جست‌وجوی استاد و پژوهشگر

• Parallel Computing
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Nicola Bombieri serves as a Full Professor at the University of Verona's Department of Engineering for Innovation Medicine. His academic profile demonstrates extensive involvement across multiple degree programs including Medical Bioinformatics, Human Centered Medical System Engineering, and Computer Engineering for Intelligent Systems. His research focuses on parallel computing architectures and embedded systems design , with particular expertise in multi-core and many-core programming using CUDA, OpenCL, OpenACC, OpenMP, and MPI frameworks. His work bridges computer engineering with medical applications through cyber-physical systems. ERC Research Sectors: Computer Architecture, Embedded Systems, Distributed Systems, Parallel Computing, Artificial Intelligence Research Groups: Electronic Systems Design (ESD), PARCO (Parallel Computing), INdAM Research Unit Professor Bombieri coordinates the PhD program in Intelligent Systems Engineering and serves on multiple academic committees including the PhD School Board and Computer Science Teaching Committee. His administrative roles include Referent for IT Services in Biotechnology and Communication/Orientation responsibilities within his department. His office is located at Ca' Vignal 2, Floor 1, Room 1.60, with contact number +39 045 802 7094. His personal webpage is available at http://profs.sci.univr.it/~bombieri.

Hailu Xuمشاهده پروفایل

استادیار

• Big Data Processing
• Stream Processing Systems
• Edge/Cloud Computing Security
• +۴ مورد دیگر

Dr. Hailu Xu is an Assistant Professor in the Department of Computer Engineering & Computer Science at California State University, Long Beach (CSULB). He holds a Ph.D. in Computer Science from Florida International University (2020), M.S. from University of Toledo (2016), and B.S. from North China Electric Power University (2014). His research focuses on systems design with emphasis on big data processing, stream processing, edge/cloud computing security, and AI integration. He designs systems for social stream data analysis, failure recovery in distributed systems, and combating misinformation in social networks. Research interests include: AI-driven system optimization Edge-cloud collaboration for real-time processing Security in distributed environments Federated learning frameworks Anti-spam mechanisms for social platforms Recent work highlights trends in federated learning efficiency, fault recovery in stream systems, and scalable misinformation detection. His 2023 Neurocomputing paper introduced WSSGCN, while 2024's StraightLine scheduler addresses ML workload optimization. Received NSF Panel Reviewer role (2023) PC member for IEEE Big Data 2024 and ACM Multimedia 2022 Advising undergraduate/graduate students in CS/CE fields. Current projects involve federated learning optimization and edge computing security. His lab develops systems like COS2 for pandemic misinformation detection and Xunified for GPU memory management.

• Computer Science
• Information Technology
• Marine Conservation Technology
• +۳ مورد دیگر

Jason Holdsworth is a Lecturer at James Cook University (JCU), specializing in Digital Technology and Computer Science. He holds a PhD in Computer Science from JCU, with earlier degrees including a BSc (Hons) and a double major BSc in Computer Science from the same institution. His research focuses on applying computer technology to real-world challenges, such as marine conservation through mobile apps and image classification systems for WWF, and improving abalone farming via image segmentation techniques. He is also engaged in educational technology, particularly enhancing adult learner engagement through digital methods. Holdsworth has been recognized with awards including the Advance Queensland Community Digital Champion (2017) and CSIRO STEM Professional (2017). He actively contributes to JCU’s Indigenous Australian Datathon Steering Committee and has advised students on topics like autonomous CNN architecture selection. His work spans healthcare technology (e.g., VR in hemodialysis), embedded systems (C++20 coroutines on microcontrollers), and collaborative learning tools (e.g., academagogy frameworks). Over 41 publications highlight his interdisciplinary research, including studies on asynchronous IoT programming, mobile application performance models, and collaborative emergency management systems. His work often bridges theory and practice, emphasizing sustainable tech solutions for education, healthcare, and environmental conservation.

FRANCISCO SANTIAGO ARGUELLO PEDREIRAمشاهده پروفایل

استاد

• Remote Sensing
• Multispectral Imaging
• Hyperspectral Imaging
• +۵ مورد دیگر

Francisco Santiago Argüello Pedreira is a Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing within the Higher Technical School of Engineering. He holds a PhD from the same institution (1992) with a thesis on parallel-segmented architectures for orthogonal transforms, supervised by Dr. Emilio López Zapata. His research focuses on remote sensing, multispectral/hyperspectral imaging, machine learning, and GPU programming. He leads the ARQCOMP (Computer Architecture) research group and has pioneered GPU-accelerated methods for image registration, anomaly detection, and classification. Key contributions include hybrid parallel computing frameworks (CUDA/OpenMP/MPI), GAN-based domain adaptation, and innovative data augmentation techniques for hyperspectral imagery. His work spans environmental applications (e.g., Galician river monitoring), forestry mapping, and high-resolution land cover analysis. Notable projects include the EffBaGAN and ResBaGAN models for addressing data scarcity and forest mapping challenges. He has developed open-source tools like HypeRvieW for hyperspectral data processing. Publications emphasize algorithm optimization for GPU architectures, with a focus on real-time processing and heterogeneous computing platforms. His research bridges computer architecture advancements with practical applications in Earth observation and environmental science.

JORGE ALBERTO SUAREZ GAREAمشاهده پروفایل

استادیار

• Deep Learning
• Hyperspectral Imaging
• Remote Sensing
• +۳ مورد دیگر

Jorge Alberto Suarez Garea is an Assistant Professor at the Department of Electronics and Computing within the Higher Technical School of Engineering at the University of Santiago de Compostela. He holds a PhD from the same institution (2021), specializing in Deep Learning-Based Classification Techniques for Hyperspectral Images in Real Time , supervised by Dr. Francisco Argüello Pedreira and Dr. Dora Blanco Heras. His research focuses on deep learning applications in remote sensing , including real-time hyperspectral image processing, parallel computing frameworks (CUDA, OpenMP, MPI), and environmental monitoring using UAV-based multispectral imagery. He is affiliated with the ARQCOMP Research Group (Computer Architecture), emphasizing interdisciplinary work between computer science and environmental science. Key contributions include developing Galician Rivers Multispectral Anomaly Detection Dataset , hybrid parallel domain adaptation methods, and open-source tools like HypeRvieW for hyperspectral data processing. His work bridges algorithmic innovation with hardware optimization, particularly in GPU-accelerated systems. Research interests span neural networks , domain adaptation , texture analysis , and real-time embedded systems , with a focus on environmental applications such as watershed monitoring and ecosystem anomaly detection.

Dragan H. Stojanovicمشاهده پروفایل

استاد

• Artificial Intelligence
• Edge Computing
• Smart City Systems
• +۳ مورد دیگر

Dr. Dragan H. Stojanovic serves as a Professor at the Faculty of Electronic Engineering, University of Niš, specializing in computer science since his appointment in 2015. His academic journey spans over three decades at the same institution, where he earned all his degrees and now leads research in cutting-edge computational fields. His educational background includes: PhD in Electrical Engineering and Computing (2004, University of Niš) MSc in Electrical Engineering and Computing (1998, University of Niš) BSc in Electrical Engineering and Computing (1993, University of Niš) Stojanovic's research centers on Artificial Intelligence and Edge Computing , with significant contributions to Smart City infrastructures, parallel processing algorithms, and neural network applications. His work bridges theoretical computer science with practical implementations in urban systems, demonstrated through collaborations on 1 national and 2 international research projects . Recent publications (2022-2025) reveal a strategic pivot toward resource-constrained AI deployment across edge-fog architectures, while earlier work (2014-2020) established foundations in geospatial data parallelization and social media analytics. His publication portfolio shows evolving expertise: 2014-2019: Focus on parallel computing (MPI/CUDA) for geospatial and trajectory data 2020-2022: Integration of IoT and social networks into Smart City business processes 2023-2025: Advanced Edge AI optimization for real-time object detection and prediction systems Stojanovic actively contributes to academic knowledge transfer through project leadership and has published 16 journal articles in impact-factor journals. His current research trajectory emphasizes sustainable computing paradigms for urban environments, with emerging work on competency-focused AI education frameworks.

Saman Aryanaمشاهده پروفایل

دانشیار

• Multiphase Flow
• Subsurface Transport
• CO2 Sequestration
• +۲ مورد دیگر

Dr. Saman Aryana is an Associate Professor in the Department of Chemical Engineering at the University of Wyoming. He holds a PhD in Energy Resources Engineering from Stanford University and bachelor's/master's degrees in Civil Engineering from UT Arlington. His research focuses on multiscale flow dynamics in subsurface systems, including multiphase flow in permeable media, CO2 foam stabilization using nanoparticles, and computational modeling of transport phenomena. He directs experimental work using microfluidic platforms and develops advanced simulation methods for porous media systems. Dr. Aryana teaches courses including Mathematical Methods in Chemical Engineering, Macroscale Models of Flow Through Permeable Media, and Petroleum Economics. He has mentored over 15 graduate students and received numerous awards including the DOE EFRC Award and Magnificent Mile Diversity Award.

Simon Hengeveldمشاهده پروفایل

پژوهشگر

• Distance Geometry
• Computational Geometry
• Bioinformatics
• +۳ مورد دیگر

Simon Hengeveld is a Researcher and Software Developer for Health Research at Wageningen University. He holds a PhD from Université de Rennes (2024), and bachelor's/master's degrees in Computer Science from Utrecht University. His research focuses on computational geometry, protein structure determination via Distance Geometry, and human motion analysis. He has developed applications like the UNESCO World Heritage Collect app, combining software engineering with historical and travel interests. Education: Bachelor's/Master's in Computer Science, Utrecht University PhD in Structural Biology/Computational Geometry, Université de Rennes 1 Research Interests: His work bridges computational methods with biological applications, including protein structure determination using NMR data and optical computing. He also explores human motion retargeting and geometric algorithms for adaptive maps. Simon emphasizes practical implementations, such as GPU-based solutions for 1D Distance Geometry problems. Publications: His articles span computational geometry, bioinformatics, and algorithm design, with contributions to journals like Journal of Optics and conferences such as SOCG, GSI, and BIBM. Key themes include optical processors for matrix operations and geometric algorithms for skeletal posture analysis. Awards: No scientific awards explicitly mentioned. Advising/Grants: No formal advising roles or grants detailed in the text. Labs/Teams: Developed the UNESCO World Heritage Collect app with collaborators, though no formal university lab affiliation is noted.

• Compiler Optimization
• High Performance Computing
• Sparse Matrix Computations
• +۴ مورد دیگر

P. Sadayappan is a Professor at the School of Computing, University of Utah, specializing in high performance computing, compiler optimization, and scalable machine learning. His research focuses on developing efficient computational methods for scientific applications, particularly in the areas of sparse/dense matrix and tensor computations. His research interests include: Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Sadayappan's recent publications demonstrate a strong focus on tensor computations, GPU acceleration, and compiler optimizations for machine learning workloads. His work spans from fundamental compiler theory to practical implementations that improve performance across various architectures. A significant trend in his recent work involves the development of frameworks for efficient tensor operations, sparse matrix computations, and domain-specific code generation, with particular emphasis on performance portability across heterogeneous computing platforms. His notable scientific achievement includes receiving the ACM SIGPLAN Most Influential PLDI Paper Award in 2018 for his work on polyhedral compilation. Sadayappan has been principal investigator or co-investigator on numerous significant research grants, including: NSF award #2217154 (2022-2027): A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF award #2112606 (2021-2026): AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) NIH SBIR-Phase 2 (2023-2025): Enabling next generation machine learning for large scale image analysis NSF award #2009007 (2020-2024): Data Locality Optimization for Sparse Matrix/Tensor Computations DARPA SBIR-Phase 2 (2017-2022): Performance Portable Framework for Developing Graph Applications He teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah and collaborates extensively with researchers across multiple institutions on projects involving computational chemistry, physics simulations, graph analytics, and machine learning.

Wonmuk Hwangمشاهده پروفایل

استاد

• Computational Biophysics
• Molecular Biomechanics
• Molecular Motors
• +۵ مورد دیگر

Wonmuk Hwang is a Professor at Texas A&M University with joint appointments in Biomedical Engineering, Materials Science & Engineering, and Physics & Astronomy. As Principal Investigator of the Hwang Laboratory, he leads research in computational biophysics and molecular biomechanics. His lab focuses on understanding how biomolecules move, interact, and assemble to drive biological phenomena, with applications in therapeutic development and bio-inspired engineering. His research program spans four interconnected domains: Multiscale Modeling (water dynamics, biofilament mechanics, supramolecular assembly), Kinesin Motor Proteins (ATP-driven motility mechanisms), T-Cell Receptor Mechanobiology (force-dependent immune recognition), and Bioimage Analysis (computational tools for complex imaging datasets). The lab develops advanced algorithms including the Computer-Aided Feature Extraction (CAFE) platform for biological structures. Analysis of 15 recent publications (2021-2025) reveals dominant research threads: 1) Computational methods for electrostatic interactions and molecular dynamics (CHARMM enhancements, GLST cubature), 2) TCR mechanobiology exploring force-dependent discrimination mechanisms, 3) Viral protein dynamics (influenza NS1, HIV MPER), and 4) Developmental biophysics (zebrafish neurodevelopment). This reflects Hwang's dual focus on method development and biological applications. Professor Hwang mentors an active research group including 3 graduate students (Sofiya Bettencourt, James Gonzales, Yue Qiu) and 3 undergraduates (Nithanial John, Jomi Olufemi, Elaine Wu). His alumni hold positions at Vanderbilt University, IBM Research, Bristol-Myers Squibb, and academic institutions worldwide. The lab maintains extensive collaborations with experimental groups including those at NIH, Harvard, and Seoul National University.

Hartwig Anztمشاهده پروفایل

دانشیار

• Algorithm Engineering
• High Performance Computing
• Numerical Linear Algebra
• +۳ مورد دیگر

Hartwig Anzt is a Research Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, and Director of the Innovative Computing Lab (ICL). He holds a Senior Research Scientist position at the Steinbuch Centre for Computing at Karlsruhe Institute of Technology (KIT), where he previously held an Early Career Award and a Junior Professorship. He earned a Diploma in Industrial Mathematics (2009) and a PhD in Applied Mathematics (2012) from KIT, with a year of studies at the University of Ottawa. His research focuses on algorithm engineering for computational mathematics, particularly numerical linear algebra, GPU computing, and mixed-precision methods. Key areas include asynchronous algorithms, communication-avoiding techniques, and sustainable software engineering for exascale computing. He leads development of the Ginkgo library, designed for high-performance numerical linear algebra on heterogeneous architectures. Anzt's work emphasizes practical software solutions for scientific computing, including sparse linear algebra, batched solvers, and FAIR-compliant research software practices. His contributions span fusion plasma simulations, cardiac electromechanics, and scalable data compression techniques for extreme-scale systems. Education: Diploma (2009), PhD (2012) in Applied Mathematics from KIT Key Roles: ICL Director, xSDK Multiprecision Project Lead Software Contributions: Ginkgo Library, JuMonC Tool, Compressed Basis GMRES

• 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

Jose Manuel Claver Iborraمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +۷ مورد دیگر

Dr. José Manuel Claver Iborra is a Full Professor at the Department of Computer Science, School of Engineering (ETSE-UV), University of Valencia. He holds a PhD in Computer Science (Parallel and Distributed Computing program) from the Technical University of Valencia and an MSc in Physics (specialized in Electronics and Computer Science) from the University of Valencia. As an IEEE Senior Member, he focuses on cloud computing, video coding, parallel/distributed systems, reconfigurable computing, and network protocols for real-time applications. Current Research: Cloud-based video encoding, GPU acceleration for DNA analysis, FPGA-based network protocols, and indoor localization systems Academic Leadership: Coordinator of the UV-Tirant node in the Spanish Supercomputing Network (RES) His recent publications analyze GPU-based motion estimation, heterogeneous computing for video standards (H.264/AV1), and QoS scheduling algorithms. He supervises PhD and Master's theses on sensor networks, FPGA programming platforms, and parallel applications. Scientific Awards IEEE Senior Member He has directed funded projects on cloud infrastructure, distributed video processing, and reconfigurable systems since the 1990s.

Dominik Żelaznyمشاهده پروفایل

پژوهشگر

• Scheduling
• Evolutionary Algorithms
• Multi-objective Optimization
• +۳ مورد دیگر

Dominik Żelazny is a researcher at the Faculty of Information and Communication Technology, Department of Computer Engineering, Wrocław University of Science and Technology. He focuses on scheduling algorithms and multi-objective optimization using evolutionary computation methods. Current position: Researcher Primary affiliation: Department of Computer Engineering Research interests include: Development of evolutionary algorithms for scheduling problems Multi-criteria and multi-objective optimization GPU-accelerated computing using CUDA Parallel and distributed algorithm design Industrial applications of operational research

• Cloud Computing
• Database Systems
• Parallel Processing
• +۳ مورد دیگر

Mariusz Fraś is a researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He works within the Department of Computer Science and Systems Engineering , focusing on computational systems' performance and cloud-based architectures. His research spans database optimization, parallel processing, and mobile application frameworks. Research interests include: Computational cloud scalability and cost optimization Database application performance analysis Parallel processing frameworks Mobile sensor data processing Web content delivery efficiency His publications demonstrate expertise in cloud computing trends, with 10 peer-reviewed works since 2014. Notable collaborations include researchers like Piotr Karwaczyński and Bogdan Marczuk. Current research likely continues exploring hybrid cloud environments and software performance metrics. Contact: mariusz.fras@pwr.edu.pl , Room 301, Building C-3, Wrocław University of Science and Technology.