جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Parallel Computing
• Cache Coherence
• +۵ مورد دیگر

Manuel Eugenio Acacio Sanchez is a Professor in the Department of Computer Engineering and Technology at the University of Murcia's Faculty of Informatics. His research focuses on computer architecture, parallel systems, cache coherence, and hardware transactional memory. He earned his Ph.D. from the University of Murcia in 2003 with a thesis on directory-based coherence protocols for distributed-shared memory multiprocessors. Doctorate: Universidad de Murcia (2003) Academic Rank: Professor Research Interests include Hardware transactional memory Cache coherence protocols GPU and DNN accelerators Energy-efficient computing Parallel architectures Recent Article Trends emphasize cycle-level simulation tools (e.g., STONNE), hardware transactional memory optimizations, and neural network accelerator design. His work bridges microarchitectural improvements and application-specific efficiency in multicore systems. Labs & Teams : Affiliated with the Computer Architecture and Parallel Systems research group, previously part of the Architecture and Parallel Computing group.

Alberto Ros Bardisaمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Hardware Optimization
• +۶ مورد دیگر

Alberto Ros Bardisa is a Professor at the Department of Computer Engineering and Technology within the Faculty of Informatics at the University of Murcia. He holds a Doctorate from the same university, completing his thesis on Efficient and Scalable Cache Coherence for Many-Core Chip Multiprocessors in 2009. His research focuses on computer architecture , with particular emphasis on cache coherence protocols , hardware transactional memory , speculative execution , and many-core systems . He has contributed to optimizing memory subsystems, concurrency management, and scalability in parallel computing environments. His work often bridges hardware-software co-design to enhance performance and security in modern architectures. Dr. Ros Bardisa is affiliated with the Computer Architecture and Parallel Systems research group and previously contributed to the Architecture and Parallel Computing group. His publications (over 130+ listed) reflect a sustained focus on advancing cache efficiency, transactional memory systems, and speculative execution techniques. His current work explores innovative solutions for fine-grain coherence , secure prefetching , and atomic operation optimization , with recent contributions in 2025 addressing novel protocols like WoperTM and MASCOT.

• Reconfigurable Computing
• Parallel Computing
• FPGA Accelerators
• +۴ مورد دیگر

Prasanna Viktor is a Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California (USC), holding the Charles Lee Powell Chair in Engineering. He also serves as Director of the Center for Energy Informatics and has held leadership roles in interdisciplinary research centers such as the USC Infosys Center for Advanced Software Technologies. With a BE from Bangalore University, ME from Indian Institute of Science, and PhD from Pennsylvania State University, he is a globally recognized expert in reconfigurable computing, FPGA accelerators, and parallel computing. His research focuses on high-performance architectures for applications in networking, security, HPC, and machine learning. Over 25 years, he has secured research grants totaling over $50M, including $12.9M from 2016–2021. He has advised over 70 doctoral students and published over 600 papers, earning 22 best paper awards. His work has led to IP cores with improved throughput, latency, and energy efficiency. Prasanna is a Fellow of the IEEE, ACM, and AAAS, and an elected member of Academia Europaea. He has received prestigious awards such as the W. Wallace McDowell Award (2015) and the Distinguished Alumnus Award from IISc (2019). He currently serves as Editor-in-Chief of the Journal of Parallel and Distributed Computing. He leads interdisciplinary efforts in energy informatics and smart oilfield technologies, bridging computer science, engineering, and energy sectors. His contributions include foundational work in reconfigurable computing and FPGA-based accelerators for diverse applications.

Sayed Ahmad Salehiمشاهده پروفایل

استادیار

• VLSI Circuits
• Stochastic Computing
• DNA Computing
• +۷ مورد دیگر

Sayed Ahmad Salehi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering, where he directs the Computing with Unconventional Technologies (CUT) Lab. His research spans energy-efficient VLSI circuits for deep learning, stochastic computing, and DNA-based molecular programming. His research focuses on unconventional computing paradigms to address limitations in traditional electronics. Key areas include stochastic and approximate computing for energy-efficient deep learning hardware targeting IoT and edge devices, and biomolecular computing using DNA for environments incompatible with silicon. His lab develops tools like FUNSC (stochastic computing) and FUNDNA (DNA computing) that enable mathematical function computation through novel encoding schemes. Analysis of his recent publications reveals strong emphasis on in-memory computing architectures (particularly for DRAM and phase-change memory), stochastic-to-binary conversion techniques , and DNA circuit synthesis for mathematical operations. His work bridges theoretical computer science with practical hardware implementations for resource-constrained environments. Award highlights include: BIOMOD competition silver award (2023) Best paper nomination at Asilomar Conference (2022) ESWEEK Student Travel Grant (2019) He actively mentors PhD and undergraduate researchers, with recent graduates joining companies like Applied Materials. His lab secures significant NSF funding for interdisciplinary projects in stochastic computing and DNA-based systems. Current initiatives include CUT Lab's Kentucky iGEM team and collaborations on photonics-integrated computing. The CUT Lab operates at the intersection of computer architecture, molecular biology, and signal processing, with research impacting edge AI hardware, biocompatible computing, and next-generation memory systems.

• Computer Networks
• Distributed Systems
• Edge Computing
• +۴ مورد دیگر

Osama Abboud is a researcher affiliated with Technische Universität Darmstadt. He holds a PhD in Computer Science from the same institution (2012), focusing on quality adaptation in peer-to-peer video streaming using scalable video coding. His work spans computer networks, distributed systems, edge computing, and federated learning. Key contributions include innovations in P2P video streaming resilience, serverless federated learning optimization, and mission-critical AI systems. He has collaborated extensively with researchers like Ralf Steinmetz and Michael Gerndt, publishing in venues such as IEEE Trans. Ind. Informatics, NetSoft, and ICC. His research emphasizes practical system design for heterogeneous environments, energy efficiency, and real-time performance. Education: PhD in Computer Science (Darmstadt University of Technology, 2012). Research Interests: Peer-to-Peer Systems, Edge Computing Architecture, Federated Learning Optimization, AI-driven Network Adaptation, and Low-Power Hardware Integration. His work bridges theoretical foundations with practical implementations in distributed environments. Notable Projects: Development of Apodotiko (serverless federated learning framework), MARQ (mission-critical AI system), and contributions to edge computing architectures like Trabant and Host Bypassing. These projects aim to enhance scalability, reduce latency, and improve sustainability in distributed systems.

Vlad Popescuمشاهده پروفایل

دانشیار

• Telecommunications
• Cognitive Radio Systems
• Software Defined Radio
• +۷ مورد دیگر

Vlad Popescu is an Associate Professor at the Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Technical University of Brasov. His research focuses on telecommunications, IoT, cognitive radio systems, and multimedia applications. He has authored numerous publications in top journals and conferences, including work on 6G-enabled smart cities, edge-cloud orchestration, and QoE-driven smart home systems. His research explores cutting-edge topics such as immersive multi-sensory experiences, real-time object detection, and energy-efficient architectures for Android TV integration. Popescu has contributed to open-source satellite simulators and pioneered studies on blockchain-based TV advertising systems. His work bridges theoretical advancements with practical implementations in smart home environments, IoT gateways, and next-generation wireless networks. Award-winning research includes innovations in SDR-based satellite gateways and hardware-software partitioning for embedded systems. His collaborative projects address challenges in distributed video applications, D2D communications, and social IoT frameworks for smart tourism management.

Suchendra M. Bhandarkarمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

• High-Performance Computing (HPC)
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Murali Emani is a researcher affiliated with Argonne National Laboratory, IL, USA. His work focuses on High-Performance Computing (HPC), machine learning, and artificial intelligence acceleration. Emani holds a PhD in Computer Science from the University of Edinburgh (2015). He specializes in optimizing large-scale systems, including performance evaluation of AI accelerators, transformer models, and neural architecture search. His research bridges HPC infrastructure with AI applications, addressing challenges in resource allocation, inference efficiency, and cross-architecture benchmarking. Key contributions include frameworks for GPU memory optimization (XUnified) and holistic performance evaluation of large language models across diverse hardware (e.g., BaKlaVa, Centimani). He collaborates extensively with institutions like the University of Chicago, NVIDIA, and MLCommons on initiatives such as DeepSpeed4Science and GenSLMs for genome-scale language models. His work frequently appears in top venues like IPDPS, SC, and Euro-Par. Emani’s research emphasizes practical system-level innovations, with applications spanning bioinformatics (SARS-CoV-2 evolutionary analysis), protein design, and exascale computing. He advocates for FAIR principles in HPC data management and contributes to open benchmarks like MLPerf HPC.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۳ مورد دیگر

Fadi A. Aloul is an academic at the American University of Sharjah, UAE , with a focus on computer science and cybersecurity. His research spans Boolean satisfiability Machine learning applications IoT and mobile security Chaotic system modeling He has collaborated extensively on FPGA implementations, intrusion detection, and health monitoring systems. Research Trends : Recent work emphasizes machine learning for cybersecurity (e.g., Android malware detection, IoMT threat mitigation) and FPGA-based chaotic models for medical/disease simulation. Earlier studies highlight symmetry-breaking algorithms in satisfiability problems and mobile health applications. Collaborations : Co-authored with Imran A. Zualkernan Assim Sagahyroon Ahmed S. Elwakil Wassim El-Hajj across institutions in UAE, USA, and Egypt.

• Hardware Security
• Machine Learning for Security
• VLSI Design
• +۷ مورد دیگر

Sai Manoj Pudukotai Dinakarrao is a Professor in the Department of Computer Engineering at Rochester Institute of Technology's College of Engineering. He is an active researcher with numerous publications spanning hardware security, machine learning applications, VLSI design, and IoT security. His work focuses on developing innovative solutions for security challenges in hardware and embedded systems. Dr. Dinakarrao's research interests center around hardware security, machine learning for security applications, VLSI design methodologies, and IoT security frameworks. His work has pioneered approaches to hardware Trojan detection, processing-in-memory architectures, and energy-efficient security mechanisms for resource-constrained devices. He has made significant contributions to the understanding of hardware vulnerabilities and the development of robust countermeasures. His recent publications demonstrate a clear trend toward integrating machine learning techniques with hardware security solutions, particularly focusing on processing-in-memory architectures for efficient security implementations. His work spans both theoretical foundations and practical implementations, with applications in IoT security, malware detection, and hardware verification. The research shows increasing sophistication in addressing security challenges through novel hardware-software co-design approaches. Dr. Dinakarrao has mentored numerous graduate students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and hardware design topics. His collaborations span multiple institutions and research groups, demonstrating his integration within the broader academic community.

Ira Assentمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Clustering Algorithms
• +۵ مورد دیگر

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

• Machine Learning
• Optimization
• Computer Vision
• +۵ مورد دیگر

Tom Goldstein is the Volpi-Cupal Endowed Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. His research focuses on AI system development, optimization methods, and their applications in computer vision and signal processing. He emphasizes the intersection of theoretical foundations and practical hardware implementations. Education: PhD in Applied Mathematics from UCLA (2010), BA from Washington University (2006). Prior to UMD, he held postdoctoral positions at Stanford University and Rice University. Research interests include AI security/privacy, algorithmic bias, diffusion models, and large language model robustness. His work addresses challenges in model watermarking, adversarial attacks, and ethical AI deployment. Notable awards include the Sloan Research Fellowship (2017), DARPA Young Faculty Award, and JPMorgan Faculty Research Award. He directs the Maryland Center for Machine Learning and has advised over 20 graduate students in AI-related disciplines. Recent research trends emphasize multimodal systems, generative model analysis, and scalable training techniques. His work on diffusion models explores style similarity and content authenticity, while watermarking studies address anti-plagiarism and data provenance. Labs/Teams: Leads the Maryland Center for Machine Learning, collaborating with UMIACS and ECE departments. Active in open-source AI initiatives and supercomputing applications for large model training.

• Computer-Aided Design
• Energy-Efficient Systems
• VLSI Design
• +۳ مورد دیگر

Sarma Vrudhula is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Electrical Engineering from the University of Southern California (1985). Previously, he was a professor at the University of Arizona and served as the founding director of the NSF UA/ASU Center for Low Power Electronics. He is an IEEE Fellow recognized for contributions to low-power and energy-efficient digital circuit design. Educations: Ph.D. Electrical Engineering, University of Southern California (1985) M.S. Electrical Engineering, University of Southern California (1980) Bachelor's in Mathematics (Computer Science and Mathematical Statistics), University of Waterloo, Canada (1976) Research Interests: His work focuses on design automation, energy management in digital systems, statistical analysis of process variations, threshold logic circuits, and emerging technologies. He has pioneered methodologies for low-power VLSI design, thermal management of multi-core processors, and hardware implementations of threshold logic using spintronic devices. Publications: His recent research includes scalable energy-efficient architectures for AI, in-memory computing, and reconfigurable threshold logic gates. Key topics span energy efficiency in edge computing, neuromorphic systems, and sustainable VLSI design. Awards: IEEE Fellow (2005) Best Paper Award (2008) for macro cell characterization methodology Service & Grants: He led the NSF IUCRC Consortium for Embedded Systems and served on editorial boards. His grants include projects on threshold logic synthesis, energy-aware embedded systems, and hardware acceleration for neural networks. Courses Taught: Algorithmic Foundations of CAD for Digital Systems Computer Architecture Discrete Mathematics for Engineers

• Artificial Intelligence
• Neural Networks
• Robotics
• +۵ مورد دیگر

Abdullatif Baba is an Assistant Professor in the Department of Computer Engineering at Turkish Aeronautical Association University. His research focuses on interdisciplinary applications of artificial intelligence, robotics, and sustainable energy systems. He develops solutions for smart infrastructure, including AI-driven energy forecasting, autonomous drones for grid maintenance, and hyperspectral imaging techniques. His research interests span: AI/ML Systems : Neural networks, probabilistic forecasting, and FPGA-accelerated deep learning Robotics : Autonomous drones with computer vision for industrial and energy applications Sustainable Energy : Solar-based systems, smart grid optimization, and consumption prediction Security : Chaos-based cryptographic systems for secure communications Recent publications (2020-2024) demonstrate strong trends in practical AI implementations. Key themes include energy forecasting using adaptive neural networks (11 papers), robotics for infrastructure maintenance (4 papers), computer vision for biometrics and imaging (3 papers), and cryptographic systems (2 papers). Methodologies frequently involve hybrid approaches combining ANN, fuzzy logic, and hardware acceleration. No awards, students, or grant details were mentioned in the source material. Research collaborations include institutions in Turkey and the Middle East.

Sascha Hunoldمشاهده پروفایل

دانشیار

• Parallel Computing
• High-Performance Computing (HPC)
• MPI Collective Communication
• +۴ مورد دیگر

Sascha Hunold is an Associate Professor at TU Wien, affiliated with the Department of Parallel Computing within the Faculty of Informatics. He holds roles as Vice Dean of Academic Affairs for Informatics Bachelor programs and Curriculum Coordinator for the Master's program in High-Performance Computing. His research focuses on parallel computing, MPI optimization, scheduling algorithms, and reproducible HPC experiments. Education: Habilitation (venia docendi) in Computer Science from TU Wien, PhD in Computer Science from the University of Bayreuth, and MSc in Computer Science from Martin Luther University Halle-Wittenberg. Research interests include MPI collective communication performance, HPC benchmarking, algorithm selection, and task scheduling. He has contributed to tools like ReproMPI and Scheduling.jl. Key awards include Best Paper awards at IEEE CLUSTER 2020 and EuroMPI/Asia 2014, and recognition for reproducible research methodologies. His work emphasizes practical applications of parallel computing in large-scale systems. Teaching responsibilities include courses on parallel algorithms, scientific programming with Python, and HPC project supervision. Active in program committees for major conferences like IPDPS and SC.