جست‌وجوی استاد و پژوهشگر

Katarzyna Świderekمشاهده پروفایل

دانشیار

• Computational Chemistry
• Biochemistry
• Enzyme Catalysis
• +۴ مورد دیگر

Dr. Katarzyna Świderek is an Associate Professor at Universitat Jaume I (UJI) in Castelló, Spain, where she leads research at the Institute of Advanced Materials (INAM) within the Department of Physical and Analytical Chemistry. She serves as Senior Researcher in Computational Biochemistry group (RG9) and Project Leader for the DeProtCa initiative focused on enzyme design principles. Dr. Świderek earned her Master's (2007) and PhD (2011) degrees in Physical Chemistry from Lodz University of Technology, Poland, under Prof. P. Paneth. Her academic journey includes postdoctoral positions at University of Valencia (2011-2014), University Jaume I (2015 onward), and a visiting position at University of Bath (2017). In December 2024, she was promoted from Ramon y Cajal fellow to Associate Professor (Profesor Titular). Her research focuses on theoretical studies of reactivity in cellular processes , with three main interconnected areas: Origin of biocatalysis Inhibition and drug design Design of biocatalysts Most of her work employs molecular dynamics (MD) simulations with hybrid QM/MM potentials to investigate enzymatic mechanisms, with applications spanning pharmaceutical, medical, and industrial domains. Analysis of her recent publications reveals a strong emphasis on computational enzymology, particularly studying proteases (including SARS-CoV-2 Mpro), proteasome inhibition mechanisms, and enzyme design strategies. Her work increasingly integrates AI approaches with traditional computational methods, demonstrating evolution in the field toward more sophisticated predictive models for enzyme behavior. Dr. Świderek has secured multiple competitive research grants, including a recent Spanish Ministry of Science project (2025-2027) titled 'Deciphering the structural and kinetic principles of enzymes to design advanced protein-based catalysts for enhanced biocatalysis.' One of her studies on SARS-CoV-2 Mpro inhibition ranked in the top 5% of highly cited works from Royal Society of Chemistry Journals. She actively mentors graduate students including Carmen Ramos, Álvaro Serrano López de la Vieja, and Adrián Fernández de la Pradilla Ibáñez. Her research group collaborates internationally with institutions across Spain, Poland, USA, UK, Canada, and Italy, utilizing the T∙R∙I∙N∙I∙T∙Y computational cluster with 320 CPUs and multiple GPUs for their demanding simulations.

• High-Performance Computing
• LiDAR Technology
• Computer Vision
• +۵ مورد دیگر

David López Vilariño is a Lecturer at the University of Santiago de Compostela, affiliated with the Department of Electronics. His research focuses on LiDAR data processing, FPGA acceleration for high-performance computing (HPC), and embedded systems. He teaches courses such as Fundaments of Electronic Instrumentation , Heterogenous Programming , and The Physics of Computing , contributing to bachelor’s and master’s programs in Physics, Informatics Engineering, and High Performance Computing. His research interests span LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He has developed algorithms for LiDAR data analysis, FPGA-based motion estimation, and GPU-accelerated medical image processing. Notable contributions include tools like the Open Lidar Visualizer and Analyser for 3D point cloud visualization. Dr. Vilariño’s work bridges computer architecture, signal processing, and geomatics. His recent publications emphasize optimizing HPC workloads using FPGAs and Intel OneAPI, as well as automated LiDAR-based detection of power lines, road points, and pedestrian zones. He collaborates on interdisciplinary projects involving parallel computing, embedded vision systems, and real-time surveillance applications. Teaching responsibilities include coordinating courses for the Máster Universitario en Computación de Altas Prestaciones, a joint program with the University of A Coruña. No academic awards are listed, but his active role in teaching and research grants underscores his contributions to the field.

Dora Blanco Herasمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Computing
• GPU Computing
• +۷ مورد دیگر

Dora Blanco Heras is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (Spain). She holds a PhD in Physics (cum laude) from the same institution and has served as Head of the Sustainable Development Office (2005–2010). Her research focuses on high performance computing, parallel computing, and GPGPU techniques applied to remote sensing and medical image processing. She leads the ARQCOMP research group in Computer Architecture and is affiliated with the Center for Research in Intelligent Technologies (CITIUS). Research interests include: High-performance computing for real-time image processing GPU acceleration of hyperspectral/multispectral analysis Anomaly detection in fluvial ecosystems Domain adaptation for Earth observation Machine learning for remote sensing classification Recent work emphasizes deep learning architectures (ResNet, GANs) for land-cover prediction and multispectral anomaly detection. Over 100 peer-reviewed publications span journals like IEEE Transactions on Geoscience and Remote Sensing, with impactful contributions to GPU-optimized algorithms and open-source tools like HypeRvieW. Professional service includes organizing HDCRS Summer Schools on high-performance geospatial AI and chairing Euro-Par workshops. Her team develops cutting-edge solutions for environmental monitoring and sustainable computing.

PABLO QUESADA BARRIUSOمشاهده پروفایل

استاد

• Remote Sensing
• Parallel Computing
• GPU Acceleration
• +۳ مورد دیگر

Pablo Quesada Barriuso is a Professor at the University of Santiago de Compostela's Department of Electronics and Computing, affiliated with the Higher Technical School of Engineering. He holds a PhD in Information Technology (2015, Cum Laude) from the same institution, focusing on real-time spectral-spatial classification of n-dimensional images using GPUs. His research emphasizes parallel computing techniques for hyperspectral/multispectral remote sensing data processing, targeting low-cost devices to optimize real-time analysis and storage efficiency. Research interests include GPU-accelerated algorithms, wavelet-based denoising, mathematical morphology, and deep learning applications for environmental monitoring. He has been part of the Computer Architecture Group since 2010, contributing to projects like the Galician Rivers Multispectral Anomaly Detection Dataset and CGAN-based reconstruction models. His publications (2013-2025) highlight innovations in superpixel segmentation, watershed algorithms on GPUs, and ELM-based classification systems. While no awards are explicitly listed, his work demonstrates significant contributions to efficient GPU computing for remote sensing challenges. Advising and grants information is not detailed in the provided text. His work is closely tied to the ARQCOMP (Computer Architecture) research group, exploring hardware-software co-design for high-performance imaging solutions.

• Computer Architecture
• Heterogeneous Parallel Systems
• GPU Computing
• +۴ مورد دیگر

Juan Luis Aragon Alcaraz is a Full Professor in Computer Architecture at the University of Murcia (Spain), affiliated with the Faculty of Informatics and the Department of Computer Engineering and Technology. He obtained his PhD in Computer Engineering from the University of Murcia in 2003 and held a postdoctoral position as a Visiting Assistant Professor and Researcher at the University of California, Irvine. He has held visiting researcher positions at EPFL (2013) and Princeton University (2015–2022). His research focuses on computer architecture, emphasizing heterogeneous parallel systems, GPUs, memory hierarchies, and energy-efficient microarchitecture design. He has advised 7 PhD theses and co-authored over 60 publications in top-tier conferences and journals. His work includes contributions to GPU-accelerated medical imaging, energy-efficient graphics pipelines, and hardware-software co-design for parallel systems. Key research trends in his articles include optimizing GPU performance for real-time applications, energy efficiency in graphics rendering, and scalable latency tolerance in manycore systems. His patents include innovations in presbyopia correction devices leveraging GPU and FPGA technologies. Dr. Aragón has taught numerous courses in computer architecture and design, including Computer Architecture and Organization, Advanced Aspects in Multicore Architectures, and Embedded Systems Technologies. His labs and collaborations span academic-industrial partnerships, emphasizing practical applications of his theoretical contributions.

Domingo S. Rodríguez Baenaمشاهده پروفایل

دانشیار

• Database Technologies
• Big Data
• Sensors Data Processing
• +۴ مورد دیگر

Domingo S. Rodríguez Baena is an Associate Professor at the Department of Computer Science (Information Systems) at Universidad Pablo de Olavide, Spain. His research focuses on database technologies, big data programming, and sensor data processing for knowledge extraction, with applications in bioinformatics and computational biology. Databases Design & Management Cloud Computing Final Degree Project Supervision External Internships Coordination His work integrates high-performance computing with bioinformatics to address challenges in gene co-expression networks , recommender systems , and biomedical data analysis . Publications highlight advancements in biclustering algorithms , GPU-accelerated computation , and biomedical informatics tools like CyEnGNet—App and BIGO. Recent collaborations focus on cancer biomarker analysis , coronavirus immune response modeling , and livestock behavior monitoring using sensor data. Research outputs emphasize parallel computing and ensemble methods to tackle data sparsity and scalability issues.

Leonardo Surianoمشاهده پروفایل

پژوهشگر

• Runtime Adaptive Hardware/Software execution
• High-Performance Computing
• Parallel Computing
• +۴ مورد دیگر

Leonardo Suriano is a Ph.D. student at the Universidad Politécnica de Madrid (UPM), affiliated with the Department of Electronic Engineering. His research focuses on runtime adaptive hardware/software execution in complex heterogeneous systems (CPU + FPGA + GPU), high-performance computing, parallel computing, and embedded systems. He has contributed to European projects Enable S3 and Cerbero, developed tools for dataflow-based design space exploration, and is a contributor to the PREESM open-source project on GitHub. His work bridges hardware acceleration, cyber-physical systems, and real-time computation. He received the Best Research Project at the Industrial Research Meeting 2016 Best Oral Session award in the Simposio Tu Tesi en 4 Minutos (UPM, 2018) He has published extensively on heterogeneous MPSoC optimization, inverse kinematics, and image processing, with key subfields including GPU/FPGA integration, task scheduling, and firmware development.

• Computer Graphics
• 3D Modeling
• Computer Animation
• +۷ مورد دیگر

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique (Institut Polytechnique de Paris), where she serves as Dean of the Master of Science and Technology program and leads the STREAM research team at LIX Laboratory (CNRS/IP Paris). Her research bridges computer graphics, HCI, and AI, focusing on expressive 3D modeling, animation, and virtual environments. Education: 1987: M.Sc. in Computer Science, École Normale Supérieure & University Paris XI 1990: Ph.D. in Computer Graphics, University Paris XI 1995: Habilitation in Computer Science, Institut National Polytechnique de Grenoble Research Interests: She pioneers user-centered tools for 3D content creation, combining gesture-based interfaces (sketching, sculpting) with knowledge-enhanced procedural models. Key areas include implicit surfaces, physical simulation, AI-driven animation, and accessible tools for non-experts. Her work enables intuitive design of complex shapes, terrains, and dynamic scenes. Publication Trends: Recent work (2023–2025) emphasizes AI-integrated graphics, including controllable natural phenomena (volcanoes, erosion), sketch-based bio-visualization, reinforcement learning for animation, and real-time character control. Cross-disciplinary applications span geology, biology, and cinematography. Awards & Honors: ACM Steven A. Coons Award (2023) French Academy of Sciences (2020) ACM SIGGRAPH Academy (2019) ERC Advanced Grant EXPRESSIVE (2012–2017) CNRS Silver Medal (2012) Leadership & Grants: She founded research teams (STREAM, Imagine, Evasion) and secured major funding: ERC Advanced Grant, Google-École Polytechnique AI Chair (2018–2021), Horizon 2020 project CLIPE (2020–2024), and Hi!Paris Creative AI Fellowship (2021–2024). She mentors researchers in geometric modeling, animation, and AI. Labs & Teams: Directs the Modeling, Simulation & Learning pole at LIX (2019–2023). Founded STREAM (Structure Representation for Animation/Modeling) and Imagine (Inria/Grenoble teams), advancing real-time graphics and procedural content.

• 3D Modeling
• Real-Time Animation
• Computer Graphics
• +۴ مورد دیگر

Damien Rohmer is a Professor of Computer Science at École polytechnique , Institut Polytechnique de Paris. He leads the VISTA research team at LIX (CNRS UMR 7161) and serves as Deputy Director of LIX since 2024. Current PhD supervisions with ANR/CIFRE funding Open-source tool development for computer graphics education Editorial roles in SCA, MIG, SIGGRAPH conferences His research spans 3D modeling , real-time animation , and user-interactive simulation , with applications in entertainment, medical sciences, and design. Key sub-areas include: Character motion retargeting Multi-scale natural phenomenon simulation Field-based implicit modeling Wearable technology visualization Gesture-controlled animation systems Recent publications in SIGGRAPH , Eurographics , and SCA earned multiple Best Paper/Poster awards in 2024-2025. He co-develops the CGP library for educational 3D programming and maintains community resources for French computer graphics institutions.

• High-performance computing
• Parallel programming
• Computer architecture
• +۷ مورد دیگر

Enrique Morancho Llena is a Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). He is an active member of the UPC PM - Programming Models research group and has been affiliated with the CAP - Grup de Computació d'Altes Prestacions (High-Performance Computing Group), one of UPC's most productive research teams with over 120 publications. His academic career spans more than 30 years, with continuous research output from 1994 to the present. Dr. Morancho Llena's research spans multiple areas in computer systems, with particular expertise in high-performance computing, parallel programming models, computer architecture, operating systems, and compiler optimization. His work has evolved from early research in instruction scheduling and memory access optimization to current work on heterogeneous computing, RISC-V architecture, hardware security, and malware detection. He has made significant contributions to OpenMP programming models, GPU acceleration, and virtualization technologies, with publications in top-tier venues including IEEE Transactions on Parallel and Distributed Systems and the Journal of Parallel and Distributed Computing. His recent publications (2021-2024) show a clear trend toward heterogeneous computing architectures, security aspects of computer systems, and RISC-V based technologies. He has published extensively on OpenMP extensions for heterogeneous systems, hypervisor implementations for RISC-V, and security applications including malware detection using opcode analysis. His work bridges theoretical computer architecture concepts with practical implementations for scientific computing and security applications. Dr. Morancho Llena has supervised PhD students, with R. Gran's thesis on "Non-speculative enhancements for the scheduling logic" being one documented example. He has participated in numerous competitive research projects funded by Spanish and Catalan governments, including multiple iterations of the "Computació d'Altes Prestacions" (High-Performance Computing) projects and RISC-V related initiatives under the RIS3CAT program. His collaborations extend across UPC departments and include partnerships with the Barcelona Supercomputing Center. He is an active member of the CAP research group and the PM - Programming Models group, focusing on advanced programming techniques for high-performance systems. His work on the UPC-Computación de Altas Prestaciones series demonstrates long-term leadership in establishing UPC as a center of excellence in high-performance computing research. Current projects include work on RISC-V based accelerators, security in the computing continuum, and trustworthy development environments for cloud services.

• High-Performance Computing
• Computer Networks
• Parallel Computing
• +۵ مورد دیگر

José L. Sánchez is a full Professor in the Department of Computer Systems at the University of Castilla-La Mancha (Spain), where he has held a permanent academic position since 1986. His research spans high-performance computing, network architecture, and GPU acceleration, with particular focus on energy-efficient interconnection networks for data centers and exascale systems. His research interests center on interconnection network optimization , including torus topologies, deadlock-free routing, and QoS provision in high-radix switches. He pioneers energy-efficient networking through on/off link strategies and develops frameworks like VEF traces for modeling MPI traffic in large-scale simulations. His work bridges theoretical network design with practical GPU-accelerated implementations for real-time computer vision and similarity search algorithms. Recent publications reveal a strong trend toward exascale-ready networking solutions , with 60% of his 2019-2022 work addressing energy efficiency in HPC topologies, congestion management in lossy networks, and photonic interconnect challenges. His methodology consistently combines formal network modeling with hardware-aware implementations, particularly leveraging GPU parallelism for bioinspired vision algorithms and metric search optimization. Professor Sánchez leads significant contributions to network simulation tooling, including the TopGen library for topology modeling and VEF3 framework extensions. His collaborative work spans multiple EU institutions, with frequent co-authorship patterns indicating strong ties to research groups specializing in network-on-chip systems and high-performance interconnects.

Francisco Fernández Riveraمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Processing
• Computer Architecture
• +۵ مورد دیگر

Francisco Fernández Rivera is a Full Professor in the Department of Computer Architecture at the University of Santiago de Compostela (Spain), where he obtained both his BS (1986) and PhD (1990). His academic career spans parallel processing, computer architecture, and high-performance computing applications. He has supervised 7 PhD theses and authored over 150 publications, including 60+ journal articles and 90+ conference papers. Research Focus: His work centers on parallel/distributed system optimization, including compilation of irregular codes, performance prediction, sparse matrix algebra, GIS, and memory hierarchy improvements. Key applications include LiDAR data processing for infrastructure analysis, NUMA system optimization, and heterogeneous computing. Publication Trends: Recent works (2021-2024) emphasize LiDAR-based geospatial analysis, thread/memory optimization for NUMA systems, and heterogeneous computing frameworks (e.g., Intel OneAPI). Methodologies feature parallel algorithms, hardware-aware optimizations, and real-world applications in urban planning and remote sensing. Projects & Leadership: Principal investigator for 8 research projects (e.g., HiPerHC2DA, SDNHPC) focused on high-performance and cloud computing. Manages collaborations in geospatial technologies and parallel systems.

Marta Fairénمشاهده پروفایل

استادیار

• Virtual Reality
• Collaborative Virtual Environments
• Haptic Interfaces
• +۳ مورد دیگر

Marta Fairén is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the ViRVIG (Visualització, Realitat Virtual i Interacció Gràfica) Research Group and serves as a senior researcher at the Virtual Reality Center of Barcelona . Her work focuses on advanced VR systems and software development platforms. Department of Computer Science (CS), UPC ViRVIG Research Group Virtual Reality Center of Barcelona Research Interests Virtual Reality software and interfaces with emphasis on collaborative environments Haptic device integration for tactile feedback Visualization of large-scale models in immersive settings Distributed systems for graphics applications GPU-accelerated algorithms for collision detection Educational technology in computer graphics and programming Research Trends The 15 most recent articles demonstrate ongoing work in VR training applications (2024), medical education (2023-2020), haptic rendering techniques (2016-2007), and educational software frameworks (2020-2006). Key themes include GPU optimization, surface detail modeling, and adaptive teaching tools. Scientific Awards Advising & Grants No formal student advisees are listed in the provided content, and grant details are not explicitly mentioned. Labs & Teams Active member of the ViRVIG Research Group and senior researcher at the Virtual Reality Center of Barcelona , focusing on collaborative VR environments and distributed graphics systems.

Max Willseyمشاهده پروفایل

استادیار

• Programming Languages
• Compiler Optimization
• Equality Saturation
• +۷ مورد دیگر

Max Willsey is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley . His research focuses on enhancing program optimization through techniques from programming languages, databases, and systems. He leads the development of tools like egg and egglog , which leverage e-graphs for equality saturation to build robust compiler optimizers. Education: PhD from University of Washington (2021), BS from Carnegie Mellon University (2016) Current Teaching: CS 164 (Programming Languages and Compilers), CS 265 (Compiler Optimization), CS 294-260 (Declarative Program Analysis and Optimization) Research Interests center on equality saturation, e-graphs, Datalog, and their applications in program synthesis, compiler design, and database theory. His work bridges formal methods with practical systems, particularly in hybrid molecular-electronic computing. Notable Scientific Awards: SIGMOD Record Research Highlight (2024) MIT PL Review Selection (2024) Distinguished Paper Awards at OOPSLA (2021) and POPL (2021) Invited Talks at ICCAD (2019) and Biochips Summer School (2019) NSF Graduate Research Fellowship Honorable Mention (2018) As founder of the EGRAPHS Community , he organizes workshops, forums, and seminars for e-graphs researchers. His publications demonstrate a synthesis of compiler optimization, database theory, and molecular computing, with a focus on unifying disparate programming paradigms.

Alastair F. Donaldsonمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Verification
• +۲ مورد دیگر

Alastair F. Donaldson is a Professor in the Department of Computing at Imperial College London, leading the Multicore Programming Group. His research focuses on programming languages, compilers, and automated testing of software systems. He previously directed GraphicsFuzz, an Imperial College spinout acquired by Google in 2018. Education: PhD in Computer Science (University of Glasgow), Postdoctoral Research Fellow (University of Oxford), Visiting Researcher (Microsoft Research Redmond) His research interests include: Compiler Design and Verification Randomized Testing Techniques Multicore and GPU Programming Formal Methods for Software Reliability Recent publications emphasize compiler fuzzing, metamorphic testing, and verification of GPU systems. He actively contributes to the development of testing tools for programming languages and hardware interfaces. Alastair holds leadership roles in academic conferences, including Publicity Co-Chair for POPL 2024 and committee roles in ASE, ECOOP, PLDI, and SPLASH. He maintains a personal website at http://www.doc.ic.ac.uk/~afd and has been registered on conf.researchr.org since 2015.