جستجوی استاد راهنمای دانشگاهی

• Embedded Systems
• System-on-Chip (SoC) Design
• Computer Architecture
• +۵ مورد دیگر

Professor Klaus McDonald-Maier is a full Professor in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex , where he leads the Embedded and Intelligent Systems (EIS) Research Laboratory and heads the Intelligent Embedded Systems and Environments Research Group . He is also Director of Impact , Visiting Professor at the University of Kent, and Visiting Research Affiliate at NASA Jet Propulsion Laboratory, California Institute of Technology. Education PhD in High-Performance Parallel Neural Network Architectures, Friedrich-Schiller-University Jena (Germany, 1999) Electronic Engineering studies, University of Ulm (Germany) Electronic Engineering studies, Cardiff University (Wales) Electronic Engineering studies, École Supérieur de Chimie Physique Électronique de Lyon (CPE-Lyon) (France) Research Interests Professor McDonald-Maier’s research integrates embedded systems , System-on-Chip (SoC) architectures , and AI-driven robotics . He pioneers visual place recognition techniques that remain robust under severe appearance and viewpoint changes, develops cybersecurity frameworks based on ICMetrics for autonomous vehicles and IoT, and designs approximate real-time computing solutions for energy-constrained multicore and FPGA platforms. His work on radiation-tolerant systems supports space and nuclear applications, while his bio-inspired algorithms enable lightweight, neuromorphic perception on resource-limited robots. Publication Trends Between 2022 and 2025 his output converges on FPGA-accelerated AI , secure edge intelligence , visual navigation for autonomous systems , and healthcare analytics . He repeatedly couples rigorous algorithmic innovation with practical hardware deployment, yielding energy-efficient, real-time systems validated in domains ranging from autonomous driving to post-stroke rehabilitation. Scientific Awards & Recognition Best Paper Award – IEEE Transactions on Sustainable Computing (2024) Best Paper Award – IEEE/ACM DATE (2024) Best Paper Award – IEEE Systems Journal (2022) Best Paper Award – IEEE Sensors Journal (2021) Best Paper Award – IEEE Access (2020) Research Grants & Industrial Collaboration He has secured major funding from EPSRC , EU Horizon 2020 , Innovate UK , and industry partners. Current projects span trustworthy autonomy, radiation-hardened edge AI, and AI-enhanced rehabilitation technologies. He is Chief Scientist of UltraSoC Technologies Ltd and CEO of Metrarc Ltd , commercialising University research in semiconductor debug and cybersecurity respectively. Laboratory & Team Leadership As Director of the Embedded and Intelligent Systems Laboratory (EIS Lab) , he oversees a multidisciplinary team of researchers and PhD students, providing state-of-the-art FPGA, robotics, and embedded-systems facilities. The lab collaborates closely with NASA JPL, UK Atomic Energy Authority, and leading semiconductor firms to translate fundamental research into high-impact industrial solutions.

• Multicore Computing
• Virtualization
• Cloud Computing
• +۴ مورد دیگر

Xiaoning Ding is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). His research focuses on virtualization, multicore computing, cloud infrastructure optimization, and mobile systems. He leads projects addressing challenges in nested virtualization, memory management, and cache conflicts in distributed and cloud environments. Key research interests include optimizing task scheduling in cloud VMs, reducing TLB misses through huge page strategies, and mitigating interference in multi-tenant GPU clouds. His work on page placement mechanisms and dynamic page coalescing aims to enhance virtualized cloud performance. Ding has received federal funding, including an NSF grant for virtualization research in heterogeneous memory hierarchies (2016–2019). His research outputs span over 74 publications, with notable contributions in EuroSys, IEEE Transactions, and conferences like PACT. Media coverage highlights his studies on cloud computing and collaborative mobile systems, such as parking assignment algorithms. Beyond technical contributions, Ding advises students in interdisciplinary projects, exemplified by collaborations with Applied Math majors on cloud computing challenges.

James H. Andersonمشاهده پروفایل

استاد

• Real-time systems
• Distributed computing
• Concurrent algorithms
• +۳ مورد دیگر

James H. Anderson is the Kenan Distinguished Professor and Department Chair in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he has been a faculty member since 1993. A leading researcher in real-time systems and distributed computing, he previously served at the University of Maryland (1990-1993). His educational background includes: B.S. in Computer Science from Michigan State University (1982) M.S. in Computer Science from Purdue University (1983) Ph.D. in Computer Sciences from the University of Texas at Austin (1990) Anderson's research focuses on real-time systems, distributed and concurrent algorithms, multicore computing, and operating systems. His work addresses fundamental challenges in scheduling, resource management, and reliability in time-critical environments, with recent emphasis on GPU acceleration for AI workloads and heterogeneous platforms. He investigates novel approaches to budget enforcement, processing graph scheduling, and timing predictability in complex systems. Analysis of his recent publications reveals a strong trend toward applying real-time scheduling principles to GPU and heterogeneous computing architectures. Key research thrusts include enabling predictable AI acceleration, managing timing uncertainties in autonomous systems, and developing composable resource partitioning techniques for safety-critical applications. His distinguished honors include: U.S. Army Research Office Young Investigator Award (1995) Alfred P. Sloan Research Fellowship (1996) Seven Computer Science Student Association Teaching Awards (1995-2019) Fellowships from IEEE (2012), ACM (2013), AAAS (2020), and AAIA (2022) IEEE TCRTS Outstanding Technical Achievement and Leadership Award (2018) Anderson actively mentors through the TOPICS Club, a reading group for undergraduate students that he co-leads with Cynthia Sturton and Danielle Szafir. His professional leadership includes chairing IEEE TCRTS (2016-2017) and ACM SIGBED (2019-2021), plus service as program/general chair for major conferences including RTSS, PODC, ECRTS, and RTAS. He directs ongoing research initiatives in real-time systems through collaborative projects and the TOPICS Club, which provides undergraduates with hands-on research experience in cutting-edge computer science topics.

• Computer Architecture
• Reconfigurable Computing
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Ioannis Sourdis is a Full Professor at the Department of Computer Engineering, Chalmers University of Technology, Sweden. His research focuses on computer architecture, reconfigurable computing, network-on-chip (NoC) design, memory systems, and fault-tolerant embedded systems, with applications in biomedical informatics and hardware security. Current projects include EUMMSS (Efficient Uncore Mechanisms for Multicore Space Systems, funded by the Swedish National Space Board) and eProcessor (European Processor Ecosystem, funded by the European Commission). Past initiatives include the DeSyRe project (on-demand system reliability), ECOSCALE (exascale reconfigurable computing), and SHARCS (secure hardware-software architectures). His work spans NoC router design (e.g., FastTrackNoC, DDRNoC), memory compression (MemSZ, L2C), and biomedical security applications (heartbeat-based protocols). He has published extensively in venues like DATE, ICS, PACT, and IEEE Transactions on Networking. Key research areas: Chiplet-based systems , hybrid memory architectures , FPGA acceleration , and real-time stream aggregation .

Rohan Tabishمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Multicore Processors
• +۵ مورد دیگر

Rohan Tabish is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), specializing in real-time systems, embedded systems, and cybersecurity. His work focuses on developing predictable and secure software frameworks for multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science and Engineering Master's in Computer Science and Engineering B.Sc. in Electrical Engineering with Telecommunications specialization Research Interests: Real-Time Task Scheduling Fault Tolerance in Embedded Systems Inter-Core Communication Frameworks Memory Bandwidth Management Cyber-Physical Systems Scratchpad-Centric Operating Systems Awards: Outstanding Paper & Best Paper Award (RTSS 2020) Outstanding Paper & Best Student Paper Award (RTSS 2020) Best Presentation Award (RTAS 2016) Nominated for Best Paper Award (ECRTS 2019) Teaching: CS 431: Embedded Systems (Instructor, 2015-2018) CS 424: Real-Time Systems (Instructor, 2019) CS 438: Communication Networks (TA, 2019) Labs/Teams: Active member of the Real-Time Systems Lab (RTSL) at UIUC, focusing on safety-critical embedded systems and real-time software frameworks.

• Embedded Systems
• Compilers
• Hardware-Software Co-Design
• +۵ مورد دیگر

Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.

Cristiana Bolchiniمشاهده پروفایل

استاد

• Dependability
• Fault Tolerance
• Reliability Analysis
• +۸ مورد دیگر

Cristiana Bolchini is a Professor at the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. She holds a PhD in Automation and Computer Science Engineering (1997) and a Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Her research focuses on dependable systems, fault tolerance, and embedded systems design, with recent work on ICT solutions for smart buildings and energy efficiency. She coordinates projects such as the FP7 SAVE initiative and serves on technical committees for conferences like DATE and DAC. Education: PhD in Automation & Computer Science (1997), Laurea in Electronic Engineering (1993), both from Politecnico di Milano. Research interests span dependability (fault modeling, diagnosis), heterogeneous architectures, and sustainable smart environments. She collaborates with Prof. Giuliana Iannaccone on foresight for sustainable built environments and has led EU-funded projects like the SAVE initiative. Publications include over 150 refereed papers on dependability and context-awareness. She holds editorial roles for journals such as IEEE Transactions on Computer-Aided Design and ACM Transactions on Embedded Computing Systems. Awards include IEEE Senior Member status and two Cisco University Research Program Fund gifts (2012, 2014). Academic roles include Rector’s delegate for Southeast Asia relations and leadership of Technology Foresight workgroups. She teaches courses on computer science fundamentals and dependable systems, emphasizing problem-solving and programming in Python and C.

Sheheeda Mariam Manakkaduمشاهده پروفایل

دانشیار

• Robotics
• Data Analytics
• Machine Learning
• +۴ مورد دیگر

Sheheeda Mariam Manakkadu is an Associate Professor in the Department of Computer Science at Southern Illinois University Carbondale. She teaches graduate courses in Data Structures, Object-Oriented Programming, Data Mining, Text Mining, and Cloud Architecture, along with undergraduate courses in Operating Systems and Data Analytics. Ph.D., Computer Engineering M.E., Biomedical Engineering Her research spans robotics, data analytics, and parallel computing. Key areas include adaptive control of robotic manipulators, big data processing via MapReduce, IoT resource allocation, and computational bioinformatics for protein networks. Recent publications focus on neuro-sliding mode control, cloud architecture, and scalable recommender systems. She actively participates in academic service as a committee member for graduate courses and the IEEE Erie Section. Her work integrates machine learning, optimization algorithms, and distributed systems across diverse domains.

Jia Raoمشاهده پروفایل

دانشیار

• Operating Systems
• Distributed Systems
• Parallel Computing
• +۱۳ مورد دیگر

Dr. Jia Rao is an Associate Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, College of Engineering. He previously served as an Assistant Professor at the University of Colorado, Colorado Springs from 2012 to 2016. His research spans operating systems, distributed and parallel computing, cloud computing, virtualization, and machine learning. Education: Ph.D., Computer Engineering, Wayne State University, 2011 M.S., Computer Science, Wuhan University, 2006 B.S., Computer Science, Wuhan University, 2004 Dr. Rao's research focuses on building adaptive, scalable, and efficient computer systems for cloud and data center environments. His interests include resource management, performance modeling, adaptive scheduling, and quality-of-service (QoS) guarantees in virtualized and containerized systems. He combines machine learning and feedback control techniques with low-level system design to improve efficiency, fairness, and predictability in heterogeneous and multi-tenant environments. An analysis of his recent publications reveals a strong trend toward memory and resource management innovations in cloud-native systems. His work explores tiered memory architectures, secure container deployment, preemptive multitasking for deep learning, and efficient packet processing in container networks. These efforts reflect a consistent focus on optimizing system-level performance, security, and scalability in modern data centers. Scientific Awards: NSF CAREER Award (2019) Best Paper Award, APSys (2016) Best Paper Award, ICAC (2013) Best Paper Nomination, HPCA (2013) Best Paper Nomination, HPDC (2013) Best Paper Award, Middleware (2021) Researcher of the Year, UCCS (2014) Dr. Rao actively advises students and serves on dissertation and thesis committees for numerous Ph.D. and Master’s candidates. He leads federally funded research projects supported by the National Science Foundation, including a major CAREER grant on virtualized architectures and collaborative big data initiatives. His research has been sponsored by NSF, IEEE, and Intel Corporation, reflecting strong industry and academic collaboration. He leads and contributes to major research labs and teams focused on cloud systems, operating systems, and performance optimization. His team has produced high-impact work in top-tier venues such as OSDI, SOSP, ATC, EuroSys, and ICDCS. Current and future work includes next-generation memory architectures using CXL, intelligent resource provisioning, and resilient container networking.

• Computer Architecture
• Emerging Non-Volatile Memory Management
• Energy and Performance optimizations in Network-on-Chip
• +۴ مورد دیگر

Dr. Sukarn Agarwal is an Assistant Professor in the EECS Department at IISER Bhopal. His primary research focuses on modern computer architecture, including emerging non-volatile memory management, energy-efficient optimizations in network-on-chip, thermal-aware chip management, and heterogeneous system design. He utilizes tools such as GEM-5, CACTI, NVSIM, and McPAT for research. Dr. Agarwal actively seeks motivated PhD, M.Tech, and long-term internship candidates aligned with his research areas. Research Interests : Non-volatile memory hierarchies, energy-performance trade-offs, thermal-aware computing, and hybrid memory systems. Key Tools : GEM-5, CACTI, NVSIM, McPAT, Hotspot. His recent work includes publications on approximate computing in MLC-MRAM-based systems, thermal-aware frameworks for ReRAM, and energy-efficient cache architectures using STT-RAM. He emphasizes interdisciplinary approaches to address challenges in modern computing systems.

Juan Manuel Cebrian Gonzalezمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Power Management
• +۴ مورد دیگر

Juan Manuel Cebrian Gonzalez is an Assistant Professor at the Department of Computer Engineering and Technology, Faculty of Informatics, University of Murcia. His work focuses on computer architecture, parallel systems, and energy-efficient computing. Doctorate: University of Murcia (2011), thesis on fine-grain power and thermal management in multicore processors. Research interests: Designing architectural mechanisms for optimizing power consumption and thermal management in multicore systems, cache coherence in parallel architectures, and vectorization techniques for high-performance computing. His work also explores heterogeneous architectures, fault tolerance, and efficient memory systems. Recent article trends: Focus on cache management, speculative execution, lock-free constructs, and performance-energy trade-offs in edge and heterogeneous computing. Key methodologies include gem5 simulation, Arm SVE, and AVX-512 vectorization. Collaboration: Supervised by Dr. Juan Luis Aragón Alcaraz and Dr. Stefanos Kaxiras. Active in the Computer Architecture and Parallel Systems research group.

Lesley Shannonمشاهده پروفایل

دانشیار

• Computer Engineering
• Reconfigurable Computing
• FPGA CAD Tools
• +۶ مورد دیگر

Lesley Shannon is an Associate Professor in the School of Engineering Science at Simon Fraser University (SFU), specializing in computing system design and reconfigurable computing. She leads the Reconfigurable Computing Lab (RCL), focusing on FPGA-based architectures, heterogeneous multicore systems, and CAD tools. Her academic journey includes a B.Sc. from the University of New Brunswick (1999), M.A.Sc. and Ph.D. from the University of Toronto (2001 and 2006). Her research spans FPGA CAD algorithms, Networks-on-Chip (NoCs), dynamic partial reconfiguration (DPR), and application-specific architectures. Key projects include FUSE (OS abstraction for hardware accelerators), PolyBlaze (multicore system emulation), and uROAMs (microfluidic reconfigurable devices). She has secured funding from NSERC, Xilinx, and Altera, among others. Education: B.Sc., Electrical Engineering (Computer Option), University of New Brunswick, 1999 M.A.Sc., Applied Science, University of Toronto, 2001 Ph.D., Applied Science, University of Toronto, 2006 Roles: NSERC Chair for Women in Science and Engineering (BC/Yukon) Faculty Advisor, SFU WEST (Women in Engineering, Science, and Technology) Her lab employs 5 current students and has graduated 7, including work on FPGA CAD, microfluidics, and embedded systems. Courses taught include Advanced Digital Systems Design and Real-Time/Embedded Systems. Her research emphasizes reducing design time through on-chip profiling and CAD tools, with applications in biomedical imaging and quantum computing. Awards & Grants: NSERC Discovery Grant NSERC Strategic Grant (Lead PI, 2009; Co-PI, 2010) SFU Startup Grant and President’s Research Grant Lab & Collaborations: The RCL develops silicon and non-silicon architectures, including quantum computing and microfluidic systems. Partnerships include industry collaborations and open-source tools like Odin II synthesis framework.

Greg Stittمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Computing
• +۵ مورد دیگر

Greg Stitt is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, affiliated with the College of Engineering. His research focuses on reconfigurable computing, FPGA acceleration, embedded systems, and compiler design. He has received notable awards including the NSF CAREER Award (2012-2017) and the Undergraduate Teacher of the Year Award (2014). His work emphasizes elastic computing frameworks, intermediate fabrics for FPGA virtualization, and warp processors for dynamic hardware/software partitioning. Education: PhD, Computer Science, University of California-Riverside, 2007 BS, Computer Science, University of California-Riverside, 2000 Research Interests: Reconfigurable computing, FPGAs, GPUs, and their applications in high-performance computing Compiler optimization and synthesis techniques for embedded systems Elastic computing frameworks for heterogeneous systems Approximate computing and energy-efficient architectures Grants & Awards: National Science Foundation (NSF) grants for elastic computing (CNS-0914474) and intermediate fabrics (CNS-1149285) Recognition for contributions to FPGA-based scientific computing tools Teaching: Current courses include Reconfigurable Computing 2 and Digital Design Past course offerings span embedded systems, compiler design, and hardware architecture Labs & Teams: Active research in FPGA acceleration, novel architectures, and security for reconfigurable systems Contributions to the Novo-G scalable reconfigurable supercomputing project

Ashish Venkatمشاهده پروفایل

دانشیار

• Computer Architecture
• Compilers
• Computer Security
• +۱۲ مورد دیگر

Ashish Venkat is an Associate Professor in the Department of Computer Science at the University of Virginia, part of the School of Engineering and Applied Science. He holds a Ph.D. from UC San Diego and has established himself as a leading researcher in computer architecture, compilers, and computer security. Research Interests: His research focuses on cross-disciplinary hardware and software techniques to build secure, high-performance computing systems. He investigates robust exploit mitigations that maintain energy efficiency and programmability, with a particular emphasis on speculative execution, memory safety, hardware security, and privacy-preserving computing. He also explores the application of machine learning to detect security threats and model execution behavior. Publication Trends: His recent publications (2020–2025) demonstrate a strong focus on hardware-based security, particularly microarchitectural vulnerabilities (e.g., micro-op cache attacks), memory safety via microcode capabilities, and secure accelerators for bioinformatics. There is a consistent trend of publishing in top-tier venues like ISCA, MICRO, IEEE S&P, and USENIX Security, often featuring novel hardware/software co-design solutions. Scientific Awards: NSF CAREER Award (2023) NSF CRII Award (2018) IEEE Micro Top Pick (2019) IEEE Design & Test Top Pick (2020, 2021) HPCA Best Paper Runner-Up (2019) DATE Best Paper Nominee (2023) UVA Research Achievement Award (2023) ISCA Prolific Author of the Decade (2013–2022) Advising and Grants: He actively mentors graduate and undergraduate students, many of whom have pursued advanced degrees or joined leading tech companies. He has secured significant funding as PI or co-PI from NSF, DARPA, SRC, and Intel, including a $4.9M DARPA HERCULES grant and an NSF CAREER award. His projects focus on holistic security solutions, speculative optimization, and privacy-preserving machine learning frameworks. Labs and Teams: He leads a research group focused on secure and efficient computing systems, collaborating with researchers at institutions like UC San Diego, UC Riverside, and UC Irvine, as well as industry partners including Intel and IBM.

Alberto Rosمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• Multicore Processors
• +۵ مورد دیگر

Alberto Ros is a Full Professor at the University of Murcia , Spain, in the Computer Engineering Department (DITEC) . His work focuses on cache coherence , memory hierarchy designs , memory consistency , and processor microarchitecture , with over 100 peer-reviewed publications. Dr. Ros earned his MS (2004) and PhD (2009) in Computer Science from the University of Murcia. He interned at the School of Informatics, University of Edinburgh , and held postdoctoral positions at the Technical University of Valencia and Uppsala University . He is an IEEE Senior Member . Research interests include optimizing hardware for multicore systems. His work spans cache coherence protocols, transactional memory, speculative execution, and data/instruction prefetching techniques. He led the ERC Consolidator Grant (2018) and ERC Proof of Concept Grant (2023) to improve multicore architecture performance. Recent publications emphasize hardware transactional memory efficiency, speculative execution, and secure cache systems. Notable works include cache locking, memory dependency prediction, and fine-grain coherence protocols. Scientific awards : Inducted into the MICRO Hall of Fame ISCA Hall of Fame 27 HiPEAC paper awards (MICRO, ISCA, HPCA, ASPLOS) Winner, ML-based Data Prefetching Competition Winner, 1st Instruction Prefetching Championship IEEE MICRO TopPicks for ISCA'17, MICRO'21 (honorable), MICRO'16 (honorable) Best paper awards at HiPC'16, FORTE'16 Honorable mention at HPCA'24 Nomination at ISCA'22 Grants as Principal Investigator include ERC Proof of Concept (2023) ERC Consolidator (2018) Europe Excellence (2018) Seneca Foundation, Young Leaders in Research (2014) . Dr. Ros is affiliated with the Computer Architecture and Parallel Systems Group (CAPS) at the University of Murcia and previously with UPMARC at Uppsala University.