جست‌وجوی استاد و پژوهشگر

Liqiang Zhangمشاهده پروفایل

استاد

• Wireless Networks
• Mobile Computing
• Resource Allocation
• +۳ مورد دیگر

Dr. Liqiang Zhang is a Professor at the Department of Computer and Information Sciences, Indiana University South Bend. He holds a Ph.D. in Computer Science from Wayne State University (2005). His research focuses on wireless networks, mobile computing, resource allocation, IoT, cognitive radio networks, and network security. His work is supported by IU and the National Science Foundation. He has organized multiple conferences including ICCCN 2013 (Track Chair), GLOBECOM 2010 (Publicity Co-Chair), and founded/co-chaired WiMAN workshops. He serves as a Guest Editor for journals like ACM Transactions on Autonomous and Adaptive Systems and Elsevier's Computer Communications. He reviews for top journals including IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. Dr. Zhang teaches courses such as Computer Structures, Mobile App Development, and Network Security. Recent publications (2019-2009) include advancements in network scheduling, cognitive radio protocols, radar positioning, and digital design education. His research spans both theoretical and applied aspects of networking and distributed systems.

Diomidis Spinellisمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Computer Science
• +۵ مورد دیگر

Professor Diomidis Spinellis is a renowned academic in Software Technology at Athens University of Economics and Business (AUEB). He specializes in software engineering practices, code quality, AI ethics, and system architecture. His work bridges theoretical advancements with practical applications in industry, emphasizing reproducibility and empirical methods. Recipient of the IEEE Computer Society's prestigious 'Distinguished Contributor Recognition,' Spinellis is the sole Greek scientist to achieve this honor. His research spans software evolution, security, and open-source ecosystems, with a focus on methodologies like refactoring, static analysis, and debugging strategies. Key research interests include AI-generated content detection, modular data analytics, and incident management systems. His studies often leverage large-scale datasets (e.g., Unix evolution, Linux supercomputing analysis) to uncover patterns in software behavior and development practices. Publications frequently address emerging technologies' societal impacts, such as energy-efficient computing and ethical AI deployment. He advocates for reproducible research through tools like the Alexandria3k framework and contributes to open-source initiatives.

Vladimir M. Ciricمشاهده پروفایل

استاد

• Computer Networks
• Network Security
• Parallel Computing
• +۳ مورد دیگر

Dr. Vladimir M. Ciric is a Full Professor at the Faculty of Electronics, University of Niš, leading the Department of Computer Science. He directs the Laboratory for Advanced Security Systems and specializes in network security, distributed systems, and high-performance computing. His research spans intrusion detection systems, cloud computing cost modeling, and parallel algorithm design. Notable contributions include Hadoop-based network intrusion detection techniques and tiered programming pedagogy. He received the Cisco Excellence Award (2015) for network technology education. Dr. Ciric teaches courses on network design, cloud computing, and security while maintaining research collaborations in cybersecurity and distributed architectures. His recent work focuses on extreme class imbalance in intrusion datasets and sparse matrix optimizations.

Kazem Cheshmiمشاهده پروفایل

استادیار

• High-performance computing
• Compilers
• Scientific computing
• +۳ مورد دیگر

Kazem Cheshmi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on compiler optimization techniques for accelerating scientific computing and machine learning applications on parallel architectures. He leads the SwiftWare Lab and teaches courses such as High-Performance Programming (COMPENG 4SP4/ECE 6SP4) and Special Topics in Computation (ECE 718). Education: B.Eng. (Ferdowsi University of Mashhad), M.A.Sc. (University of Tehran), Ph.D. (University of Toronto). He has held research positions at Microsoft Research, Adobe Research, Concordia University, and Rutgers University. Research Interests: High-performance computing, compiler design, sparse matrix computations, and their applications in machine learning and scientific computing. His work emphasizes optimizing sparse codes for parallel architectures and developing efficient QP solvers like NASOQ. Key Contributions: Developed Sympiler (a domain-specific compiler for sparse matrix codes) and NASOQ (a scalable QP solver). His awards include the ACM-IEEE CS George Michael Memorial HPC Fellowship (2020) and recognition for contributions to compiler-driven sparse computation optimization. Teaching and Service: Organizes SONAD’25, serves on program committees for PPoPP, Supercomputing, and IPDPS. Supervises students in compiler design, parallel programming, and high-performance computing. Labs/Teams: Leads the SwiftWare Lab focusing on compiler optimization and high-performance systems. Collaborates on open-source projects like Sympiler and NASOQ.

Sam Amiriمشاهده پروفایل

مدرس

• Embedded Systems
• Hardware Design
• Signal Processing
• +۳ مورد دیگر

Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.

Steven M. Druckerمشاهده پروفایل

استاد مدعو

• Data Visualization
• Human-Computer Interaction
• Machine Learning
• +۵ مورد دیگر

Steven M. Drucker is a Partner Research Manager at Microsoft Research's Visualization and Interactive Data Analysis (VIDA) group and an adjunct professor at the University of Washington's Computer Science and Engineering Department. With a 30+ year career, he has published over 100 papers and holds 130+ patents, focusing on data visualization, human-computer interaction, and machine learning. His work emphasizes tools like SandDance and Thumbtack to democratize data analysis and storytelling. He earned his Ph.D. from MIT Media Lab (1994), master's from MIT's AI Lab, and a neuroscience degree from Brown University. Affiliations: Microsoft Research, University of Washington Educations: Ph.D. MIT Media Lab (1994), M.S. MIT AI Lab, B.S. Brown University (Neurosciences) Research interests include data visualization techniques, interactive analytics, and bridging the gap between data and human understanding. His work spans visual storytelling, exploratory search, and immersive analytics. Notable contributions include foundational papers on unit visualizations, interactive machine learning tools, and visualization frameworks like Atom and Refinery . Key articles highlight advancements in AI-driven visualization (e.g., Data Formulator 2 ) and model interpretability ( What Did My AI Learn? ). He actively chairs conferences like SIGCHI and contributes to open-source tools like SandDance and MorphCharts.

• Mathematical Physics
• Differential Geometry
• Topology
• +۴ مورد دیگر

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.

Saad Mubeenمشاهده پروفایل

استاد

• Embedded Systems
• Real-Time Communication
• Predictable Software Development
• +۵ مورد دیگر

Saad Mubeen is a Full Professor of Computer Science at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Networked and Embedded Systems. He holds a Master's in Electrical Engineering (Embedded Systems) and a PhD in Computer Science and Engineering from Mälardalen University (2014), with a Docent title (2018) focused on vehicular embedded systems. His research emphasizes predictable embedded systems, timing analysis for real-time communication, and component-based software design. Key areas include model-driven development for automotive systems, integration of TSN/5G networks, and fault-tolerant industrial architectures. He has led projects on end-to-end timing analysis in distributed systems, ROS 2 verification, and cognitive edge-cloud scheduling. Publications span 2021–2025, focusing on real-time systems, network protocols (TSN, AVB, 5G), and industrial automation. Notable work includes frameworks for TSN configuration, fault diagnosis tools using NETCONF, and scheduling algorithms for heterogeneous edge-cloud environments. His contributions address critical challenges in timing predictability, security, and resource optimization for cyber-physical systems. Education contributions include problem-based learning modules for vehicular software engineering. He is actively involved in bridging academia and industry through collaborative research on next-generation automotive and industrial systems.

• Optimizing Compilers
• Runtime Systems
• Embedded Systems
• +۵ مورد دیگر

Summary Role & Affiliation: Mahmut Taylan Kandemir is a Professor in the Department of Computer Science and Engineering at Pennsylvania State University. He is a member of the Microsystems Design Lab and has held roles such as Graduate Program Coordinator. His research focuses on optimizing compilers, runtime systems, embedded systems, and storage technologies. Education: B.S. and M.S. from Istanbul Technical University (Computer Engineering), Ph.D. from Syracuse University (Computer Science). Research Interests: Kandemir’s work spans compiler optimization, energy-efficient architectures, non-volatile memory systems, and cloud storage. His research has been funded by NSF, DARPA, and industry partners like Intel and Microsoft. Articles & Contributions: Over 150 journal papers and 650+ conference publications, covering topics like compiler-driven storage efficiency, 3D NAND SSD optimization, and approximate computing frameworks. Awards: NSF Career Award, Penn State Premier Research Award, IEEE Fellow, and multiple best paper awards. His contributions include compiler tools and storage systems for high-performance computing. Advising & Grants: Advised 32 Ph.D. and 20 M.S. students. Active in program committees for conferences like MICRO, ISCA, and HPCA. Current grants focus on cloud resource management and emerging memory technologies. Labs & Collaborations: Leads projects in collaboration with Argonne National Lab, NVIDIA, and Intel. His work bridges academia and industry, addressing challenges in multicore and embedded systems.

Phillip Raffeckمشاهده پروفایل

پژوهشگر

• Real-Time Systems
• Embedded Systems
• Energy-Aware Computing
• +۷ مورد دیگر

Phillip Raffeck is a researcher at the Department of Computer Science (INF) within the Chair of Computer Science 4 (System Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on real-time systems, embedded systems, and energy-aware computing, with a particular emphasis on worst-case execution time (WCET) and energy consumption (WCEC) analysis. He has contributed to projects like the Invasive Run-Time Support System (iRTSS) and tools for energy-neutral system design. Department: Department of Computer Science (INF) University: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Position: Researcher His research spans real-time systems , multi-core optimization , and power-aware computing . Key contributions include migrative synchronization protocols , energy-neutral operating systems , and code metrics for timing analysis . Recent work explores carbon-aware co-design and predictable migration in embedded environments. Publications highlight collaborations with teams across Germany and international symposia like RTSS, WCET, and EMSOFT. His article trends reflect advancements in intermittent execution models , transactional networking , and static analysis toolchains . He serves as a secondary reviewer for conferences including RTAS and ISORC. Phillip supervises graduate theses on topics such as DMA-based OS offloading , dynamic migration , and interrupt latency analysis . He contributes to teaching courses like Betriebsystemtechnik and Systemnahe Programmierung in C at FAU.

Michael Wimmerمشاهده پروفایل

استاد

• Computer Graphics
• Real-Time Rendering
• Point Clouds
• +۴ مورد دیگر

Univ.Prof. Michael Wimmer is a Professor in the Department of Computer Graphics and Visualization at TU Wien's Faculty of Informatics. His research focuses on real-time rendering, point cloud processing, GPU computing, and computational design. He leads projects involving architectural visualization, thermal simulation, and integrative design frameworks combining 4D sketching with material modeling. His work bridges computer graphics with applications in architecture and engineering. ORCID: 0000-0002-9370-2663 Research Group: Network Lab Recent research emphasizes GPU-accelerated algorithms (e.g., LOD generation for 2 billion points), real-time rendering techniques, and deep learning approaches for surface reconstruction. Collaborations span thermal simulation with civil engineers and 4D design tools for architects. Notable contributions include: Developing PPSurf for detailed surface reconstruction using point convolutions Advancing Vulkan-based rendering pipelines in academic settings Integrating light polarization for HDR imaging His lab explores applications in architectural design metaphors, computational material assessment via GeoRadar, and thermal simulation using precomputed radiative transport. Students under his supervision focus on GPU optimization, 3D reconstruction, and VR ergonomics.

Mian M. Hamayunمشاهده پروفایل

دانشیار

• Computer Science
• Transaction Level Modeling (TLM)
• MPSoC Simulation
• +۱۵ مورد دیگر

Mian M. Hamayun is an Associate Professor of Computer Science and Head of Subject Group at the University of Birmingham Dubai campus, part of the College of Engineering and Physical Sciences. He holds a PhD in System Modelling, Simulation & Virtualization from the University of Grenoble Alpes, France, and has academic qualifications from Joseph Fourier University and COMSATS Institute of Information Technology, Pakistan. PhD in System Modelling, Simulation & Virtualization, University of Grenoble Alpes, 2013 MS in Informatics (Parallel & Distributed Systems), Joseph Fourier University, 2009 MSE in Software Engineering (Gold Medalist), COMSATS, 2005 PGCHE, University of Birmingham, 2021 FHEA and SFHEA, University of Birmingham (2021, 2024) His research spans Transaction Level Modeling (TLM) , MPSoC simulation , cloud computing , virtualization , Internet of Things/Vehicles , edge AI , and TinyML . He focuses on performance estimation, binary translation, and system-level simulation for embedded and distributed systems. The recent publications reflect a strong trend in IoT and vehicular networks , secure telemedicine , cloud orchestration , and embedded system simulation . His work integrates virtualization with security, real-time constraints, and machine learning on edge devices. Senior Fellow of the Higher Education Academy (SFHEA), 2024 Fellow of the Higher Education Academy (FHEA), 2021 Dr. Hamayun has supervised multiple research students and contributed to projects involving fatigue detection, person re-identification, retinal image analysis, and secure surgery frameworks. He has taught core computer science courses including Operating Systems, Networks, Data Structures, Algorithms, and Functional Programming at both undergraduate and postgraduate levels. His prior roles include Assistant Professor at NUST SEECS, R&D Engineer in France, and Lecturer at IUT1. He leads research in virtual prototyping and edge intelligence at the Dubai campus, contributing to smart city and cognitive systems initiatives. His lab work involves hybrid simulation environments, secure automotive platforms, and distributed IoT systems.

Furat Al-Obaidyمشاهده پروفایل

مدرس

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +۷ مورد دیگر

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

Mitra Nasriمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۴ مورد دیگر

Mitra Nasri is an Assistant Professor at the Eindhoven University of Technology, affiliated with the College of Engineering's Department of Electrical Engineering. She contributes to the High Tech Systems Center and EAISI Foundational, focusing on interconnected resource-aware intelligent systems. Research Focus: Real-Time Systems, Scheduling Algorithms, Embedded Systems, Fault-Tolerant Computing, and Cyber-Physical Systems. Key Contributions: Development of scheduling frameworks for multi-rate task chains, response-time analysis techniques, and containerization strategies for real-time distributed applications. Her recent work includes advancements in weakly-hard timing constraints, parallel global scheduling, and cloud integration for embedded systems. She actively collaborates on projects like SAM-FMS and COMP4DRONES. Scientific Awards: Best Paper Award - RTAS 2022 Best Paper Award - RTNS 2016 Outstanding Paper Awards at RTAS 2017, 2022 and RTSS 2020 She teaches courses in Real-Time Systems, Operating Systems, and Automotive Software, and participates in organizing conferences like Embedded Systems Week and CompSys.

• High performance computing
• Computational Science and Engineering
• Scientific computing
• +۷ مورد دیگر

Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.