Find Academic Supervisors

Henri BalView profile

Professor

• Distributed Systems
• Parallel Computing
• High-Performance Computing
• +5 more

Henri Bal is a Full Professor in the Department of Computer Science at Vrije Universiteit Amsterdam's Faculty of Sciences, leading a research group of over 20 members in High Performance Distributed Computing since 2005. His career at the university spans from Research Assistant (1982) to current Professor, with prior appointments including Associate Professor (1994) and split Full Professor roles across Computer Science and Physics (1998). Education: PhD in Computer Science, Vrije Universiteit Amsterdam (1989) MSc in Mathematics, Delft University of Technology (1982) Professor Bal's research integrates fundamental distributed systems with real-world applications, specializing in programming environments for clusters, grids, clouds, and mobile systems. His work spans semantic web reasoning, distributed model checking, and scientific applications in astronomy (LOFAR), high-energy physics (NIKHEF), and climate modeling (Utrecht University), evidenced by systems like Orca, MagPIe, Manta, and Ibis/JavaGAT. Scientific Awards: NWO PIONIER award (1993-1997), comparable to an ERC starting grant Work on Awari recognized in 'The Math Book' as a historic mathematics achievement Multiple first prizes in IEEE International Scalable Computing Challenges (2008, 2010) Two EuroSys Roger Needham PhD awards for his students (2010, 2013) He established foundational Dutch research infrastructure through the NWO DAS project, creating distributed testbeds used by ~100 PhD candidates. As adjunct director of the €40M Virtual Lab for e-Science (2004-2009), he catalyzed the Netherlands eScience Center's formation and participated in EU projects (Gridlab, XtreemOS, Contrail). Currently, he chairs the Department of Computer Science Science committee and explores GPU and smartphone-based distributed systems. His research group actively collaborates with NIKHEF, LOFAR, and Utrecht University on domain-specific applications while advancing hybrid distributed computing frameworks.

• Hardware Acceleration
• High-Level Synthesis
• Asynchronous Circuit Design
• +2 more

Sebastian Michael Wiedemann is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Embedded Computing Systems group. His research focuses on hardware acceleration, asynchronous circuit design, and software-hardware integration techniques. He holds a Diplom-Ingenieur (Dipl.-Ing.) degree and a BSc in Electrical Engineering. His recent work includes publications at the Austrochip Workshop on Microelectronics and a diploma thesis on high-level synthesis tools for asynchronous circuits. His research interests span hardware modeling, GPU computing, and FPGA optimization. Publications highlight advancements in vendor-agnostic preemption mechanisms and asynchronous circuit synthesis methodologies. As a PreDoc Researcher, he contributes to academic projects and teaches courses such as Hardware Modeling and HW/SW Codesign .

Sascha HunoldView profile

Associate Professor

• Parallel Computing
• High-Performance Computing (HPC)
• MPI Collective Communication
• +4 more

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.

Markus SteinbergerView profile

Associate Professor

• GPU Computing
• Parallel Algorithms
• Real-Time Rendering
• +5 more

Markus Steinberger is an Associate Professor at Graz University of Technology (TU Graz), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds a PhD (2013) and Habilitation (2020) in Computer Science from TU Graz. His research focuses on GPU scheduling, parallel computing, real-time rendering, and procedural content generation. He has held roles including Assistant Professor (2015–2021) and Director of Cloud Rendering at Huawei (2021–present). Education: MSc (2010), PhD (2013), Habilitation (2020) in Computer Science from TU Graz PostDoc and Research Positions: NVIDIA (2013–2014), Max Planck Institute (2015–2017) Research interests include dynamic resource scheduling, GPU algorithms, and high-performance visualization. His work has been recognized with awards such as the GI Dissertation Prize (2014), Eurographics Best Paper (2021), and the Heinz Zemanek Prize. Key Projects: Cloud-native rendering, procedural planet rendering, and GPU-optimized algorithms Advising includes PhD student Karl Haubenwallner. His lab explores cutting-edge techniques in real-time graphics and parallel computing.

Michael MeisterView profile

Professor

• Wastewater Treatment Plant Modeling
• Numerical Flow Simulation
• Smoothed Particle Hydrodynamics (SPH)
• +3 more

Michael Meister is a Professor at MCI – The Entrepreneurial School, holding a position in the Department of Environmental, Process & Energy Technology. He has held roles including Junior Professor and Post-Doc researcher at Universität Innsbruck and teaching positions at Imperial College London. His research focuses on wastewater treatment plant modeling, numerical flow simulation using SPH, and hydraulic simulations. Education: PhD in Environmental Technology (University of Innsbruck), MSc in Physics (Imperial College London), BSc in Physics (University of Innsbruck). Research interests include optimizing anaerobic digester mixing efficiency, CFD applications in wastewater processes, and developing SPH-based models for environmental systems. His work bridges computational fluid dynamics with practical engineering solutions for sustainable energy and water management. Key projects include OPTIFAUL (energy-efficient mixing optimization) and SPHAUL (SPH-based digester modeling). He has received multiple teaching awards from MCI and recognition for his doctoral and undergraduate work. Advised over 15 students on topics ranging from biogas potential estimation to CFD-based energy optimization in wastewater treatment plants. Active in peer review for journals and conferences, and serves on evaluation committees for international research programs. Labs/Teams: Leads the OptiFaul research initiative and collaborates with institutions like the University of Leeds and the Austrian Research Promotion Agency.

Tirado FranciscoView profile

Professor

• Power Aware Computing
• Heterogeneous Computing
• Applications optimization
• +4 more

Francisco Tirado is a Full Professor of Computer Architecture at Complutense University of Madrid since 1986. He has held leadership roles including Vice-Chancellor for Research (2012-2015), Dean of the Faculty of Physics (1994-2002), and Director of the Complutense Supercomputing Center (2002-2007). He is a founding member of the Spanish Society for Computer Science (SCIE) and Spanish Society for Computer Architecture (SARTECO). His research focuses on power-aware computing, heterogeneous systems, bioinformatics, and hardware design automation. He has authored over 242 publications and supervised 15 PhD theses. His professional service includes chairing 138 international conferences and serving on numerous committees for EUROMICRO, IEEE, and national research agencies. Education: No explicit details provided, but has been active at Complutense University since 1978. Affiliations: Member of COSCE Executive Board (2012), IEEE Senior Member (2005), and EUROMICRO Board (1991-2004). Research Interests: - Optimizing energy efficiency in high-performance computing systems - Design of heterogeneous architectures and accelerators - Bioinformatics applications through hardware/software co-design - Parallel programming models for emerging architectures - Automation tools for FPGA and embedded systems development Conference Contributions: Played multiple roles (General Chair, Program Chair) for major events like ParCo, HPCA, and EUROMICRO PDP conferences. Delivered 64 keynote/plenary lectures globally. Scientific Awards: Doctor 'Honoris Causa' from universities in Spain, Paraguay, and Peru 2013 National Computer Science Award IEEE Senior Membership Advising: Supervised 15 PhD students. Active in technology transfer contracts with industry. Labs/Teams: Founder and leader of the ArTeCS research group at Complutense University, specializing in computer architecture and high-performance computing innovations.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +3 more

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.

Mher SafaryanView profile

Researcher

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +7 more

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

Klaus MuellerView profile

Professor

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +7 more

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

• Applied Mathematics
• Computational Physics
• Scientific Machine Learning
• +10 more

Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.

Gerald Quentin Maguire Jr.View profile

Professor

• Data Communications
• Medical Image Processing
• Mobile Computing and Communication
• +3 more

Gerald Quentin Maguire Jr. is a Professor (emeritus) at KTH Royal Institute of Technology, Sweden, specializing in Computer Science and Communication Systems. He holds a Ph.D. in Computer Science from the University of Utah and a B.A. in Physics from Indiana University of Pennsylvania. His academic roles include Professor of Computer Communication at KTH since 1994, and prior positions at Columbia University and the U.S. National Science Foundation. He has contributed to fields like data communications, medical imaging, and mobile computing. Education: Ph.D. in Computer Science, University of Utah, 1983 M.S. in Computer Science, University of Utah, 1981 B.A. in Physics (magna cum laude), Indiana University of Pennsylvania, 1975 Research Interests: Focuses on network protocols, mobile computing, medical imaging integration, and high-performance computing. His work includes cognitive radio systems, GPU-centric networking, and CT/PET fusion for medical diagnostics. Awards: IEEE Fellow (2013) Teacher of the Year (2006/2007, KTH) Giovanni DiChiro Award (1997) Multiple honors in medical imaging and network research Advising & Grants: Supervised 8 doctoral students and over 75 master's students. Authored 5 books and numerous papers, with over 200 invited lectures globally. His research has been supported by grants from Swedish and international funding bodies. Labs & Teams: Formerly led the Computer Communication Systems Lab (CCSlab) at KTH and collaborated with Karolinska Institutet on medical imaging projects.

• High-Performance Computing
• Parallel Programming
• GPU Computing
• +3 more

assoz. Prof. Dipl.-Ing. Dr. Peter Thoman is an Associate Professor in the Department of Computer Science at the University of Innsbruck. His work focuses on high-performance computing, parallel programming models, and GPU-accelerated systems. He leads research in areas such as SYCL standard implementations, distributed computing optimizations, and energy-efficient algorithms for exascale applications. His research interests include task-based parallelism, heterogeneous computing architectures, and scalable runtime systems. Notable contributions include the Celerity API for accelerator clusters and the LIGATE drug discovery platform. He is affiliated with the University of Innsbruck’s technical research groups and collaborates on projects like the AllScale framework. Dr. Thoman’s teaching includes courses on parallel programming and high-performance computing systems. He is a member of the Department’s leadership team under Prof. Thomas Fahringer.

Thomas FahringerView profile

Professor

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +4 more

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.

• Machine Learning
• Data Science
• Computer Vision
• +3 more

Siddiqi Shafaq is a Researcher at TU Graz's Institute of Human-Centred Computing, holding a Dr.techn. (technical doctorate), BSc, and M.Sc. Her work bridges computer science and human-centric applications, focusing on machine learning, data science, and education technology. She contributes to interdisciplinary research, including smart surveillance systems, cloud computing security, and IoT applications. Her educational background includes advanced technical degrees, though specific institutions are not detailed. Research interests span machine learning interpretability, data cleaning frameworks, outlier detection in non-IID datasets, and educational paradigms like U-Learning. Her publications highlight innovation in declarative ML systems (SystemDS), ontology-driven opinion mining, and CPU-GPU optimization. Her articles collectively explore trends in scalable data science pipelines, ethical AI practices, and real-world deployment challenges. Notable work includes analyzing internet addiction's academic impacts and designing trusted cloud platforms. No awards or grants are explicitly listed, though her active publication history suggests ongoing research engagement. Shafaq is affiliated with TU Graz's Institute of Human-Centred Computing, contributing to both research and teaching activities (via unspecified courses). No student advisees or lab affiliations are mentioned in the provided data.

• High-Performance Computing
• Heterogeneous Systems
• Parallel Computing
• +5 more

Eduard Mehofer is an Associate Professor at the Faculty of Computer Science , affiliated with the Research Group Scientific Computing . His academic work focuses on high-performance computing and heterogeneous systems. Academic Rank: Associate Professor School: Faculty of Computer Science Department: Research Group Scientific Computing His research spans high-performance computing , parallel computing , and energy efficiency in heterogeneous systems. Key contributions include optimizing work scheduling algorithms and exploring GPU acceleration for magnetic field simulations. Recent publications highlight his work on runtime optimization in heterogeneous systems, multi-kernel data-parallel applications , and hardware configuration in asymmetric clusters. These studies reflect his focus on balancing computational efficiency with energy constraints. He has presented at academic conferences, including a 2018 keynote on Multi-Objective Optimization: Runtime Efficiency vs. Energy Efficiency and talks on parallel computing challenges (2009) and cluster efficiency (2015).