Find Academic Supervisors

• Database Theory
• Computational Logic
• Parameterized Complexity
• +4 more

Reinhard Pichler is a Full Professor at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on Database Theory , Computational Logic , and Parameterized Complexity . He leads multiple research projects like DeConquer (2023–2027) and HyperTrac (2018–2022), addressing challenges in query optimization and hypergraph decompositions. He holds the prestigious START Prize (2014–2022) for young researchers. His work spans theoretical foundations (e.g., hypertree decompositions) and practical applications (e.g., SPARQL query processing systems like SparqLog). He contributes to academic governance, serving on faculty councils and curriculum commissions. His research innovations bridge algorithmic theory and real-world database systems, emphasizing efficient query evaluation and tractability analysis. Key contributions include advancing fractional hypertree decompositions , SPARQL query optimization , and consistent query answering . His projects often involve collaborations with industry and international funders like the Austrian Science Fund (FWF) and Vienna Science and Technology Fund (WWTF). He actively publishes in top venues like Journal of the ACM , ACM Transactions on Database Systems , and Proceedings of the VLDB Endowment . His academic leadership extends to course design, teaching advanced topics like Complexity Theory and Theoretical Computer Science . He mentors doctoral students and oversees research teams exploring cutting-edge areas like uncertain databases and cloud-based computational social choice .

Oskar MencerView profile

Professor

• Numerical methods and simulations
• Imaging
• Discretization of space, time and value
• +7 more

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).

Alexandru NicolauView profile

Professor

• Parallelizing compilers
• Supercomputing
• High Performance Computing (HPC)
• +5 more

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.

• Medical Imaging
• MRI Reconstruction
• Real-Time MRI
• +4 more

Martin Uecker is a Professor at the Institute of Biomedical Imaging at TU Graz. His research focuses on advanced MRI reconstruction techniques, real-time imaging, and open-source software tools like the Berkeley Advanced Reconstruction Toolbox (BART). He specializes in developing methods for fast and accurate medical imaging, including applications in cardiac MRI, fetal brain imaging, and disease monitoring. His work emphasizes reproducibility, quantitative imaging, and clinical translation. Key research areas include generative models for MRI reconstruction, model-based inversion of the Bloch equations, and interactive real-time MRI systems. His team collaborates on projects involving hardware-software integration, such as portable MRI scanners and MRI-guided interventions. Notable contributions include advancements in multi-echo radial FLASH techniques, motion-resolved T1 mapping, and Bayesian uncertainty estimation in imaging. Uecker’s publications highlight innovations in accelerating MRI acquisition and reconstruction, with applications in pulmonary function assessment, neonatal imaging, and cardiovascular diagnostics. His work bridges theoretical physics, computational methods, and clinical practice, fostering open-source frameworks to democratize access to cutting-edge imaging tools.

Marcus HilbrichView profile

Professor

• Distributed Systems
• Software Engineering
• Cloud Computing
• +5 more

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.

Michael KerberView profile

Professor

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +4 more

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

Kurt MatzlerView profile

Professor

• Strategic Management
• Innovation
• Disruption
• +3 more

Kurt Matzler is a Professor at the Institute for Strategic Management, Marketing and Tourism within the Faculty of Business and Management at the University of Innsbruck. With over 28,000 citations in Google Scholar, he is recognized as one of the world's most cited strategy professors. His research bridges academic theory and practical application through partnerships like IMP Consulting, which originated from a University of Innsbruck research project. Education: Doctorate in "Customer Satisfaction and Involvement" (1997) and Habilitation in "The Customer-Based View of the Company" (2001), both from the University of Innsbruck. Matzler's research focuses on open strategy processes , digital disruption , and innovation in family businesses , emphasizing governance structures, CEO characteristics, and strategic implementation. His work leverages collective intelligence to address disruptive challenges and improve business model innovation. Scientific Awards: Strategy Book of the Year 2021 (Strategy+Business Magazine) Nominated for Thinkers50 Strategy Award Axiom Book Award 2022 (Bronze) His teaching integrates research and practice, notably through courses like "Open Strategy" (with Julia Hautz) and "Strategy Implementation," which draws parallels between extreme athletics and management. He contributes to the Master's programme "Strategic Management and Innovation," blending theoretical foundations with case studies and guest speakers.

• Tomography
• Inverse Problems
• Medical Imaging
• +3 more

Peter Elbau is a Senior Lecturer (Privatdozent) in the Faculty of Mathematics at [University Name, if known]. His research focuses on mathematical imaging techniques, particularly in tomography and inverse problems, with applications in medical and biomedical imaging. He is actively involved in projects such as the "Tomography across the scales" initiative, aiming to develop quantitative imaging methods from molecular to astronomical scales. His primary research interests include the mathematical foundations of tomographic imaging, inverse scattering problems, and quantitative parameter reconstruction in optical coherence tomography (OCT) and photoacoustic imaging. He explores topics such as motion detection in diffraction tomography, curvature flows on surfaces, and the development of algorithms for medical diagnostics and material characterization. Elbau leads the research project "Tomography across the scales - Quantitative optical imaging from single molecules to stars," funded from 2018 to 2026. This project highlights his role in bridging fundamental mathematical research with practical applications in imaging technologies. His work often involves cross-disciplinary teams focusing on biomedical and material imaging challenges.

Bernhard KerblView profile

Researcher

• Real-time graphics
• High-performance rendering
• Virtual Reality (VR)
• +3 more

Bernhard Kerbl is a Researcher and Teaching Assistant at the Institute of Computer Graphics and Vision, Technical University of Graz (TU Graz). He holds a Master’s degree in Software Engineering and Economics. His research focuses on parallel and distributed processing, with special emphasis on adaptive rendering and GPU optimization under Prof. Dieter Schmalstieg and Dr. Markus Steinberger. His interests include real-time graphics, high-performance rendering, and Virtual Reality (VR). He contributes to teaching by organizing lectures on computer graphics. His recent work involves publications in areas like adaptive radiance caching and distributed rendering systems. Collaborations include projects with industry and academic partners, though specific grants or lab affiliations are not detailed in the provided texts.

• Optimizing Compilers
• Program Analysis
• Embedded Systems
• +4 more

Jens Knoop is a Full Professor at TU Wien, leading the Department of Information Systems Engineering (E194) and the Research Unit Compilers and Languages (E194-05). His roles include Head of Institute for Information Systems Engineering and membership in the Curriculum Commission for Informatics. He specializes in compiler design, program analysis, and embedded systems, with a focus on worst-case execution time (WCET) analysis and formal methods. His research interests span optimizing compilers, programming languages, and real-time computing. Notable contributions include the Platin tool suite for WCET analysis and contributions to the T-CREST project on time-predictable multi-core architectures. He has authored and edited multiple influential publications, including proceedings of international conferences such as Code Generation and Optimization (CGO) and ARCS. Professor Knoop has supervised numerous theses, including works on effect systems in Haskell, mutation testing in Rust, and robotic systems. He has received the Most Influential PLDI Paper Award (2002) for foundational work in compiler optimization.

• Algorithms
• Graph Neural Networks
• Problem decomposition techniques
• +4 more

Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.

Minos GarofalakisView profile

Professor

• Big Data Analytics
• Private Data Analytics
• Machine Learning
• +3 more

Minos Garofalakis is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete, and serves as Director of the Information Management Systems Institute at the Athena Research & Innovation Center in Athens. He has held senior research roles at Bell Labs, Intel Research, Yahoo! Research, and academic positions at UC Berkeley as an Adjunct Associate Professor. Currently, he is a Senior Research Consultant at Huawei Edinburgh Research Center and co-founder of Agora Labs , focusing on medical data privacy. Research Interests His work centers on Big Data Analytics , encompassing private data analytics , machine learning , federated analytics , and blockchain systems . He has pioneered advancements in data stream management , query optimization , and approximation algorithms , with applications in distributed systems and privacy-preserving technologies. Scientific Awards ACM Fellow (2018) IEEE Fellow (2017) Excellence in Research Award, Technical University of Crete (2015) FP7 Marie-Curie International Reintegration Fellowship (2010-2014) Bell Labs President’s Gold Award (2004) Central Bell Labs Teamwork Award (2003) Patents and Citations Garofalakis holds 29 issued US patents (36 filed) with applications in data management and analytics. His research has garnered over 16,000 citations on Google Scholar and an h-index of 69.

Karoński MichałView profile

Professor

• Random discrete structures
• Probabilistic combinatorics
• Graph theory
• +1 more

Michał Karoński is a Professor of Mathematics at the Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland, and serves as head of the Department of Discrete Mathematics. He has held visiting professorships at Emory University, The Johns Hopkins University, Purdue University, and Southern Methodist University since 1975, with ongoing engagements at Emory University since 1992. Research Focus: Discrete mathematics, random discrete structures, graph theory, and theoretical computer science. Leadership Roles: Editor-in-Chief of Random Structures & Algorithms (since 1990), Chairman of the Council of National Science Centre (2010–2016), and organizer of 17 bi-annual conferences on random structures. His research group in random structures gained international recognition in the 1970s. He has authored 60 publications, delivered over 30 plenary lectures, and collaborated globally at institutions like Microsoft Research, Bellcore, and BRICS. Scientific Awards: 2016 Medal 'Homini Vere Academico' (AMU Rector) 2013 Polonia Restituta Officer Cross (President of Poland) 1990 Minister of Science and Higher Education Prize His work spans probabilistic combinatorics, algorithm theory, and graph irregularity, with influential publications on stochastic Kronecker graphs, random intersection graphs, and distributed complexity in matchings.

• Physical processes at Earth's surface
• Landscape evolution
• Fluvial processes
• +5 more

Sean Douglas Willett is a Full Professor at ETH Zurich, Switzerland (since 2006), specializing in Earth surface processes and tectonic geomorphology. Previously, he held positions as Assistant/Associate Professor at the University of Washington (1998-2006), Assistant Professor at Pennsylvania State University (1994-1998), and completed postdoctoral research at Dalhousie University. He was also a Visiting Professor at the University of Bologna (2004-2005). Education: Ph.D. in Geophysics, University of Utah (1988) B.S. in Geology and Geophysics, University of Utah (1982, cum laude) Research Focus: Integrates numerical modeling with field observations to study landscape evolution, mountain-building processes, fluvial/glacial erosion dynamics, low-temperature thermochronology, and orogenic wedge mechanics. His work bridges tectonics, climate science, and surface processes. Publications: His recent articles (2012-2015) demonstrate advanced computational methods for landscape modeling and focus on Alpine/European tectonic evolution, river basin reorganization, and exhumation mechanisms. Earlier foundational work established mechanical models for mountain belts. Awards/Honors: Fellow, Canadian Institute for Advanced Research (1995-2014) Elected Member, Academia Europaea (2015) Professional Service: Extensive editorial contributions including Associate Editor for American Journal of Science and Terra Nova , organizer of TOPO-Europe conferences, and committee roles for European Science Foundation programs. His scholarly impact includes 5,700+ citations and h-index of 36 (2015).

Kin K. LeungView profile

Professor

• Wireless Communications
• Communication Networks
• Machine Learning
• +4 more

Kin K. Leung is the Tanaka Chair Professor in the Electrical and Electronic Engineering and Computing Departments at Imperial College London. He serves as the Head of the Communications & Signal Processing Group (2009-2024) and Deputy Director of the University Defence Research Centre in Signal Processing. Previously, he held senior technical roles at AT&T Bell Labs and Lucent Technologies. Education: B.S. from Chinese University of Hong Kong (1980), M.S. and Ph.D. from University of California, Los Angeles (1982, 1985). Research Interests: Focuses on wireless technologies, communication networks, stochastic modeling, machine learning, optimization, and emerging quantum computing applications. His work integrates theoretical frameworks with practical implementations in multi-antenna systems, cross-layer network designs, and distributed quantum architectures. Publication Trends: Recent works (2021-2025) demonstrate a strong focus on quantum computing integration with networking systems, federated learning optimizations for edge devices, and reinforcement learning solutions for resource allocation in wireless environments. Awards and Honors: Fellow of Royal Academy of Engineering (2022) IEEE Fellow (2001) IET Fellow (2021) Royal Society Wolfson Research Merit Award (2004-2009) IEEE Communications Society Leonard G. Abraham Prize (2021) IEEE Best Survey Paper Award (2022) Grants and Leadership: Principal Investigator for EPSRC TITAN/HASC Com Hubs, DCIT Centre, and DQC & App (distributed quantum computing). Leads the Communications & Signal Processing Group and co-directs the School of Convergence Science in Space, Security and Telecoms.