Zsolt Horvath is a researcher at TU Wien's Engineering Hydrology Research Section (Forschungsbereich Ingenieurhydrologie). His work focuses on advanced hydrological modeling, flood risk management, and computational fluid dynamics. He specializes in developing high-resolution simulation frameworks for urban/rural flash floods and river flooding, leveraging GPU acceleration and numerical methods like the Saint-Venant system. Expertise: Flood modeling, computational hydrology, geospatial analysis, climate impact studies Key Projects: HORA 3.0 flood risk zoning, interactive flood visualization tools, Kepler shuffle GPU algorithms
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).
Dr. Heiko Andreas Schmidt is a computational biologist affiliated with the University of Vienna , where he works at the Department of Structural and Computational Biology as part of the Center for Integrative Bioinformatics Vienna (CIBIV) and Max Perutz Labs. His research focuses on phylogenetics, phylogenomics, and the development of computational tools for evolutionary tree reconstruction, including contributions to software like IQ-TREE and TREE-PUZZLE . His work spans parallel computing applications in phylogenetics, scientific workflows, and quality assessments of alignments. He has co-authored numerous high-impact studies in molecular biology, developmental biology, and bioinformatics, often collaborating with Prof. Arndt von Haeseler and other researchers. Dr. Schmidt has contributed to methodological advancements in phylogenetic inference, particularly for large genomic datasets. He has taught courses such as Bioinformatics for Phylogeny and Evolutionary Tree Reconstruction and Algorithmic Bioinformatics at the University of Vienna.
Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.
Minyi Guo is a Chair Professor and Head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China. Previously, he served as Professor and Department Chair at the School of Computer Science and Engineering, University of Aizu, Japan. Dr. Guo received his BSc and ME degrees from Nanjing University, China in 1982 and 1986, and his PhD from University of Tsukuba, Japan in 1998. Dr. Guo's educational background includes: BSc in Computer Science, Nanjing University, China (1982) ME in Computer Science, Nanjing University, China (1986) PhD in Computer Science, University of Tsukuba, Japan (1998) Dr. Guo's research spans multiple areas in computer science, with a primary focus on parallel/distributed computing , compiler optimizations , cloud computing , database systems , and big data . He has published over 400 papers including approximately 150 in major journals and 250 in international conferences, with more than 60 papers in IEEE/ACM transactions and over 100 papers in prestigious conferences. Dr. Guo has also authored 7 books (4 in English, 3 in Chinese) and received 5 best/highlight paper awards from international conferences. Dr. Guo's publication record demonstrates strong contributions across multiple domains of computer systems research. His recent work shows particular emphasis on big data processing, edge computing, graph neural networks, and data center optimization. The publications reveal a consistent trajectory of impactful research in parallel and distributed systems, with increasing focus on AI/ML applications and blockchain technologies in more recent years. Dr. Guo has received numerous prestigious awards and honors: State Technological Invention Award of China (second class award, 2019) Shanghai Technological Invention Award (first class award, 2018) IEEE Technical Committee on Scalable Computing Award for Excellence in Scalable Computing (2018) Ministry of Education Natural Science Award (first class award, 2017) IEEE Fellow (2017) Chief Scientist of National Basic Research Project (973 Program, 2014) Recruitment Program of Global Experts (2010) Excellent Academic Leaders of Shanghai (2010) National Science Fund for Distinguished Young Scholars (2007) As an academic leader, Dr. Guo has served as Department Head for ten years, managing a department with over 100 faculty members and 1000+ students. Under his leadership, the department was promoted to the top tier in China and ranked among the top 40 in the world. He has secured significant research funding, including serving as Chief Scientist of the prestigious 973 Program in 2014 and receiving the National Science Fund for Distinguished Young Scholars in 2007. Dr. Guo has also been selected for the Recruitment Program of Global Experts in China (2010). Dr. Guo actively contributes to the academic community as an associate editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and Journal of Parallel and Distributed Computing. He has served as General/Program Chair for IEEE conferences and delivered keynote speeches at well-established conferences. His research group has developed practical technologies with industry impact, including 28 licensed patents, some of which have been transferred to companies like Alibaba.
Ulrich Bauer is an Associate Professor in the Department of Mathematics at Technical University of Munich (TUM), leading the Applied & Computational Topology group. His research focuses on topology and geometry, particularly persistent homology, discrete Morse theory, and geometric complexes, supported by DFG and MDSI grants. He developed Ripser, a leading software for computing Vietoris–Rips persistence barcodes, and contributed to PHAT, a persistent homology library. Bauer holds editorial roles at Foundations of Computational Mathematics , Journal of Applied and Computational Topology , and SIAM Journal on Applied Algebra and Geometry . He is a core member of the Munich Data Science Institute (MDSI) and a principal investigator at the Munich Center for Machine Learning (MCML). His academic journey includes a PhD from the University of Göttingen and postdoctoral work at IST Austria under Herbert Edelsbrunner. Research Interests: Bauer's work bridges computational topology and geometry, with applications in topological data analysis. His key areas include persistent homology algorithms, discrete Morse theory, and geometric complexes. Recent focus includes Reeb graph analysis, stability theorems, and topological machine learning. His software tools (e.g., Ripser) are widely used in academia and industry. Publications Trends: Bauer's recent work emphasizes theoretical foundations (e.g., Reeb graph stability, Morse theory) and practical software development. His articles often address computational efficiency, algorithmic innovation, and interdisciplinary applications in data science and machine learning. Scientific Awards: None explicitly listed. Grants & Funding: DFG Collaborative Research Center (Discretization in Geometry & Dynamics), Munich Data Science Institute (MDSI). Labs & Teams: Leads the Applied & Computational Topology group at TUM, collaborates with MDSI and MCML. His advisory roles include the DFG CRC board and EPSRC's Centre for Topological Data Analysis.
Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.
Alexander Rieder is a Research Fellow and University Assistant in Computational Mathematics at TU Wien's Institute of Analysis and Scientific Computing. His work focuses on numerical methods for partial differential equations, particularly boundary element methods (BEM), finite element methods (FEM), and their applications in wave propagation and fractional calculus. He holds a PhD from TU Wien (2017) and has held postdoctoral positions at TU Wien and the University of Vienna. Notable achievements include the TU Best Paper Award 2020 for his contributions to numerical analysis. Education: Bachelor of Science (BSc), Mathematics, TU Wien (1989) Diploma in Mathematics, TU Wien (2011) Doctorate (Dr.techn.), TU Wien (2017) Research Interests: Alexander specializes in fractional differential operators , hp-adaptive FEM/BEM , and time-domain boundary integral equations . His projects include developing open-source libraries for advanced BEM simulations and studying wave propagation in composite media. Recent work emphasizes convolution quadrature for semigroups and nonlinear wave equations. Teaching: Current courses include Numerics of Partial Differential Equations and Numerical Computation . He oversees seminars on differential equations and computational mathematics, emphasizing practical implementation and theoretical rigor. Projects: Leading the Advanced BEM for Wave Propagation initiative, he designs algorithms to reduce computational effort while improving accuracy in wave simulations. The project also develops an open-source software library for broader academic use.
Ass.-Prof. Dr. Sashko Ristov is an Assistant Professor at the Department of Computer Science, University of Innsbruck. His research focuses on serverless computing, distributed systems, and workflow orchestration, with applications in cloud computing, healthcare informatics, and high-performance computing. He leads research on federated serverless infrastructures, resilient function choreographies, and digital twins in construction engineering. His work addresses challenges in cross-cloud resource management, workflow scheduling, and anomaly detection in microservices. Recent contributions include frameworks like StoreLess for federated storage orchestration and CODE for cross-platform serverless deployment. Research trends in his publications emphasize bi-objective optimization for batch workflows, GPU-accelerated document processing, and disaster-resilient cloud systems. He actively participates in workflows community summits to advance scientific workflow standards and interoperability. Dr. Ristov has collaborated on projects like the Montage workflow analysis and ECG monitoring systems using serverless architectures. His work bridges theoretical computing models with practical implementations in healthcare and civil engineering domains.
Eduarda Sangiogo Gil is a researcher at the University of Vienna's Faculty of Chemistry, Department of Theoretical Chemistry. Her work focuses on nonadiabatic dynamics, quantum algorithms, and computational methods for simulating photochemical processes. She has developed software tools like EXASH for exciton-based surface hopping dynamics and contributed to packages such as Newton-X and MOPAC-PI. Her research integrates quantum-classical hybrid algorithms, semiempirical methods, and advanced trajectory-based simulations. Teaching: Courses on Chemical Dynamics Simulation, Computational Methods, and Journal Club at University of Vienna (2024). Collaborations: Active in international projects involving quantum computing for dynamics and exciton models in materials. Key research areas include extending exciton models beyond Frenkel theory, incorporating quantum algorithms into surface hopping approaches, and benchmarking molecular dynamics methods. Recent work explores azo-escitalopram photoisomerization and methanimine dynamics on quantum computers. Her software contributions enable efficient simulations of multichromophoric systems and nonadiabatic processes, with applications in drug design and material science.
Joao Nuno Estevao Fidalgo Ferreira Alves is a dedicated researcher within the Faculty of Computer Science, actively contributing to the Research Group Scientific Computing with expertise in high-performance computing and matrix algorithm optimization. His work focuses on developing innovative solutions for European exascale supercomputing challenges. His educational background includes: Bachelor of Science (BSc) Master of Science (MSc) Mr. Alves specializes in cache-oblivious algorithms, space-filling curves, and matrix transposition techniques, addressing critical data locality challenges in large-scale parallel systems. His research bridges theoretical computer science and practical supercomputing applications, with publications in premier venues including ACM Transactions on Mathematical Software and IEEE IPDPS. Analysis of his 2022-2023 publications reveals a concentrated research trajectory in matrix operations, where he pioneered triangular space-filling curves and Hilbert curve-based blocking schemes to achieve cache-efficient in-place transposition. These contributions significantly advance scientific computing methodology for modern parallel architectures. He currently serves as co-Principal Investigator for the project 'Innovative Algorithms for Applications on European Exascale Supercomputers' (2024-2025), securing active research funding for next-generation algorithm development in the European supercomputing ecosystem. Within the Research Group Scientific Computing, Mr. Alves maintains strong international collaborations, notably with Prof. Benkner at the University of Vienna, driving cooperative research on exascale algorithm design and implementation.
René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a key member of the Computational Logic Group. His work bridges theoretical computer science and practical formal verification, with a strong emphasis on automated reasoning and program correctness. Research Interests: Program Verification using interactive theorem proving (Isabelle/HOL) Termination and complexity analysis of programs Term rewriting systems and dependency pairs SAT/SMT solving and decision procedures Formalization of algebraic algorithms (LLL, Smith normal form, algebraic numbers) Development of the Certification Problem Format (CPF) and the CeTA tool His recent publications (2017–2025) reflect a consistent focus on formalizing advanced algorithms in Isabelle/HOL, especially those related to termination, complexity, and algebraic computation. These works are published in top venues like CPP, FSCD, LICS, and ITP, demonstrating rigorous, machine-checked proofs. His research often centers on verifying tools like AProVE and developing foundational libraries for number theory and rewriting. Scientific Projects: ARI : Automation of Rewriting Infrastructure (Task Leader, since 2022) Certifying Termination and Complexity Proofs : Project Leader (2014–2021) Constrained Rewriting and SMT : Task Leader (2012–2015) Improving Certifiers for Termination Proofs : Project Leader (2010–2014) Teaching Activities: Lecture and Proseminar: Program Verification (SS 2023–2025) Lecture: Constraint Solving (SS 2024–2025) Lecture and Proseminar: Functional Programming (WS 2021–2025) Lecture: Advanced Functional Programming (WS 2024/25) Lecture: Interactive Theorem Proving in Isabelle/HOL (SS 2022–2024) Lecture: Decision Procedures (SS 2021) He has supervised no listed students in the provided data but actively contributes to collaborative research. His email is rene.thiemann@uibk.ac.at.
Sepp Hochreiter is University Professor ( Univ.-Prof. ) at the Institute for Machine Learning , Johannes Kepler University Linz, and heads the LIT Artificial Intelligence Lab . He is Principal Investigator of several major Austrian and EU projects, including the FWF Cluster of Excellence “Bilateral Artificial Intelligence” and industry collaborations on certified deep learning, generative CFD simulation, and quantum-AI integration. Research interests revolve around the foundations and applications of deep learning: Recurrent neural networks: Inventor of LSTM; recent work on the xLSTM family that scales to billion-parameter language models while outperforming Transformers in efficiency. Uncertainty & reliability: New uncertainty estimation techniques for language generation, conformal prediction, and out-of-distribution detection using modern Hopfield networks. Generative models: Discrete diffusion samplers, generative drug-design pipelines (Bio-xLSTM), masked-image-model refiners, and physics-informed neural samplers. Applied AI: Few-shot learning in robotics, traffic prediction, medical risk modeling, antibody design, and climate downscaling. His 2022-2025 publications reveal a clear trend toward scaling recurrent architectures (xLSTM, FlashRNN, pLSTM) with hardware-aware optimisation, bridging theory and practice through energy-based models, and deploying trustworthy AI in safety-critical domains such as healthcare and autonomous systems. Scientific recognition & service: Keynote & invited speaker at NeurIPS, ICLR, ICML, and leading industry venues (60+ invited talks since 2022). Principal Investigator on 61 funded projects totalling >€50 M, spanning FWF, FFG, EU Horizon, and direct industry contracts. Editorial board and senior programme committee roles for top-tier ML conferences and journals. Supervision & team leadership: Leads a vibrant lab of 30+ PhD students and post-docs across machine learning theory, generative modelling, and AI for science. Active in joint doctoral training programmes with IST Austria and international partner labs.
Tom van Dijk serves as an Assistant Professor in the Formal Methods and Tools group at the University of Twente, where he conducts cutting-edge research in formal verification and develops practical software tools for the verification community. His work bridges theoretical computer science with real-world applications in model checking and synthesis. His educational background includes: PhD in Computer Science (2016), University of Twente. Thesis: Sylvan: Multi-core Decision Diagrams MSc in Computer Science (2012, cum laude), University of Twente. Thesis: The parallelization of binary decision diagram operations for model checking BSc in Computer Science (2010), University of Twente. Thesis: Analysing And Improving Hash Table Performance Van Dijk's research centers on formal verification with specialization in parity games and binary decision diagrams . He pioneers multi-core parallel algorithms for symbolic model checking, focusing on practical implementations that scale to industrial problems. His work integrates SAT/SMT solving , reactive synthesis , and algorithm optimization to advance the state-of-the-art in verification tooling. His publication trajectory reveals consistent innovation in parity game solving, evolving from foundational work on tangle-based algorithms to recent contributions in reproducibility and AI-aided verification. The 2024 papers demonstrate expanding scope into educational technology while maintaining core focus on game-solving efficiency and synthesis techniques. His key recognitions include: Dutch national M&I Informatie Scriptieprijs 2012 (2nd place) for MSc thesis Best paper award at SPIN 2017 for distributed BDD research Van Dijk actively mentors students and seeks collaborations: Student Supervision : Welcomes BSc/MSc students for projects on parity games and BDDs Tool Development : Maintains open-source research tools (Sylvan, Oink, Knor) Community Service : Serves on 20+ program committees including CAV and TACAS As core member of the Formal Methods and Tools group, he contributes to major verification frameworks including LTSmin, Storm, and IscasMC. His Lace work-stealing framework underpins parallelization in multiple verification tools, while ongoing projects focus on polynomial-time parity game solutions and AI integration in verification workflows.
Bogdan Burlacu serves as R&D-Headquarters at the Center of Excellence for Smart Production HEAL at University of Applied Sciences Hagenberg. With an ORCID identifier 0000-0001-8785-2959 and h-index of 10 (619 citations), he maintains active research leadership through 2025. His research focuses on Symbolic Regression and Genetic Programming, with significant contributions to Multiobjective Optimization and Benchmark Problems. Key application areas include Explainable AI systems, hardware acceleration for evolutionary algorithms, and astrophysical modeling. His work demonstrates strong interdisciplinary connections between computer science and physical sciences. Recent publication trends show increasing focus on interpretability frameworks and domain-expert validation in symbolic regression, with notable applications in cosmology and engineering systems. His 2025 publications emphasize practical benchmarking methodologies and hardware acceleration techniques. Burlacu actively supervises research through two documented supervised works and contributes to major collaborative projects. He leads research activities within the Center of Excellence for Smart Production HEAL and participates in the Josef Ressel Center for Symbolic Regression. His work integrates distributed intelligence systems with rapid prototyping methodologies for industrial applications.
