جستجوی استاد راهنمای دانشگاهی

Viktor Prasanna is the Charles Lee Powell Chair in Engineering and Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California. He holds courtesy appointments in Computer Science and leads the Center for Energy Informatics, focusing on interdisciplinary research linking energy technologies, computer science, and engineering. Education: BE in Electronics (Bangalore University), ME (Indian Institute of Science), PhD in Computer Science (Pennsylvania State University) His research spans reconfigurable computing, FPGA accelerators, parallel and distributed systems, and big data applications. He has pioneered high-performance architectures and algorithms using FPGAs, impacting domains like networking, security, HPC, and machine learning. Prasanna has published over 600 papers, received 22 best paper awards, and secured >$50M in grants. His work emphasizes energy-efficient computing, with recent grants totaling $12.9M (2016–2021). His h-index is 73, with 23,454 total citations. Scientific Awards: IEEE Fellow, ACM Fellow, AAAS Fellow, W. Wallace McDowell Award, multiple Distinguished Alumnus Awards He has advised over 70 doctoral students and led major centers including CiSoft (Big Data in oilfield tech) and CAST. His editorial roles include Editor-in-Chief of IEEE Transactions on Computers and Journal of Parallel and Distributed Computing.

• Reconfigurable Computing
• FPGA Accelerators
• Parallel and Distributed Computing
• +۳ مورد دیگر

Dragi Kimovski is a Habilitated Assistant Professor in Distributed Systems at Klagenfurt University, Austria, focusing on Edge Computing and AI. He previously held roles at the University of Innsbruck and the University of Information Science and Technology in Macedonia. His research spans Edge/Fog/Cloud computing, multi-objective optimization, and high-performance computing. He has coordinated major projects like 6GContinuum and KärtnerFog, and led initiatives such as DataCloud and ASPIDE. His teaching includes courses on Distributed Computing, Cloud Computing, and IoT. He is the co-creator of the Carinthian Computing Continuum and maintains a blog on Edge AI World. His work emphasizes sustainable and efficient computing solutions for emerging technologies. Education: Not explicitly listed in the provided text. Research Interests: Edge Computing, Fog Computing, Cloud Computing, Multi-objective Optimization, High-Performance Computing, AI in Distributed Systems. His work addresses challenges in resource management, latency reduction, and scalability across heterogeneous environments, with applications in healthcare, IoT, and 6G networks. Projects: 6GContinuum (Coordinator): Focuses on AI services over 6G networks. KärtnerFog (Scientific Coordinator): Develops adaptive Fog infrastructures over 5G. DataCloud (WP5 Leader): Manages Big Data pipelines on the Computing Continuum. ASPIDE (Scientific Coordinator): Advances exascale programming models for data processing. Teaching: Klagenfurt University: Courses include Distributed Computing, IoT, Cloud Computing, and Advanced Programming. University of Innsbruck: Taught Advanced Parallel and Distributed Systems. University of Information Science and Technology: Courses in High-Performance Computing and Network Architectures. Labs/Teams: Co-created the Carinthian Computing Continuum, an automated SDN testbed for Edge computing research. Active in interdisciplinary teams addressing extreme data processing and sustainable computing.

• Edge Computing
• Fog Computing
• Cloud Computing
• +۴ مورد دیگر

Dr. Vincenzo De Maio is a PostDoc Researcher at the Department of Computational Sustainability, Faculty of Informatics, Technische Universität Wien. His research spans quantum computing, edge computing, distributed systems, and sustainable computing, with a focus on hybrid quantum-classical systems and edge AI applications. Current projects: HPQC (2023–2025), ThEMIS FWF (2024–2027), TRITON FWF (2023–2027) Previous projects: RUCON (2016–2023), SWAIN (2021–2024) His research explores the integration of quantum computing with classical systems, particularly in software engineering, workflow decomposition, and hyperparameter optimization. He also works on edge computing for smart cities, environmental monitoring, and traffic safety applications. Recent publications analyze quantum compilation bottlenecks, hybrid quantum-classical workflow orchestration, and quantum neural network optimization. He supervises master's theses on topics like quantum edge benchmarking and hyperparameter tuning. Key collaborations with Prof. Ivona Brandic and others Active in IEEE/ACM conferences and Springer publications

• Quantum Computing
• Edge Computing
• Distributed Systems
• +۲ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Oskar Mencer is a Professor in the Department of Computing at Imperial College London , where he has been a member of academic staff since 2000. He was also a Consulting Professor at Center for Computational Earth and Environmental Science , Stanford University (2009-2010). His research focuses on Multiscale Dataflow Computing , exploring the interaction of hardware and software systems through domain-specific representations, parallel programming, VLSI design, and compiler methodologies. He has contributed to fields including high-performance computing, FPGA optimization, and computational finance. Dr. Mencer's work has been recognized with a Special Award from Com.sult (2012), Imperial College Research Excellence Award (2007), and a Top EPSRC Advanced Fellowship (2001). He has received multiple best paper awards, including at ICFPT'08 and ASAP 2008, and his 2000 paper on stream architectures was recognized as historically significant in 2015. His publications demonstrate a strong emphasis on FPGA acceleration , custom architectures , and parallel processing across geophysics, finance, and neuroscience applications. Special Award for Dataflow Innovation (2012) Best Paper Award - ICFPT'08 Best Paper Award - ASAP 2008 He has served on technical committees for conferences such as FPL , DATE , and FPT , and leads the Computer Architecture Research Group (currently inactive).

• Numerical methods and simulations
• Imaging
• Discretization of space, time and value
• +۷ مورد دیگر

Alexandru Nicolau is a Distinguished Professor and Chair of the Department of Computer Science at the University of California, Irvine (USA), where he has worked since 1992. He previously held positions as Associate Professor (1988–1992) and Assistant Professor (1984–1988) at UC Irvine and Cornell University, respectively. Education : B.A., Brandeis University (1980) MS (1981), Ph.D. (1984), Yale University Research Interests : A leading expert in parallelizing compilers , high-performance computing , and software-hardware co-design , Nicolau pioneered foundational techniques like Percolation Scheduling and Optimal Loop Parallelization . His work enables efficient exploitation of instruction-level parallelism in general-purpose programs, with applications in embedded systems , matrix algorithms , and GPU-based neural networks . He has also contributed to Electronic Design Automation (EDA) and lightweight synchronization protocols . Scientific Awards : IEEE Fellow (2014) ACM SIGPLAN Most Influential Paper (PLDI 20 years) EDAA/IEEE/ACM DATE Most Influential Paper (10 years) ACM ICS Most Influential Paper (25 years) 4 Best Paper awards (VLSI design 2003, ISHPC 2005, CASES 2008, IJCNN 2009) Advising & Grants : He has mentored notable scholars now at Stanford, McGill, and Google, and secured over $20M in funding from NSF , DARPA , and industry leaders like IBM and Intel . His professional service includes chairing ACM ICS’09 and PPOPP’13, and serving on steering committees for LCPC and ICS. Labs & Collaborations : His techniques have been adopted by IBM Watson, Siemens Munich, Fujitsu Labs Japan, and the open-source GCC compiler.

• Parallelizing compilers
• Supercomputing
• High Performance Computing (HPC)
• +۵ مورد دیگر

Marcus Hilbrich is a Professor in Computer Science with extensive academic experience across multiple institutions. His primary affiliation is Chemnitz University of Technology, where he held roles including Assistant Professor, Acting Professor of Software Engineering, and Research Assistant. He also served as Scientific Coordinator at Humboldt University of Berlin (SFB 1404 FONDA) from 2022–2024. Education: He earned a Doctor of Engineering (Dr.-Ing.) from Dresden University of Technology in 2015, focusing on job/user-centric monitoring in distributed environments. His diploma thesis (2008) explored performance analysis of parallel simulations using the SUN Niagara II architecture. Research Interests: Hilbrich specializes in distributed systems, software engineering, cloud computing, and workflow management. He investigates microservices architecture, resilience engineering, and scalable monitoring techniques. His work bridges theoretical foundations (e.g., formal methods for workflows) with practical applications in HPC and data analysis. Publications: He has authored/co-authored over 15 peer-reviewed articles, including key contributions on ßMACH software management guidance, validity constraints in data workflows, and microservices design patterns. His research often addresses interdisciplinary challenges in computational systems and software lifecycle management. Projects: Active involvement in third-party funded projects like SFC (Cloud Computing for Secure Financial Transactions), ECOUSS (parallel simulations), and WisNetGrid (knowledge networks in grids). His work often integrates performance analysis and system scalability. Labs/Teams: Central to his work is the SFB 1404 FONDA collaborative research center, focusing on foundational aspects of distributed heterogeneous environments.

• Distributed Systems
• Software Engineering
• Cloud Computing
• +۵ 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.

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

Sergei Kalinin is the Weston Fulton Professor at the Department of Materials Science and Engineering, University of Tennessee, Knoxville, and holds a Corporate Fellow position at Oak Ridge National Laboratory (ORNL). His academic affiliations include adjunct professorships at Sung Kyun Kwan University (South Korea) and Penn State University. He earned his M.S. summa cum laude from Moscow State University (1998) and Ph.D. from the University of Pennsylvania (2002), both in Materials Science. Kalinin's research focuses on transformative applications of machine learning and AI in materials science, particularly in: Atom-by-atom fabrication using electron beams Big data analytics in scanning probe/transmission electron microscopy Nanoscale electromechanical phenomena in functional oxides Development of novel microscopy techniques and autonomous experimentation frameworks His recent publications (2024-2025) demonstrate strong emphasis on AI-driven microscopy, autonomous labs, and computational materials design. Over 85% of his last 15 articles involve machine learning applications in experimental physics, with emerging focus on human-AI collaboration and real-time instrumentation control. Major scientific honors include: Blavatnik National Award (2018) Feynman Prize in Nanotechnology (2022) Presidential Early Career Award (2009) 4 R&D100 Awards (2008-2023) Fellowships in APS, MRS, IEEE, AVS He directs the ORNL Institute for Functional Imaging of Materials and leads research on autonomous experimentation systems integrating high-performance computing with microscopy platforms.

• Machine Learning in Microscopy
• Atom by Atom Fabrication
• Scanning Probe Microscopy
• +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.

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

Prof. Jesper Larsson Träff is a Full Professor and Head of the Research Unit in Parallel Computing at Technische Universität Wien (TU Wien). His academic role is centered within the Department of Computer Engineering. He holds an MSc and PhD, and has contributed extensively to the field of parallel computing through his research and leadership in international projects. His research focuses on parallel algorithms, scheduling, and optimization of the Message Passing Interface (MPI), with emphasis on high-performance computing (HPC) systems and distributed architectures. Key projects include the Process Mapping initiative (2019–2024), Autotune (2021–2025), and contributions to Exascale Programming Models. He has also led efforts in evaluating MPI performance tools and their reproducibility challenges. Research Areas: Parallel Algorithms, MPI Optimization, HPC, Process Mapping, Collective Communication, and Memory Models. Notable Achievements: Awarded the Best Paper at EuroMPI 2014 and recognized by the Innovation Radar for his work on PGAS-based MPI interoperability in 2018. Teaching: Teaches courses such as Advanced Multiprocessor Programming, Bachelor/Master Thesis supervision, and seminars in Computer Engineering and Theoretical Computer Science. Prof. Träff’s advising includes students researching topics like lock-free data structures, MPI datatype optimization, and task scheduling. His grants span Austrian and EU funding bodies, addressing challenges in exascale computing and reproducible experimental research. He is affiliated with the Vienna Mapping and Sparse Quadratic Assignment (Vienna Mapping) project and actively contributes to international workshops and conferences.

• Parallel Algorithms
• Parallel Computing
• High-Performance Computing (HPC)
• +۴ مورد دیگر

Enes Bajrovic is a researcher affiliated with the Faculty of Computer Science, focusing on high-performance computing (HPC), big data processing, and performance portability. His work spans task-based parallelism, runtime systems, and optimization frameworks for heterogeneous architectures. He has contributed to major European projects like PEPPHER and AutoTune, which aim to advance HPC software tools and autotuning methodologies. His research emphasizes practical applications of parallel computing in domains such as mobile networks and scientific simulations. Education: Dipl.-Ing. Dr.techn., BSc in Computer Science His research interests include developing frameworks for compute- and data-intensive applications, leveraging technologies like Kubernetes, OpenCL, and Intel Xeon Phi coprocessors. He has authored numerous peer-reviewed publications on topics such as pipeline patterns, autotuning algorithms, and hybrid execution models. His work bridges theoretical advancements in parallel computing with real-world software engineering challenges. Bajrovic has collaborated on projects funded by the European Commission’s FP7 program, contributing to deliverables like runtime systems, tuning frameworks, and benchmarking tools. His research also addresses the integration of big data processing with HPC, particularly in telecommunications and distributed computing environments.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +۳ مورد دیگر

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

• Computer Science
• Cybersecurity
• Distributed Systems
• +۵ مورد دیگر

Thomas Fahringer is a full Professor and Head of the Distributed and Parallel Systems Group at the University of Innsbruck's Institute of Computer Science. His research focuses on parallel computing, distributed systems, and high-performance computing (HPC), with particular emphasis on GPU acceleration, cloud/edge computing, and serverless architectures. He leads interdisciplinary projects involving scientific workflows, resource management systems, and exascale computing solutions. Key research areas include: Design of high-level APIs for accelerator clusters (e.g., Celerity-RSim) Optimization of IoT and LoRa networks using machine learning Scalable key-value stores for geo-distributed systems Workflow scheduling in edge-cloud continuum environments Recent work emphasizes automation of deployments, energy-efficient transmission policies, and fault-tolerant orchestration of serverless functions. He actively participates in community initiatives like the Workflows Community Summit, driving advancements in scientific workflows and HPC ecosystems. His lab develops frameworks such as Apollo and AllScale, targeting exascale computing and distributed runtime systems.

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +۴ مورد دیگر

Majid Salimibeni is a PostDoc Researcher and Research Fellow at Vienna University of Technology (TU Wien) , affiliated with the Parallel Computing research unit. He previously held a Postdoc position at the Department of Computer Science at the University of Salerno, Italy (2024), and has been a visiting researcher at TU Wien (2023). Currently based in Vienna, Austria, his work focuses on High Performance Computing (HPC) and its interdisciplinary applications. Education: Ph.D. in Computer Science, University of Salerno, Italy (2020–2024) M.Sc. in Computer Engineering (Software), Shiraz University, Iran (2017–2020) B.Sc. in Computer Engineering (Software), University of Birjand, Iran (2012–2016) Salimibeni's research explores High Performance and Parallel Computing , with a focus on optimizing GPU communication via NCCL , improving energy efficiency in heterogeneous computing environments , and enhancing MPI collective algorithms for large-scale systems. His work bridges HPC infrastructure with emerging applications in Distributed Deep Learning and AI . Recent publications highlight his expertise in GPU frequency scaling , NCCL profiling , and process arrival pattern analysis to improve distributed system performance. These works appear at top conferences like IPDPS , Cluster Computing , and ASHPC . Scientific Awards: Best Paper Award, Bench 2022 Salimibeni contributes to academic committees as a program committee member for IEEE ICPP 2025, ISC 2025, and BigHPC 2024, and serves on artifact evaluation committees for ASPLOS and CF conferences.

• High Performance Computing
• Parallel Computing
• Message Passing in Large-scale HPC systems
• +۶ مورد دیگر