellow IBM Graduate Fellowship Korea Foundation for Advanced Studies Graduate Fellowship Leadership: Directs the Multicore Computing Research Laboratory and Center for Manycore Programming, focusing on next-generation parallel computing architectures.