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Monika HenzingerView profile

Professor

• Combinatorial Optimization
• Dynamic Optimization
• Parallel and Distributed Optimization
• +5 more

Monika Henzinger is a Full Professor of Computer Science at the Institute of Science and Technology Austria (IST Austria) and Deputy Speaker of the Vienna Graduate School on Computational Optimization. She holds a PhD from Princeton University and has held positions at Cornell University, Digital Equipment Corporation, Google, EPFL, and the University of Vienna. Her research focuses on combinatorial algorithms, dynamic optimization, and efficient graph algorithms. She leads a group at IST Austria exploring algorithm design for dynamic environments, privacy-preserving algorithms, and practical implementations of theoretical results. Research Interests: Combinatorial algorithms (especially graphs), dynamic algorithms, approximation algorithms, algorithmic game theory, and privacy-preserving computation. Her work includes breakthroughs in decremental graph algorithms, submodular optimization, and computational advertising. Collaborations: Works with Vladimir Kolmogorov, Nysret Musliu, Günther Raidl (Combinatorial Optimization), Birgit Rudloff (Dynamic Optimization), and Dan Alistarh (Parallel/Distributed Optimization). Awards: Wittgenstein Award (2021), ERC Advanced Grants (2021, 2014), ACM Fellow (2016), and numerous others listed in her CV. Grants & Labs: Principal investigator on an ERC Advanced Grant for graph algorithms. Her team includes PhD students (e.g., Bardiya Aryanfard, Antoine El-Hayek) and postdocs focused on algorithmic challenges in dynamic systems.

• 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.

• Algorithms
• Sparse Matrices
• Parallel Computing
• +3 more

Robert Ernstbrunner is a researcher affiliated with the Faculty of Computer Science , contributing to algorithm development and parallel computing. His work focuses on low-rank approximations and fault-tolerant strategies in linear algebra methods. Research Interests: His research spans computational efficiency, numerical linear algebra, and fault tolerance in high-performance computing environments. Key areas include sparse matrix operations and resilience mechanisms for iterative algorithms. Publications: He has published two peer-reviewed papers: (1) a 2022 study on precision-cost trade-offs in low-rank matrix approximations, and (2) a 2020 framework for node-failure resilience in iterative linear algebra methods. Academic Activities: Robert has presented at international conferences, including the 2022 IEEE IPDPS and the 2020 FTXS workshop.

Kazuo IwamaView profile

Professor

• Stable Matchings
• Boolean Satisfiability
• Auction algorithms
• +13 more

Kazuo Iwama is a Professor at the School of Informatics, Kyoto University, where he has been employed since 1997. He previously served as a Professor at Kyushu University's Department of Information Science (1992–1997) and held various academic positions, including Associate Professor and Visiting Associate Professor, at institutions such as Kyoto Sangyo University and UC Berkeley's Computer Science Division. Education Ph.D., Electrical Engineering, Kyoto University (1980) M.E., Electrical Engineering, Kyoto University (1975) B.E., Electrical Engineering, Kyoto University (1980) His research spans theoretical computer science, with a focus on algorithms, computational complexity, and quantum computing. Key areas include stable matchings, approximation algorithms, Boolean satisfiability, and quantum network coding. Scientific Awards 2008 Honorary Doctorate from University of Latvia 2005 Member, Science Council of Japan

Michael AffenzellerView profile

Professor

• Genetic Programming
• Symbolic Regression
• Evolutionary Algorithms
• +6 more

Michael Affenzeller is a Professor at the University of Applied Sciences Upper Austria, leading the HEAL AIST Center of Excellence for Smart Production with a focus on Digital Transformation. His research spans Genetic Programming, Symbolic Regression, and Evolutionary Algorithms, with applications in optimization, AI, and manufacturing automation. He has authored over 400 publications and led major projects such as HCAI (Human-Centered AI) and HEAL, emphasizing interdisciplinary collaboration. Research interests include developing advanced algorithms for dynamic optimization, prescriptive analytics, and federated learning in industrial contexts. He has contributed to projects like LOISI (Logistics Optimization in Steel Industries) and SimGenOpt2, addressing challenges in production planning and workforce optimization. Key collaborations involve institutions in Austria and beyond, focusing on smart manufacturing, logistics, and AI integration. His work bridges theoretical advancements with real-world applications, driving innovation in energy systems, crane scheduling, and predictive maintenance. Grants and projects include funding from FWF (doc.funds.connect) and Upper Austrian initiatives, emphasizing doctoral training and industry partnerships. He actively participates in conferences such as EUROCAST and PPSN, contributing to algorithmic advancements and computational theory.

Matthias JanetschekView profile

Associate Professor

• Computer Science
• Cybersecurity
• Distributed Systems
• +5 more

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.

• Distributed Systems
• Service Platforms
• Energy-Aware Computing
• +1 more

Margret Steinbuch is a Researcher in Distributed Systems at the Vienna University of Technology. Her work focuses on federated data systems, energy-aware computing, and performance optimization for heterogeneous architectures. Her research explores trustworthy data lakes, service platform engineering, and scalable computing solutions for modern distributed environments. Steinbuch has contributed to multiple European Commission projects including INDENICA (engineering virtual service platforms) and COIN (enterprise interoperability). Her articles consistently address optimization challenges in distributed and parallel computing systems. She coordinates laboratory resources and infrastructure for distributed systems research while collaborating on international technology initiatives.

• High Performance Computing
• MPI Programming
• Parallel Algorithms
• +2 more

Ioannis Vardas is a Researcher in Parallel Computing at TU Wien's Faculty of Informatics. His work focuses on optimizing high-performance computing systems through advanced process mapping, MPI programming, and performance analysis tools. Key research areas: Topology-aware process mapping for HPC MPI library development and optimization Performance profiling of parallel applications Resource allocation in hierarchical architectures Energy-efficient parallel computing He contributes to projects improving MPI performance tools and develops methods for efficient resource utilization in supercomputing environments.

• Computer Architecture
• Compilers
• Programming Languages
• +4 more

Andreas Krall is an Associate Professor in the Department of Software Technology at TU Wien's Faculty of Informatics. He holds roles as Curriculum Coordinator for the Bachelor Informatics and Bachelor Software and Information Engineering programs, and serves as a Substitute Member of the Curriculum Commission for Informatics. His research focuses on compiler design, architecture description languages (e.g., VADL), and formal methods for ensuring correctness in compiler-processor co-design. Key research areas include computer architecture, compiler verification, embedded systems, and the development of tools for processor simulation and optimization. Krall has led projects such as 'Correct Compilers for Correct Processors' (2010–2015) and contributed to the CACAO JVM project. He has published extensively on topics like instruction selection, abstract state machines (CASM), and SSA-based optimizations. Notable awards include the Heinz Zemanek Preis (1987). His work spans over 30 years, with contributions to both academic research and industry-relevant tools like the VADL architecture description framework. Krall has advised numerous students in topics ranging from compiler backends to garbage collection algorithms.

• Automated Software Verification
• Formal Methods
• Model Checking
• +7 more

Georg Weissenbacher is a Full Professor in the Institute of Logic and Computation at TU Wien, leading the Formal Methods in Systems Engineering group. His research focuses on automated software verification, formal methods, and concurrency bug analysis. He holds a DPhil from Oxford and has held positions at Princeton and ETH Zurich. Education: TU Wien (since 2010), Postdoctoral Researcher at Princeton University (2010–2012), PhD from Oxford University (2008–2010), Research Assistant at ETH Zürich (2005–2010), Master's at TU Graz (until 2003). Research Interests: Automated software verification, formal methods, model checking, concurrency bug detection, logic and automated reasoning, symbolic simulation, and test case generation. His work addresses challenges like Heisenbugs in concurrent systems and robustness verification of neural networks. Grants & Projects: Lead projects such as AutoTest (Vienna Science Fund), LogiCS-Scholarships (2017–2025), and contributions to the National Research Network 'Rigorous Systems Engineering' (FWF). Active in the FWF-funded doctoral college 'Automated Reasoning' (2025). Advising: Supervised over 20 PhD and master's students, including notable works on Heisenbugs, mutation testing, and neural network verification. Current advisees include Thomas Hader and Sarah Sallinger. Labs & Teams: FORSYTE Group at TU Wien, collaborating on formal methods and automated reasoning. Engaged in international conferences like CAV and FMCAD as program chair/co-chair.

Petter E. BjørstadView profile

Professor

• Numerical analysis
• Domain decomposition
• Elliptic PDE
• +4 more

Petter E. Bjørstad is a Professor of Computer Science and Mathematics at the University of Bergen, Norway, where he has served as Head of the Department of Informatics since 2010. Previously, he was Director of the Bergen Center for Computational Science (2000-2010) and Professor in both the Department of Mathematics (2006-2010) and Department of Informatics (1985-2005). He also held a position as Professor of Mathematics at the University of Minnesota during 1996-1997 on unpaid leave from Bergen. Dr. Bjørstad earned his PhD in Computer Science from Stanford University in 1980, following which he completed a postdoctoral fellowship at the Courant Institute of Mathematical Sciences at New York University. Before entering academia, he worked as a Principal Engineer at Det Norske Veritas from 1981-1985. Dr. Bjørstad's research focuses on numerical analysis and high-performance computing, with particular expertise in domain decomposition methods, parallel algorithms for elliptic partial differential equations, and scientific computing. His work bridges theoretical mathematics with practical computational challenges, especially in industrial applications. He has led significant research projects including 'Multiscale Domain Decomposition: Algorithms and Analysis' (2010-2014), funded by the Research Council of Norway and the University of Bergen. His publication record demonstrates consistent contributions to the field of numerical methods and parallel computing over several decades. The research trends show a strong focus on developing efficient algorithms for solving partial differential equations through domain decomposition techniques, with applications spanning structural analysis, reservoir simulation, semiconductor device modeling, and other industrial contexts. His work increasingly emphasizes practical implementation on modern parallel architectures including SIMD and MIMD systems. Large number of research grants from Norway and the European Union Dr. Bjørstad has been actively involved in mentoring students and collaborating with industry through projects like the Europort effort, where industrial codes were ported to parallel computing platforms. His laboratory for parallel computing (Parallab) has been instrumental in advancing practical applications of high-performance computing since its establishment in 1985 with Europe's first 64-processor Intel hypercube. The lab has evolved to include multiple MIMD machines including an Intel Paragon, Parsytec GC/Power-Plus, and DEC α-cluster. As Head of the Department of Informatics, Dr. Bjørstad leads one of Norway's premier computing research units with expertise spanning theoretical computer science, numerical methods, and practical applications in various scientific domains. His leadership has helped establish Bergen as a significant center for computational science in Europe.

Regino CriadoView profile

Professor

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +4 more

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

Esparza JavierView profile

Professor

• Algorithms and tools for the design and verification of reactive and distributed systems
• Verification of systems with infinitely many states
• Software Model Checking
• +7 more

Javier Esparza is a Professor at the Technische Universität München, holding the Chair of Foundations of Software Reliability and Theoretical Computer Science since 2007. Prior to this, he held chairs at the University of Stuttgart (2003-2007) and the University of Edinburgh (2001-2003), along with academic positions dating back to 1990. Education: M.S. in Theoretical Physics (1987, University of Zaragoza), Ph.D. in Computer Science (1990, University of Zaragoza), and Habilitation in Computer Science (1994, University of Hildesheim). His research focuses on formal verification, distributed systems, and software reliability, particularly through algorithms for software model checking, logic and automata theory, and analysis of probabilistic systems. His work has been supported by numerous grants from the German Research Council (DFG), British Engineering and Physical Sciences Research Council (EPSRC), and the Humboldt Foundation. Scientific Awards: Doctor honoris causa from Masaryk University (2009), TeachInf Awards for best Bachelor and Master courses (2009, 2010), TU München Diploma for excellent teaching (2011), Member of Academia Europaea (2011), and Dissertation Prize from the University of Zaragoza (1990). Esparza has supervised over 15 PhD students, including Dr. Stefan Schwoon, Dr. Claus Schröter, and Dr. Michael Luttenberger, and collaborated on research projects with institutions across Europe and the UK.