Selina Reinhard is a PreDoc Researcher and University Assistant (BSc MSc) at the Department of Software Engineering , part of the Faculty of Informatics at TU Wien. Her work focuses on educational technology, particularly in optimizing online learning environments such as MOOCs and adaptive assessment methods. She teaches the 2025S Seminar in Learning Technologies (Course Code: 194.179) , integrating software engineering principles into educational platforms. Her research emphasizes personalized learning pathways and the technical infrastructure supporting modern online education systems.
Martin Riener is a Senior Lecturer at the Department of Theory and Logic, Faculty of Informatics, TU Wien. His research focuses on automated theorem proving, higher-order logic, and formal methods. He contributes to the GAPT framework for proof theory and collaborates on the Vampire theorem prover. He has worked on projects like CERESω cut-elimination and TLAPM for TLA+. Education: PhD (2017) and MSc (2011) in Computer Science from TU Wien. Research projects include an Austrian Science Fund (FWF) project (2010–2012) on proof-theoretic applications of CERES. Teaching includes courses on programming fundamentals, digital systems, and formal modeling. He is involved in outreach activities explaining computer science concepts through unplugged activities for diverse age groups. Software contributions include GAPT, Vampire, and TLAPS. Contactable via three email addresses and ORCID: 0000-0001-8836-7808 .
Aakanksha Saha is a PreDoc Researcher at the Department of Security and Privacy, Faculty of Informatics, Technical University of Vienna. Her research focuses on cybersecurity, with emphasis on malicious document analysis, advanced persistent threat (APT) attribution, and source code security. She contributes to projects like IoTIO (2020–2025), exploring security challenges in interconnected systems. Education: Bachelor of Science Master of Science Research Interests: Saha investigates methods to systematically detect and analyze malicious documents, automate APT campaign attribution using heterogeneous data sources, and reduce false positives in secret detection within source code. Her work integrates machine learning and software engineering principles to address real-world cybersecurity vulnerabilities. Publications: Focus on cutting-edge topics like APT analysis, secret exposure in repositories, and automated threat detection systems. Recent work emphasizes practical solutions for enterprise security challenges. Awards/Grants: No awards explicitly listed, but contributes to ongoing IoTIO project (2020–2025). Teams/Labs: Engaged with TU Wien’s Security and Privacy research group, collaborating on projects involving malware analysis and defensive strategies.
Johannes Schoisswohl is a PreDoc Researcher at TU Wien's Department of Formal Methods in Systems Engineering. His research focuses on automated reasoning, theorem proving, and formal methods in computer science. He is involved in projects such as ForSmart (2023–2027) and SFB SPyCoDe (2023–2026), exploring topics like quantifier elimination, unification algorithms, and decision procedures for arithmetic systems. His work bridges theoretical foundations with practical applications in automated deduction and formal verification. Education: Diplom-Ingenieur (Dipl.-Ing.) and Bachelor of Science (BSc). His academic contributions include seminal papers on conflict-driven quantifier elimination (VIRAS framework), superposition-based reasoning with delayed unification, and inductive benchmarks for automated systems. His research emphasizes improving automated reasoning tools for real-world verification challenges. Notable projects include contributions to the ALASCA system for quantified linear arithmetic and the development of reflection-based techniques for automating induction. His publications span conferences like CADE, LPAR, and CICM, showcasing advancements in formal methods and logic-based AI.
Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Ernst Schuster is an Associate Professor in the Department of Databases and Artificial Intelligence at Technische Universität Wien. His research focuses on healthcare information systems, medical informatics, and telemedicine infrastructure. He has supervised numerous theses on topics ranging from cancer documentation systems (e.g., HNOOncoNet) to mobile health applications and medical data security. His work emphasizes interdisciplinary collaboration between informatics and healthcare domains. Key research interests include: Database systems for medical applications Telemedicine and remote patient monitoring Software engineering for healthcare IT Decision support systems in clinical settings Recent publications highlight innovations in: Medical data version control systems Usability engineering for healthcare interfaces Telecommunication security frameworks AI-driven diagnostic tools Supervised over 30 graduate students since 2002, contributing to advancements in medical informatics and health technology. Active in developing web-based medical documentation systems and e-learning platforms for medical education.
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.
Florentina Voboril is a Research Fellow in the Algorithms and Complexity group at Technische Universität Wien. Her research explores how Large Language Models can solve Constraint Programming instances efficiently, with additional focus on SAT-solving and algorithm design. She investigates applications of AI in combinatorial optimization problems and develops innovative approaches to algorithmic challenges. Voboril has developed methods for SAT-based local improvement in string problems and generates streamlining constraints using LLMs. Her work bridges theoretical computer science with practical applications in computational biology and AI-assisted programming.
Eva Maria Wagner is a PreDoc Researcher in the Department of Formal Methods in Systems Engineering at Vienna University of Technology (E192-04). Her contact email is eva.maria.wagner@tuwien.ac.at, and she works in Room HA0305 at Favoritenstrasse 9, Vienna. Education: Diploma Thesis (2024): Program synthesis of provable recursive functions, Technische Universität Wien Research Focus: Eva specializes in formal methods, program synthesis, and recursive programming. Her work bridges theoretical computer science with practical systems engineering, particularly in automated reasoning and software verification. Recent Publications: Her research explores the synthesis of recursive programs using saturation techniques and the development of provable recursive functions. These contributions align with advancements in automated deduction and formal verification.
Dr. Felix Winter is a PostDoc Researcher in the Databases and Artificial Intelligence group at Vienna University of Technology. He teaches courses including Bachelor Thesis supervision, Project in Computer Science, and Database Systems. His research focuses on optimization algorithms for industrial scheduling problems, including oven scheduling, artificial teeth production scheduling, employee task distribution, and paint shop scheduling. Methodologies include hybrid approaches combining exact methods with metaheuristics. Recent publications demonstrate consistent application of operations research techniques to manufacturing contexts, with emphasis on developing practical solutions for complex scheduling challenges in automotive supply chains and dental production environments. Dr. Winter supervises student research projects including diploma theses on optimization topics. Current projects include the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017-2025).
Frank Sommer is a Humboldt fellow (Feodor Lynen scholarship for Postdocs) at Vienna University of Technology since June 2024. He is affiliated with the Institute of Logic and Computation, where he conducts research in theoretical computer science and algorithms. Previously, he held postdoc positions at Friedrich Schiller University Jena (May 2023-May 2024) and Philipps University Marburg (November 2022-May 2023). Dr. Sommer earned his PhD in Mathematics from Philipps-University Marburg (2017-2022), following a Master's degree (2015-2017) and Bachelor's degree (2012-2015), both in Mathematics from Friedrich Schiller University Jena. His research focuses on parameterized algorithms , algorithm engineering , and graph algorithms , with applications to hard problems in Data Science and Machine Learning. He has made significant contributions to the theoretical foundations of graph problems, particularly in network analysis, community detection, and subgraph optimization. His methodology combines theoretical analysis with practical implementation, emphasizing both computational complexity and real-world applicability. Dr. Sommer's publication record shows a strong emphasis on parameterized complexity, kernelization techniques, and practical algorithm implementations. His work spans from theoretical foundations to practical applications in network analysis, machine learning, and data science, with particular expertise in graph-theoretic problems and their computational aspects. Scientific Awards: Humboldt fellow (Feodor Lynen scholarship for Postdocs) Dr. Sommer collaborates extensively with researchers including Christian Komusiewicz, Niels Grüttemeier, and Tomohiro Koana. His work is supported by the Humboldt Foundation and has been published in top-tier computer science venues including Algorithmica, Journal of Graph Algorithms and Applications, and proceedings of major conferences like ESA and ISAAC. He is part of the Algorithms and Complexity Group within the Institute of Logic and Computation at TU Vienna, contributing to research on fundamental algorithmic problems with applications across various domains. His current research explores the intersection of theoretical computer science with practical challenges in data science and machine learning.
Martin Antenreiter is a Professor and Chair of Information Technology. His research spans materials science, educational technology, and machine learning. Key projects include fracture analysis in materials and development of online proctoring systems. He has contributed to 18 publications and 10 professional activities. Education: Dipl.-Ing. (Master of Engineering) and Dr.mont. (Doctor of Engineering Sciences). Research interests focus on interdisciplinary applications of IT in education and materials engineering. Recent collaborations involve international conferences like i-KNOW and Hybrid Modeling Summer School. Advising: Supervised 3 academic works. No scientific awards explicitly mentioned. Active in conferences and workshops, emphasizing practical IT solutions for education and materials research.
Peter Auer is a Professor and Chair of Information Technology at Montanuniversität Leoben. His research focuses on machine learning, reinforcement learning, and algorithm design with applications in network optimization, materials science, and industrial processes. He has authored 132 publications and supervised 23 works. Recent activities include invited talks on 'Mathematische Grundlagen der AI' (May 2025) and visiting the Fraunhofer-Institut für Nachrichtentechnik (August 2022). His educational background includes Dipl.-Ing. (Diplom-Ingenieur) and Dr.techn. (Doctor of Technical Sciences) degrees. Research interests span multi-armed bandits, reinforcement learning theory, and practical implementations in domains like cognitive radio networks and crystal growth simulations. He actively participates in academic collaborations, including the 'MINT@Leoben' outreach program (2021-2022) and international conferences. His work bridges theoretical computer science with real-world engineering challenges.
Prof. Jessica Cauchard is a Full Professor in the Department of Computational Perception and Reality at TU Wien. Her research focuses on Visual Computing, Human-Centered Technology, and Human-Robot Interaction, with emphasis on drones, affective computing, and wearable systems. She leads the Artifact-based Computing and User Research group and teaches courses in interface design, human-centered computing, and project supervision. Her work bridges technical innovation with user-centric design, addressing challenges in emergency response (e.g., firefighting drones), cross-cultural technology adoption, and emotional interaction with autonomous systems. Key research areas include human-drone collaboration, trust in automation, and affective robotics. Recent publications explore drone applications in firefighting, emotion regulation in robotics, and culturally adaptive HCI. She actively participates in international conferences like MobileHCI and AutomationXP, contributing to both technical and ethical aspects of emerging technologies. Prof. Cauchard's research integrates multidisciplinary methods from computer science, cognitive science, and design studies. Current projects investigate drone safety protocols, expressive robot behavior, and the socio-technical impact of aerial vehicles in public spaces.
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.
Stefan Neumann is an Assistant Professor at TU Wien, funded by a WWTF VRG grant, and an associate faculty member at the Complexity Science Hub. His research focuses on algorithms for data science and social network analysis, including opinion dynamics, graph algorithms, and scalable algorithms with provable guarantees. He coordinates the Machine Learning curriculum for the Master's program at TU Wien. Education: Ph.D. from the University of Vienna (advised by Monika Henzinger), postdoc at Brown University with Eli Upfal, and WASP assistant professor at KTH Royal Institute of Technology. He won the Heinz Zemanek Award and an Award of Excellence from the Austrian federal government. Research interests include: Foundations of data science: Practical algorithms with theoretical guarantees Social network analysis: Impact of timeline algorithms on polarization Graph algorithms and dynamic data structures Projects include Towards Trustworthy Recommendation Systems for Online Social Networks (2023–2031, funded by Vienna Science and Technology Fund). Teaching: Courses on machine learning, algorithms, and research writing. Advises PhD students like Sebastian Lüderssen and manages a team of student assistants. Labs/Teams: Active in TU Wien's Machine Learning Research Unit and collaborates with the Complexity Science Hub on interdisciplinary projects.
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