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 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.
Erich Schikuta is an associate professor at the University of Vienna's Faculty of Computer Science and heads the Research Group Workflow Systems and Technology. He has held multiple administrative roles, including Vice Dean and Curricular Commission Head. His research focuses on Cloud Computing, Parallel/Distributed Systems, Neural Networks, and Database Systems, with over 200 peer-reviewed publications. Education: Studied Computer Science, Business Informatics, and Mathematics at the University of Technology Vienna and University of Vienna. Holds titles including Dipl.-Ing., Dr., and Berufstitel Universitätsprofessor (2009). Research interests emphasize Utility Computing, Cloud Resource Optimization, and Neural Network Simulation. Notable contributions include ranking 25th globally in Cloud Computing productivity and developing frameworks like N2Sky for neural network-as-a-service. Key awards include the 2011 Best Paper Award and 2010 Best Teacher Award. Active in international collaborations, he serves as a European Commission evaluator (FP5-FP7/H2020) and chairs program committees. Labs/Teams: Leads the Research Group Workflow Systems and Technology. Involved in projects like BIG-Business (Grid Infrastructure) and 'Änderung in kollab.Prozessen' (2011-2016).
Helge Hagenauer is an Associate Professor at the Department of Computer Science, University of Salzburg, specializing in simulation methods, software engineering, and concurrent programming. His research focuses on Parallel and distributed simulation Object-oriented design patterns Ada/Java programming techniques He is affiliated with the Faculty of Digital and Analytical Sciences and works from Room 1.07 at Jakob-Haringer-Str. 2, 5020 Salzburg. Contact: hagenau@cs.sbg.ac.at | Tel: +43 662 8044-6314
Stefan Wagner serves as a Professor at the University of Applied Sciences Upper Austria (FH OOE), based at the Research Center Hagenberg Center of Excellence for Smart Production. His academic affiliation resides within the Department of Production and Operations Management under the institution's ICT focal area. Wagner holds the title FH-Prof. DI Dr. and actively serves as Principal Investigator (PI) on multiple research projects including ENPro (Energy Neutral Production) and JRZ adaptOp (Josef Ressel Center for Adaptive Optimization), driving innovation in optimization and smart manufacturing systems. Wagner's research expertise centers on Evolutionary Computation and Optimization , with specialized focus on Genetic Algorithms, Heuristic Optimization, and Dynamic Optimization. As lead developer of HeuristicLab —an open-source optimization environment—he bridges theoretical advances with industrial applications in Symbolic Regression, Production Scheduling, and Energy-Efficient Manufacturing. His work addresses complex challenges in logistics, crane scheduling, and energy-neutral production through evolutionary techniques. Recent publications (2025) reveal a pronounced trend toward distributed and dynamic optimization frameworks. Key themes include diversity management in changing environments, containerization for scalable evolutionary computation, and hybrid cooperative approaches for symbolic regression. Applications span container terminal logistics, energy-neutral manufacturing, and robust unloading bay optimization, demonstrating strong industry-oriented problem-solving. Wagner received the FH OÖ Researcher Award in 2019 for exceptional contributions. He has secured funding for 9 research projects including ENPro (2025-2028) and SMART UNLOAD, frequently serving as PI or Co-PI. His projects tackle production optimization, workforce planning, and energy efficiency in Upper Austrian manufacturing. With 4 supervised students and extensive community engagement through EvoSoft at GECCO and EUROCAST peer review, he actively shapes the evolutionary computation landscape. Leading the HeuristicLab project, Wagner collaborates with industry partners through the Center Hagenberg research team to develop simulation and optimization tools for adaptive scheduling. His current work in the ENPro project targets energy-neutral production via modeling and optimization, positioning him at the forefront of sustainable manufacturing innovation in industrial settings.
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).
Wolfgang Schreiner is an A.Univ.-Prof. (Associate University Professor) at the Research Institute for Symbolic Computation (RISC), Johannes Kepler University Linz, Austria. His research focuses on formal methods, parallel and distributed computing, and symbolic computation. He leads projects in automated reasoning, programming language semantics, and educational software tools. His work emphasizes practical applications of formal methods, including the development of tools like RISCAL for model checking and the RISC ProgramExplorer for program reasoning. He collaborates on interdisciplinary projects, such as analyzing queueing systems using probabilistic model checking and designing grid computing frameworks for medical applications. Key research interests include semantics-based language design, theorem proving, and educational technology. He has authored textbooks like *Concrete Abstractions* and *Thinking Programs*, emphasizing foundational concepts in computer science. His contributions span theoretical advancements and practical implementations, with notable work in distributed systems, formal verification, and computational logic.
Elisabeth Oswald is a University Professor at the Alpen-Adria-Universität Klagenfurt, affiliated with the Institut für Artificial Intelligence und Cybersecurity. Her research focuses on cryptographic security, side-channel attacks, and leakage detection in hardware and software implementations. She is a key contributor to advancements in cryptographic resilience, countermeasures against side-channel vulnerabilities, and the development of efficient security evaluation frameworks. Her work spans theoretical and applied cryptography, with a strong emphasis on practical security challenges in embedded systems and cryptographic protocols. Notable areas include the analysis of masking techniques, multi-task learning applications in AES implementations, and the certification of leakage-resistant systems. She also explores statistical methods for evaluating cryptographic systems' robustness, such as mutual information estimation and distinguisher-based approaches. Her articles from 2021–2025 highlight contributions to post-quantum cryptography (e.g., FrodoKEM optimization), leakage model completeness, and the practical implications of side-channel countermeasures. While no specific awards are listed, her extensive publication record underscores her influence in the field. She is actively involved in academic governance, including membership in the Curricularkommission Informatik.
Johann Blieberger is an Associate Professor and Head of the Institute of Computer Engineering at Technische Universität Wien. He also leads the Automation Systems Research Unit (E191-03) and serves as a Principal Member of the Faculty Council. His work focuses on real-time systems, concurrent programming, railway operation optimization using Kronecker Algebra, static analysis, and Ada programming. Key projects include developing simulation tools for sustainable public transport (2021–2023), coordinating autonomous vehicles in pedestrian environments (2017–2019), and optimizing railway systems via Kronecker Algebra (e.g., the Zagreb-Rijeka line). He has secured grants from the Austrian Research Promotion Agency (FFG) and Austrian Science Fund (FWF). Blieberger’s research interests span weak memory models, symbolic evaluation, and algorithm analysis. He actively contributes to the ISO JTC1/SC22/WG9 committee for Ada language development. His advising spans multiple PhD and master’s theses, including work on concurrent program verification and Kronecker Algebra applications.
Dipl.-Ing. Dr.techn. Carlo Corinaldesi is affiliated with TU Wien's Department of Energy Economics and Energy Efficiency within the Research Unit Energy Economics and Energy Efficiency (E370-03). His research focuses on sustainable energy systems, electromobility, and smart grid technologies. He contributes to projects addressing decentralized energy systems optimization, sector coupling, and prosumer dynamics in energy transition contexts. Key research interests include energy flexibility markets, electric vehicle integration, cost-benefit analysis of charging infrastructure, and grid stability enhancement through EVs. His work explores innovative business models for aggregated flexibility in European electricity markets and the socio-technical aspects of local energy communities. Recent publications emphasize real-time trading algorithms, portfolio optimization of end-user flexibilities, and the economic viability of residential car-sharing models. He collaborates on projects analyzing transmission congestion mitigation and market potentials for new flexibility products in continental Europe. Corinaldesi's contributions span technical reports on algorithm scalability, forecasting methodologies, and dynamic interfaces between aggregators and energy management systems. His research bridges theoretical frameworks with practical implementations in energy policy and market design.
Markus Kuba is a Professor (FH-Professor) at FH-Technikum Wien in Vienna, Austria, where he leads the Department of Applied Mathematics and Physics. He holds a habilitation (Privatdozent) from TU Wien's Faculty of Mathematics and Geoinformation, granting him the venia docendi. He is also a teacher at HTL-Spengergasse, a higher technical institute, specializing in mathematics and computer science. His academic journey includes a PhD in Mathematics from TU Wien (2006) and a habilitation in 2013. His research focuses on theoretical computer science, analysis of algorithms, analytic combinatorics, and random structures. Notable specializations include tree structures, urn models, multiple zeta values, and SAT-problems. His work bridges combinatorial theory with practical applications in network analysis and stochastic processes. Key contributions include studies on urn models, tree growth models, and interdisciplinary projects like integrating traffic simulation tools. His publications span prestigious journals such as Theoretical Computer Science , Combinatorics, Probability and Computing , and Journal of Combinatorial Theory . Teaching spans secondary schools and universities of applied sciences since 2009, with a focus on mathematics and programming. Collaborations include co-authors from institutions worldwide, reflecting his active role in international academic networks.
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).
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.
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.
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.
